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Explore our latest articles, tutorials, and insights on data engineering, career growth, and interview preparation.

dbt Docs & Lineage: Self-Serve Documentation for the Modern Stack
De InterviewSql

dbt Docs & Lineage: Self-Serve Documentation for the Modern Stack

Gowtham Potureddi

dbt docs and dbt lineage for senior data engineers and analytics engineers — what `dbt docs generate` actually compiles (manifest.json + catalog.json + sources.json), model-level vs column-level lineage, dbt exposures as the BI/ML/reverse-ETL boundary, `dbt source freshness` as a paging signal, and the OSS self-host vs dbt Explorer hosting decision in 2026. Each section ships a worked interview answer with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Prac

Jun 23, 202684 min read
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dbt Cloud vs dbt Core: Pick the Right Edition for Your Team
De InterviewSql

dbt Cloud vs dbt Core: Pick the Right Edition for Your Team

Gowtham Potureddi

dbt Cloud vs dbt Core for senior data engineers — the OSS CLI toolchain vs the managed scheduler + IDE product, the manifest/run_results artifact contract, the Cloud IDE / scheduler / slim CI / semantic layer / Explorer / hosted docs surfaces, the true total cost of self-hosting Airflow + GitHub Actions + docs hosting + on-call rota, the team-size pivot at ~10 engineers, and the migration + hybrid + lifeboat playbook used by real analytics-engineering leads. Each section ships a worked interview

Jun 23, 202696 min read
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SQL MERGE / UPSERT Patterns: Postgres, Snowflake, BigQuery, Databricks Compared
De InterviewSql

SQL MERGE / UPSERT Patterns: Postgres, Snowflake, BigQuery, Databricks Compared

Gowtham Potureddi

SQL MERGE / UPSERT for senior data engineers — the ANSI three-branch mental model (WHEN MATCHED, WHEN NOT MATCHED BY TARGET, WHEN NOT MATCHED BY SOURCE), the four-dialect tour (Postgres 15+, Snowflake, BigQuery, Databricks Delta), the idempotent SCD-2 idiom with dedupe-source-first, and the concurrency + performance runbook senior engineers actually use in 2026. Each section ships a worked interview answer with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works.

Jun 23, 202685 min read
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Kubernetes for Data Engineering Workloads: Spark on K8s, Airflow Helm, KEDA Scalers
De InterviewSql

Kubernetes for Data Engineering Workloads: Spark on K8s, Airflow Helm, KEDA Scalers

Gowtham Potureddi

Kubernetes for data engineering workloads in 2026 — the K8s mental model mapped onto Spark, Airflow, and streaming consumers, Spark on Kubernetes (spark-submit vs SparkOperator, dynamic allocation, Karpenter node pools, shuffle-on-K8s), the Airflow Helm chart with KubernetesExecutor and KubernetesPodOperator, KEDA scale-to-zero on Kafka lag / SQS depth / Prometheus queries, and the platform-team handoff for senior data engineers. Each section ships a worked interview answer with code

Jun 22, 202687 min read
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GitHub Actions for Data Engineering: CI/CD for dbt, SQL & Airflow Pipelines
De InterviewSql

GitHub Actions for Data Engineering: CI/CD for dbt, SQL & Airflow Pipelines

Gowtham Potureddi

GitHub Actions for data engineering — the workflow / job / step / runner hierarchy, dbt slim CI with state defer and manifest diffing, Airflow DAG CI plus OIDC role-assumption to AWS / GCP / Azure without static secrets, SQLFluff lint and schema-diff gates on every PR, GitHub environments with required reviewers, and the dbt-cloud-vs-Actions trade-offs senior data engineers are expected to reason about in 2026.

Jun 22, 202691 min read
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Terraform for Data Infrastructure: Warehouse, Lakehouse, Catalogs & IAM as Code
De InterviewSql

Terraform for Data Infrastructure: Warehouse, Lakehouse, Catalogs & IAM as Code

Gowtham Potureddi

Terraform for data engineering in 2026 — the senior-DE playbook for provisioning warehouses, lakehouses, catalogs, and IAM as code. Walks through the HCL → plan → apply → state mental model, the Snowflake / Databricks / BigQuery / Glue provider matrix, Unity Catalog and Lake Formation grant trees, secrets discipline, module composition, remote state with S3+DynamoDB locking, the OpenTofu fork, drift detection, and the plan/apply CI ritual every platform team should run in production.

Jun 22, 202693 min read
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StarRocks & Apache Doris: New-Generation MPP Engines for Sub-Second Analytics
De InterviewSql

StarRocks & Apache Doris: New-Generation MPP Engines for Sub-Second Analytics

Gowtham Potureddi

StarRocks and Apache Doris are the new-generation MPP engines powering sub-second BI dashboards and lakehouse query acceleration in 2026. This guide walks the new MPP landscape, the 2020 fork that produced StarRocks from Doris, the FE / BE query architecture, vectorized execution and SIMD batch processing, the Primary Key / Aggregate Key / Duplicate Key data models, materialized indexes and async materialized views, colocate joins, and the migration patterns from ClickHouse, Snowflake, BigQuery,

Jun 22, 202674 min read
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Apache Druid vs Pinot vs ClickHouse: Real-Time OLAP Compared
De InterviewSql

Apache Druid vs Pinot vs ClickHouse: Real-Time OLAP Compared

Gowtham Potureddi

Apache Druid vs Pinot vs ClickHouse — the real-time OLAP comparison data engineers actually need. Three open-source engines, three architectural philosophies: Druid for time-series with rollup pre-aggregation and segment storage, Pinot for low-latency user-facing dashboards via star-tree indexes and Helix coordination, ClickHouse for general analytics SQL with MergeTree and AggregatingMergeTree. Each section ships a worked interview answer with code, a step-by-step trace, an output table

Jun 22, 202667 min read
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ClickHouse for Real-Time Analytics: MergeTree, Materialized Views & Sharding
De InterviewSql

ClickHouse for Real-Time Analytics: MergeTree, Materialized Views & Sharding

Gowtham Potureddi

ClickHouse for real-time analytics — the columnar storage and vectorised execution model that makes sub-second dashboards possible, where ClickHouse fits in the modern stack alongside Kafka and a batch warehouse, the MergeTree family (MergeTree, ReplacingMergeTree, SummingMergeTree, AggregatingMergeTree, CollapsingMergeTree, ReplicatedMergeTree) and when each variant is the right answer, insert-time materialized views with -State and -Merge aggregate functions for real-time roll-ups, and shards

Jun 17, 202679 min read
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Trino vs Presto vs Athena: Federated SQL Engines for the Modern Lakehouse
De InterviewSql

Trino vs Presto vs Athena: Federated SQL Engines for the Modern Lakehouse

Gowtham Potureddi

Trino vs Presto vs Athena — a federated SQL engine cheat sheet for the modern lakehouse. The Presto to Trino to Athena lineage, the coordinator + workers + connectors architecture shared by all three, the connector ecosystem (Hive, Iceberg, Delta, Hudi, Postgres, Kafka, Elasticsearch), predicate pushdown vs cross-source joins, and the cost-versus-utilisation decision (Athena per-query vs Trino fixed cluster vs Starburst managed). Each section ships a worked interview answer with code,

Jun 17, 202669 min read
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Feature Stores Compared: Feast vs Tecton vs Hopsworks for Production ML
De InterviewSql

Feature Stores Compared: Feast vs Tecton vs Hopsworks for Production ML

Gowtham Potureddi

Feature stores compared for production ML — what a feature store actually is, how the offline store and online store split the same logical feature into two latencies, how point-in-time joins prevent label leakage, and a side-by-side of Feast vs Tecton vs Hopsworks for production ML features and feature serving. Each section ships a worked interview answer with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Practice data engineering on PipeCode.

Jun 17, 202681 min read
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Reverse ETL with Hightouch, Census & RudderStack: Operational Analytics in Practice
De InterviewSql

Reverse ETL with Hightouch, Census & RudderStack: Operational Analytics in Practice

Gowtham Potureddi

Reverse ETL with Hightouch, Census & RudderStack — the operational analytics discipline that ships warehouse rows back into Salesforce, HubSpot, Marketo, Intercom, Slack, and ad platforms. This guide covers the model / audience / sync data model, the vendor comparison across Hightouch, Census, and RudderStack, the diff-engine + queue + worker sync architecture, API rate-limit and retry semantics, and the governance / observability layer that turns a sync into a production data product. Each sect

Jun 17, 202675 min read
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RAG Data Pipelines: Chunking, Embeddings, Vector Stores & Freshness
De InterviewSql

RAG Data Pipelines: Chunking, Embeddings, Vector Stores & Freshness

Gowtham Potureddi

RAG data pipelines for data engineers — the four-stage ingest → chunk → embed → index → retrieve pipeline, chunking strategies (fixed, recursive, semantic, hierarchical), embedding model selection and vector store layout, hybrid dense + BM25 search, cross-encoder reranking, freshness SLOs, CDC-driven incremental reindex, tombstone deletes, blue/green embedding model upgrades, and per-tenant ACL pushdown. Each section ships a worked interview answer with code, a step-by-step trace

Jun 16, 202682 min read
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Vector Databases for Data Engineers: Pinecone vs Weaviate vs Qdrant vs pgvector
De InterviewSql

Vector Databases for Data Engineers: Pinecone vs Weaviate vs Qdrant vs pgvector

Gowtham Potureddi

Vector databases for data engineers — Pinecone vs Weaviate vs Qdrant vs pgvector compared on hosting, index types, scale, and ops. Covers HNSW, IVFFlat, scalar / product quantization, and DiskANN; hybrid retrieval with metadata filter pushdown and rerankers; memory sizing per million vectors at dim=768 / 1536; multi-tenant namespaces, drift on embedding-model upgrades, and blue / green collection swaps. Each section ships a worked interview answer with code, a step-by-step trace.

Jun 16, 202681 min read
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Semantic Layer Showdown: Cube vs dbt Semantic Layer vs Looker LookML
De InterviewSql

Semantic Layer Showdown: Cube vs dbt Semantic Layer vs Looker LookML

Gowtham Potureddi

Semantic layer showdown for analytics engineers — Cube.dev, dbt Semantic Layer (MetricFlow), and Looker LookML compared on data model, BI tool fan-out, caching, governance, and migration risk. Walks through the headless-BI premise, the three platforms' data models (cubes vs semantic_models vs LookML views/explores), defining the same Weekly Active Users metric in each engine, and a consumer-fan-out playbook for Tableau / Power BI / Hex / Mode / embedded apps / LLM agents. Each section ships

Jun 16, 202682 min read
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MetricFlow & dbt Metrics: Single Source of Truth for KPIs
De InterviewSql

MetricFlow & dbt Metrics: Single Source of Truth for KPIs

Gowtham Potureddi

MetricFlow and dbt metrics tutorial — the dbt metrics layer as the KPI single source of truth, MetricFlow architecture (semantic models, measures, dimensions, metrics, saved queries, the MetricFlow server), the anatomy of a metric definition with entity / measure / dimension / filter and ratio / cumulative / derived variants, the query flow from metric to BI / Python via the Semantic Layer API, and a migration playbook from BI calculated fields to dbt semantic models.

Jun 16, 202675 min read
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dbt Model Contracts, Constraints & Versioning: Production Patterns
De InterviewSql

dbt Model Contracts, Constraints & Versioning: Production Patterns

Gowtham Potureddi

dbt model contracts, constraints, and versioning for production analytics engineering teams — the dbt-Core 1.5+ feature timeline (contracts then constraints then versions), the anatomy of a contract.enforced block, the four constraint kinds (not_null, unique, primary_key, foreign_key, check) and which warehouses actually enforce them, SemVer-for-data versioning with deprecation_date and cross-version refs, and the rollout / deprecation playbook that coordinates dbt, BI

Jun 15, 202684 min read
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OpenLineage & OpenMetadata: Open Standards for Lineage and Cataloging
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OpenLineage & OpenMetadata: Open Standards for Lineage and Cataloging

Gowtham Potureddi

OpenLineage & OpenMetadata are the two open standards reshaping data lineage and cataloging — OpenLineage as the wire format (run, job, dataset, facets) emitted by Airflow, dbt, Spark, and Flink; OpenMetadata as the catalog application with REST APIs, ingestion connectors, and a search/lineage UI. This guide walks the standards stack, the OpenLineage event model with column-level facets, the OpenMetadata architecture, and the interop patterns that let you escape Atlan, Collibra, or Alation

Jun 15, 202680 min read
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Data Observability Platforms Compared: Monte Carlo, Anomalo, Bigeye & Lightup
De InterviewSql

Data Observability Platforms Compared: Monte Carlo, Anomalo, Bigeye & Lightup

Gowtham Potureddi

Data observability platforms compared — a side-by-side breakdown of Monte Carlo, Anomalo, Bigeye, and Lightup across the five pillars of data observability (freshness, volume, schema, distribution, lineage), the rule-based vs ML-based vs metadata-only detection landscape, the six-stage incident lifecycle from detect to learn, pricing model shapes, dbt and OpenLineage integration, and the 30-day pilot scorecard. Each section ships a worked example with code, a step-by-step trace, an output table

Jun 15, 202680 min read
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Data Quality Frameworks: Great Expectations vs dbt Tests vs Soda Core
De InterviewSql

Data Quality Frameworks: Great Expectations vs dbt Tests vs Soda Core

Gowtham Potureddi

Data quality framework comparison for data engineers — Great Expectations vs dbt tests vs Soda Core in 2026. The three vocabularies (expectations, generic tests, SodaCL checks), the dialect matrix of common assertions (not_null, unique, accepted_values, freshness, row_count, foreign_key), where each framework plugs into Airflow / Dagster / dbt build pipelines, the gold/silver/bronze coverage tiering, and the combine-frameworks playbook. Every section ships a worked interview answer with code, a

Jun 15, 202665 min read
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Bytewax, Pathway & Quix: Python-Native Streaming Frameworks Compared
De InterviewSql

Bytewax, Pathway & Quix: Python-Native Streaming Frameworks Compared

Gowtham Potureddi

Bytewax, Pathway and Quix Streams compared — the three Python-native streaming frameworks that let data engineers ship real-time pipelines without standing up a Flink or Spark cluster. Walk the Bytewax dataflow DSL on a Rust core (stateful operators, fold_window, K8s recovery store), the Pathway reactive engine (incremental computation, LLM/RAG-friendly hot index reload, batch-stream parity), and the Quix Streams Kafka-native StreamingDataFrame (consumer groups, RocksDB state, exactly-once

Jun 14, 202665 min read
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Polars vs Pandas vs DuckDB Benchmarked: Speed, Memory & API Trade-offs
De InterviewSql

Polars vs Pandas vs DuckDB Benchmarked: Speed, Memory & API Trade-offs

Gowtham Potureddi

Polars vs Pandas vs DuckDB benchmarked for single-node data engineering — the three philosophies (eager NumPy, lazy Rust+Arrow, embedded columnar SQL), the lazy plan vs eager conveyor evaluation model, the Pandas index alignment philosophy and NaN tax, the DuckDB embedded SQL surface with zero-copy Arrow interop, and the 2026 benchmark verdict on speed, memory peak, API ergonomics, and ecosystem across group-by, join, parquet scan, and window workloads.

Jun 14, 202669 min read
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DuckDB for Data Engineering: In-Process OLAP, Local ETL & Parquet-First Workflows
De InterviewSql

DuckDB for Data Engineering: In-Process OLAP, Local ETL & Parquet-First Workflows

Gowtham Potureddi

DuckDB for data engineering — the in-process OLAP engine, vectorized executor, Arrow zero-copy, MVCC snapshots, Parquet-first scans with partition pruning and httpfs / S3, local ETL with Python and dbt-duckdb, pytest-driven CI, and the four-quadrant deployment matrix that pins DuckDB to laptop, CI runner, edge, and notebook workloads while naming the anti-patterns (long-running OLAP clusters, concurrent writes, multi-user serving). Every section ships a worked interview answer with code,

Jun 14, 202679 min read
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Iceberg REST Catalog, Nessie & Polaris: Open Lakehouse Catalogs Compared
De InterviewSql

Iceberg REST Catalog, Nessie & Polaris: Open Lakehouse Catalogs Compared

Gowtham Potureddi

Iceberg REST catalog, Project Nessie, and Apache Polaris compared for senior data engineers picking an open lakehouse catalog — the REST OpenAPI surface (createNamespace, loadTable, commitTable, vendCredentials), Nessie's git-like branches / tags / atomic multi-table commits, and Polaris's principals / roles / grants for multi-tenant Iceberg with RBAC.

Jun 14, 202670 min read
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Apache Hudi Merge-on-Read vs Copy-on-Write: Picking the Right Table Type
De InterviewSql

Apache Hudi Merge-on-Read vs Copy-on-Write: Picking the Right Table Type

Gowtham Potureddi

Apache Hudi Merge-on-Read vs Copy-on-Write — the senior data engineer's playbook for picking the right Hudi table type. Walk the timeline (commit, deltacommit, compaction, clean, rollback, savepoint), the file-group anatomy (base parquet + delta log avro), the CoW rewrite path that keeps reads fast, the MoR base-plus-logs path that keeps writes fast, the compaction and cleaner contracts (inline vs async scheduler, KEEP_LATEST_COMMITS vs KEEP_LATEST_FILE_VERSIONS)

Jun 13, 202665 min read
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Delta Lake Change Data Feed (CDF) & Z-Ordering: Performance Tuning
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Delta Lake Change Data Feed (CDF) & Z-Ordering: Performance Tuning

Gowtham Potureddi

Delta Lake performance tuning guide for senior data engineers — Change Data Feed (CDF) row-state mechanics with _change_type / _commit_version / _commit_timestamp, Z-Ordering as a space-filling curve for multi-dimensional file skipping, OPTIMIZE bin-packing into 1 GB target files, auto compaction and optimized writes, VACUUM retention with the 168-hour floor, deletion vectors for row-level deletes without file rewrites, and liquid clustering as the 2024+ successor to Z-Order. Each section ships

Jun 13, 202673 min read
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Apache Iceberg Branching, Tagging & WAP: Production Patterns
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Apache Iceberg Branching, Tagging & WAP: Production Patterns

Gowtham Potureddi

Apache Iceberg branching, tagging and write-audit-publish (WAP) production patterns for senior data engineers — snapshot atomicity and the apache iceberg vs delta lake decision, the createBranch / fastForward / cherryPick lifecycle, immutable tags for month-end audit and golden datasets, the branch-based WAP pattern that replaces spark.wap.id, and the production maintenance cadence for snapshot expiration, OPTIMIZE / rewrite_data_files, rewrite_manifests, remove_orphan_files, and schema

Jun 13, 202680 min read
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Lambda vs Kappa Architecture: When Each Wins in 2026
De InterviewSql

Lambda vs Kappa Architecture: When Each Wins in 2026

Gowtham Potureddi

Lambda vs Kappa architecture in 2026 — the original dual-path Lambda design (batch + speed + serving layer + merge query) versus Jay Kreps's stream-as-source-of-truth Kappa model, the duplicate-code tax that drove most teams off Lambda, reprocessing patterns (replay window, backfill, state restoration), and how the modern Lakehouse + Streaming SQL stack (Iceberg, Delta, Materialize, RisingWave, Flink) collapses both architectures into a single query surface for new builds. Each section ships a w

Jun 13, 202682 min read
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Apache Pulsar vs Kafka for Data Engineering: Geo-Replication, Tiered Storage & Functions
De InterviewSql

Apache Pulsar vs Kafka for Data Engineering: Geo-Replication, Tiered Storage & Functions

Gowtham Potureddi

Apache Pulsar vs Kafka for data engineering — a 2026 head-to-head on architecture (Kafka brokers with partition logs vs Pulsar stateless brokers backed by BookKeeper segments), native geo-replication vs MirrorMaker 2 with offset translation, tiered storage (BookKeeper S3 offloader vs Kafka KIP-405), Pulsar Functions vs Kafka Streams plus Kafka Connect, and the topic-per-tenant multi-tenancy model. Each section ships a worked engineering answer with code, a step-by-step trace, an output table

Jun 12, 202672 min read
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Spark Structured Streaming: Triggers, State, Watermarks & Exactly-Once Sinks
De InterviewSql

Spark Structured Streaming: Triggers, State, Watermarks & Exactly-Once Sinks

Gowtham Potureddi

Spark Structured Streaming deep dive for data engineers — the trigger-mode rubric (Once vs AvailableNow vs ProcessingTime vs Continuous), watermarks and state stores (HDFS vs RocksDB) for windowed aggregations, the output modes matrix (append / update / complete) crossed with Delta / Kafka / file / foreachBatch sinks, exactly-once via checkpoint + idempotent sink, and the production foreachBatch + MERGE INTO pattern for CDC into Delta. Each section ships a worked interview answer with code

Jun 12, 202677 min read
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Kafka Connect Deep Dive: Source, Sink, SMTs, Schema Registry & Idempotent Writes
De InterviewSql

Kafka Connect Deep Dive: Source, Sink, SMTs, Schema Registry & Idempotent Writes

Gowtham Potureddi

Kafka Connect deep dive for data engineers — the declarative ingestion framework that replaces hand-rolled producers and consumers, the worker / task / connector / REST API runtime model, source connectors (Debezium log-based CDC, JDBC incrementing PK, file), sink connectors (S3 Parquet, JDBC upsert, Elasticsearch), the SMT chain for in-flight reshape (ExtractField, Cast, RegexRouter, MaskField, ReplaceField), Schema Registry with Avro / Protobuf / JSON Schema and the BACKWARD / FORWARD

Jun 12, 202674 min read
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Apache Kafka Streams vs Apache Flink: Stateful Streaming Engines Compared
De InterviewSql

Apache Kafka Streams vs Apache Flink: Stateful Streaming Engines Compared

Gowtham Potureddi

Apache Kafka Streams vs Apache Flink for senior data engineers — the library-in-your-JVM model vs the cluster-runtime model, KStream / KTable / GlobalKTable duality, the StreamGraph → JobGraph → ExecutionGraph compilation pipeline, RocksDB + changelog topic vs RocksDB + distributed barrier snapshots, exactly-once-v2 vs TwoPhaseCommitSinkFunction, when Flink vs Spark Structured Streaming wins, and a 5-question decision tree to pick the right engine in 2026.

Jun 12, 202674 min read
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Lakehouse Data Mesh: Domain Ownership, Contracts & Federated Governance
De InterviewSql

Lakehouse Data Mesh: Domain Ownership, Contracts & Federated Governance

Gowtham Potureddi

Lakehouse data mesh for senior data engineers and platform leads — the four principles (domain ownership, data as product, self-serve platform, federated computational governance) translated into concrete repos, contracts, and CI policies. Walks the bounded-context map (raw / derived / product tiers), the six-field data contract YAML with semver, the federated-governance loop (OPA + Unity Catalog + tag inheritance), the migration path from a central warehouse, and the \"when not to do mesh\"

Jun 11, 202677 min read
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AI Agents in the Data Stack: Lineage, Anomaly Detection & Auto-Repair Pipelines
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AI Agents in the Data Stack: Lineage, Anomaly Detection & Auto-Repair Pipelines

Gowtham Potureddi

AI agents in the data stack — a practitioner's reference for 2026 covering the agent loop, MCP tool servers, lineage agents that answer natural-language metadata questions, anomaly-detection agents that go from metric to root cause in one pass, and auto-repair agents that ship every change as a confidence-gated, shadow-tested PR. Each section ships a worked engineering answer with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Practice data engineering on P

Jun 11, 202680 min read
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psql Command Reference for Data Engineers: Connect, \\copy, Bulk-Load, Inspect
De InterviewSql

psql Command Reference for Data Engineers: Connect, \\copy, Bulk-Load, Inspect

Gowtham Potureddi

psql command reference for data engineers — the 30 meta-commands every PostgreSQL engineer should memorise, the COPY vs \\copy decision (server-side bulk-load vs client-side bulk-load), connection strings and the .pgpass / pg_service.conf / IAM auth flow, and the scripting primitives (\\set, \\gset, \\if, ON_ERROR_STOP, --single-transaction) that turn psql into a real CI tool. Each section ships a worked example with code, a step-by-step trace, an output table, and a concept-by-concept why-this-

Jun 11, 202674 min read
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Python timedelta & datetime for Data Engineers: Time Math, Windows & Time Zones
De InterviewSql

Python timedelta & datetime for Data Engineers: Time Math, Windows & Time Zones

Gowtham Potureddi

Python timedelta & datetime for data engineers — the four Python time objects, the arithmetic rules between datetime and timedelta, naive vs aware timestamps, zoneinfo and pytz, UTC normalization, DST traps, pandas Timedelta vs polars Duration, tumbling / hopping / session windows, and the watermark / late-data contract. Each section ships a worked interview answer with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Practice Python time math on PipeCode.

Jun 11, 202672 min read
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Real-Time SQL on Streams: Materialize, RisingWave & Flink SQL Compared
De InterviewSql

Real-Time SQL on Streams: Materialize, RisingWave & Flink SQL Compared

Gowtham Potureddi

Real-time SQL on streams in 2026 — a side-by-side comparison of Materialize, RisingWave, Apache Flink SQL, and ksqlDB, the four engines that turned CREATE MATERIALIZED VIEW into a streaming primitive. Walks through incremental view maintenance, watermarks, event-time vs ingestion-time, exactly-once delivery, change data capture, and three reference architectures (Kafka -> Flink, Kafka -> Materialize, RisingWave end-to-end). Each section ships a worked interview answer with code, a step-by-step t

Jun 11, 202667 min read
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BigQuery Console & SQL Workbench: Hands-On Tour for New Data Engineers
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BigQuery Console & SQL Workbench: Hands-On Tour for New Data Engineers

Gowtham Potureddi

A hands-on tour of the BigQuery console and the unified SQL Workbench for new data engineers — what the BigQuery web UI looks like in 2026, every panel in the query editor mapped to a job it does, the dry-run-to-schedule workflow, the scheduling ladder from scheduled queries to Workflows to Cloud Composer to Dataform to Studio notebooks, and the INFORMATION_SCHEMA-driven cost and slot monitoring loop. Each section ships a worked interview answer with code, a step-by-step trace, an output table,

Jun 10, 202669 min read
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NoSQL vs SQL for Data Engineering: When to Pick Mongo, Cassandra, DynamoDB or Postgres
De InterviewSql

NoSQL vs SQL for Data Engineering: When to Pick Mongo, Cassandra, DynamoDB or Postgres

Gowtham Potureddi

NoSQL vs SQL for data engineering — when to pick MongoDB, Cassandra, DynamoDB or Postgres. An eight-axis decision matrix, the four-family NoSQL map (document, key-value, wide-column, graph), the CAP theorem with PACELC extension, and a workload-first decision tree across OLTP, OLAP, schema flexibility, scale, consistency, and global active-active. Each section ships a worked interview answer with code, step-by-step trace, output table, and concept-by-concept why-this-works. Practice on PipeCode.

Jun 10, 202677 min read
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Snowflake Certification Path (SnowPro Core → Advanced): Full Prep & Sample Questions
De InterviewSql

Snowflake Certification Path (SnowPro Core → Advanced): Full Prep & Sample Questions

Gowtham Potureddi

Snowflake certification path for data engineers — the full SnowPro ladder (Core COF-C02 → Advanced Architect / Data Engineer / Administrator / Data Scientist / Data Analyst → Specialty), 2026 exam blueprints with domain weights, an 8-week study plan with hands-on labs, sample multi-select and scenario-style questions with worked solutions, the recertification cadence, cost and voucher policy, and which certification actually moves the salary needle. Each section ships an interview-style answer

Jun 10, 202670 min read
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SQL Data Analyst Jobs in 2026: Salary, Interviews & Top Hiring Markets
De InterviewSql

SQL Data Analyst Jobs in 2026: Salary, Interviews & Top Hiring Markets

Gowtham Potureddi

SQL data analyst jobs in 2026 — the state of the market, the top hiring metros (NYC, SF Bay, London, Berlin, Bengaluru) plus remote-first companies, salary bands by region and seniority, the anatomy of the 5-stage interview loop (recruiter screen, SQL test, take-home case, virtual onsite, offer), and a 12-week application roadmap covering CV rewrites, portfolio projects, SQL drill plans, and targeted outreach. Each section ships a worked interview answer with code, a step-by-step trace,

Jun 10, 202672 min read
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SQL Murder Mystery, SQL Island & Gamified SQL Practice Walkthrough
De InterviewSql

SQL Murder Mystery, SQL Island & Gamified SQL Practice Walkthrough

Gowtham Potureddi

A full, step-by-step walkthrough of the two breakout free SQL games — SQL Murder Mystery and SQL Island — plus a comparison of the wider gamified SQL landscape (Schemaverse, CodingGame SQL, Lost at SQL) and a skill-progression map showing exactly which SQL concept each game teaches. Each section ships an interview-style worked example with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Use it as the first stop for tutorial-fatigued learners, bootcamp TAs,

Jun 9, 202677 min read
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SQLZoo, SQLBolt, Mode SQL Tutorial & DataCamp SQL Compared: Which Platform Wins?
De InterviewSql

SQLZoo, SQLBolt, Mode SQL Tutorial & DataCamp SQL Compared: Which Platform Wins?

Gowtham Potureddi

SQLZoo vs SQLBolt vs Mode SQL Tutorial vs DataCamp SQL — an honest 2026 comparison across free tier, interactivity, dialect coverage, depth, and audience level. The post grades all four platforms (plus PipeCode) on a 5-axis feature matrix, maps which platform teaches MySQL, PostgreSQL, BigQuery, Snowflake, SQL Server, and SQLite, and lays out the recommended zero-to-hero learning journey (SQLBolt warm-up, SQLZoo JOIN drills, Mode windowing, optional DataCamp certificate, PipeCode interview prep)

Jun 9, 202668 min read
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M.Tech / Master's in Data Engineering: Programs, Curriculum & ROI vs Self-Study
De InterviewSql

M.Tech / Master's in Data Engineering: Programs, Curriculum & ROI vs Self-Study

Gowtham Potureddi

M.Tech and Master's in data engineering decoded for 2026 — the four program archetypes (M.Tech India at IIT / IISc / IIIT, MS at CMU / Columbia / NYU / UC Berkeley MIDS, MISM at CMU Heinz, and OMSCS / online MS at Georgia Tech), the five-core curriculum that every top program actually teaches (distributed systems, database internals, warehousing + lakehouse, ML systems, cloud + infra), a head-to-head ROI breakdown of cost, duration, salary uplift, and break-even years across self-study, M.Tech

Jun 9, 202688 min read
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SQL Comments, Documentation & Readable Queries: Style Guides for DE Teams
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SQL Comments, Documentation & Readable Queries: Style Guides for DE Teams

Gowtham Potureddi

SQL comments and documentation style guide for data engineering teams — the dialect matrix for -- single-line and /* */ block comments across Postgres, MySQL, SQL Server, Snowflake, BigQuery and Oracle, the COMMENT ON statement for first-class schema docstrings, dbt YAML model descriptions and tests, reusable {% docs %} blocks, and the 8-rule readability checklist for production SQL — header docstrings, CTE labels, magic-number comments, business-rule annotations, comma style, indent policy, key

Jun 9, 202677 min read
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Big Data Engineering: Hadoop, Spark, Kafka, Lakehouse — A 2026 Roadmap
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Big Data Engineering: Hadoop, Spark, Kafka, Lakehouse — A 2026 Roadmap

Gowtham Potureddi

Big data engineering in 2026 is no longer about HDFS and MapReduce — it is about distributed systems design, exactly-once streaming, open table formats, and a 5-layer cloud stack. This roadmap walks the full stack from Hadoop's legacy footprint to Spark, Kafka, and the Iceberg / Delta lakehouse, compares Lambda vs Kappa architectures, decodes the 3 V evolution (volume, velocity, variety) from 2010 to 2026, and ends with a month-by-month 6-month learning ladder for early-career data engineers.

Jun 8, 202672 min read
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Data Engineer vs Data Scientist vs Data Analyst: Role Boundaries, Stacks & Salary
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Data Engineer vs Data Scientist vs Data Analyst: Role Boundaries, Stacks & Salary

Gowtham Potureddi

Data engineering vs data science vs data analytics — a 2026 role-boundary, stack, and salary breakdown for the three core data roles. Covers why the three job titles keep getting confused, the three-role Venn (who owns pipelines vs models vs dashboards), the dialect-by-dialect stack matrix across languages, warehouses, orchestration, modelling, BI, and streaming, US / EU / India salary bands for junior / mid / senior, and the two main career rails (analytics → analytics engineer → DE;

Jun 8, 202679 min read
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SQL UNIQUE Constraints & Deduplication Strategies: Hard vs Soft Uniqueness
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SQL UNIQUE Constraints & Deduplication Strategies: Hard vs Soft Uniqueness

Gowtham Potureddi

SQL UNIQUE constraints and deduplication strategies for data engineers — the UNIQUE vs PRIMARY KEY matrix, composite UNIQUE for multi-tenant tables, partial unique indexes for soft-delete and only-one-active rows, expression-based UNIQUE (LOWER(email)), hard vs soft deduplication (constraint at write time vs ROW_NUMBER / DISTINCT ON / QUALIFY at read time), and the three upsert dialects (Postgres ON CONFLICT, MySQL ON DUPLICATE KEY, MERGE for SQL Server / Snowflake / BigQuery). Each section ship

Jun 8, 202671 min read
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SQL ROUND, FLOOR, CEIL & TRUNC: Numeric Rounding for Reporting & Finance
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SQL ROUND, FLOOR, CEIL & TRUNC: Numeric Rounding for Reporting & Finance

Gowtham Potureddi

SQL ROUND, FLOOR, CEIL and TRUNC cheat sheet for data engineers writing finance and reporting queries — the four-function matrix for negative numbers, NUMERIC(p, s) precision and scale, the half-up vs banker's rounding (HALF_EVEN) split, the dialect cheat sheet across Postgres, MySQL, SQL Server, Snowflake, BigQuery and Oracle, and the 'round at the edge' rule that keeps revenue reports off-by-a-penny-proof. Each section ships a worked interview answer with code, a step-by-step trace,

Jun 8, 202674 min read
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TRUNCATE vs DELETE vs DROP in SQL: Behavior, Performance, Replication & Rollback
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TRUNCATE vs DELETE vs DROP in SQL: Behavior, Performance, Replication & Rollback

Gowtham Potureddi

TRUNCATE vs DELETE vs DROP in SQL — the full behavioural matrix, transaction-log impact, locks, replication, trigger semantics, foreign-key rules, identity reset, and the decision tree for picking the right destructive verb across SQL Server, Postgres, MySQL, Oracle, Snowflake and BigQuery. Each section ships a worked interview answer with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Practice SQL on PipeCode

Jun 7, 202671 min read
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T-SQL Stored Procedures for SQL Server: Params, Return Codes, sp_executesql & TRY/CATCH
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T-SQL Stored Procedures for SQL Server: Params, Return Codes, sp_executesql & TRY/CATCH

Gowtham Potureddi

T-SQL stored procedures for SQL Server — the production-grade guide to CREATE PROCEDURE anatomy, input/output/default/table-valued parameters, RETURN codes, sp_executesql vs EXEC for safe dynamic SQL, and TRY/CATCH + XACT_ABORT error handling for SQL Server data engineers and .NET backends. Each section ships a worked interview answer with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Practice SQL on PipeCode.

Jun 7, 202675 min read
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SQL Server Analysis Services (SSAS): Tabular vs Multidimensional for Data Engineers
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SQL Server Analysis Services (SSAS): Tabular vs Multidimensional for Data Engineers

Gowtham Potureddi

SQL Server Analysis Services (SSAS) explained for data engineers — the decision matrix for Tabular vs Multidimensional, the Vertipaq columnar engine that powers in-memory tabular models, DAX vs MDX for the same KPI, and the deployment topology from SQL Server source to XMLA endpoint to Power BI / Fabric consumers. Each section ships a worked interview answer with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Practice semantic-layer modelling on PipeCode.

Jun 7, 202666 min read
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SSMS 21 (SQL Server Management Studio): A Data Engineer's Productivity Guide
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SSMS 21 (SQL Server Management Studio): A Data Engineer's Productivity Guide

Gowtham Potureddi

SSMS 21 (SQL Server Management Studio) productivity guide for data engineers — what's new in SSMS 21 vs SSMS 20, GitHub Copilot for SSMS, the native dark theme, Entra ID auth, and the Visual Studio 2022 shell upgrade; the top 20 keyboard shortcuts grouped by Navigation, Edit, Debug, and Plan; how to read an execution plan like a senior DBA with operator cost arrows, missing-index hints, Live Query Statistics, and Compare Showplan; the SSMS vs Azure Data Studio vs VS Code mssql decision matrix;

Jun 7, 202665 min read
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SQL Server 2025 Interview Questions: What's New in T-SQL, Performance & AI Features
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SQL Server 2025 Interview Questions: What's New in T-SQL, Performance & AI Features

Gowtham Potureddi

SQL Server 2025 interview questions — the JSON-native primitives (JSON_OBJECT, JSON_ARRAY, JSON_ARRAYAGG, OPENJSON), ANSI regex functions (REGEXP_LIKE, REGEXP_REPLACE, REGEXP_SUBSTR), the new vector data type with VECTOR_DISTANCE, Optional Parameter Plan Optimization, Intelligent Query Processing wave 4, secure enclaves with Always Encrypted, Change Event Streaming as the outbox-style replacement for Service Broker, and Copilot in SSMS — each with a worked interview answer, a step-by-step trace,

Jun 6, 202666 min read
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Databricks API & CLI for Data Engineers: Jobs, Clusters, Repos & CI/CD
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Databricks API & CLI for Data Engineers: Jobs, Clusters, Repos & CI/CD

Gowtham Potureddi

Databricks API and Databricks CLI for data engineers — the REST 2.x endpoint map for Jobs, Clusters, Repos, Secrets, Workspace, DBSQL and Unity Catalog; the twenty CLI commands worth memorising; the CI/CD pattern with Databricks Asset Bundles, GitHub Actions and a manual-approval gate; and the auth-pattern matrix for PAT vs OAuth U2M vs OAuth M2M with a service principal. Each section ships a worked interview answer with code, a step-by-step trace, an output table, and a concept-by-concept why-t

Jun 6, 202670 min read
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Databricks Genie (AI/BI): Text-to-SQL Spaces, Trust & Production Use Cases
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Databricks Genie (AI/BI): Text-to-SQL Spaces, Trust & Production Use Cases

Gowtham Potureddi

Databricks Genie (AI/BI) explained for data engineers and BI leads — what a Genie space actually is, how text-to-SQL drafts and validates queries against Unity Catalog, the semantic layer of certified datasets, sample queries and instructions that makes answers trustworthy, the trust spectrum (certified vs verified vs unverified vs hallucination), and the dev → staging → certified prod rollout topology with SME review loops, hallucination guardrails, Git-versioned Asset Bundles,

Jun 6, 202671 min read
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Databricks Unity Catalog: Governance, Lineage, Row/Column Security & Delta Sharing
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Databricks Unity Catalog: Governance, Lineage, Row/Column Security & Delta Sharing

Gowtham Potureddi

Databricks Unity Catalog for data engineers and analytics engineers — why the workspace-scoped Hive metastore is dying, the account → metastore → catalog → schema → table three-level namespace, automatic table-level + column-level data lineage, row filter functions and column mask functions, the GRANT / REVOKE / USAGE chain, Delta Sharing topology (open protocol + Databricks-to-Databricks), foreign catalogs via Lakehouse Federation, and a production rollout checklist that maps Catalog Explorer

Jun 6, 202672 min read
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ChatGPT / LLM Workflows for Data Engineers: SQL Generation, dbt Macros & Lineage
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ChatGPT / LLM Workflows for Data Engineers: SQL Generation, dbt Macros & Lineage

Gowtham Potureddi

ChatGPT and LLM workflows for data engineers — schema-as-context prompt patterns for SQL generation, RAG-grounded dbt macro and model generation, auto-generated docs and column-level lineage, and the human-in-the-loop guardrail ladder that keeps LLM-generated code from breaking production. Each section ships a worked example with prompt, code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Practice on PipeCode.

Jun 5, 202660 min read
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Pandas for Data Engineering: melt, pivot, groupby, merge & DuckDB Migration
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Pandas for Data Engineering: melt, pivot, groupby, merge & DuckDB Migration

Gowtham Potureddi

Pandas for data engineering — melt and pivot_table for reshape, groupby with agg/transform/apply, the seven merge join types including merge_asof for time-window joins, and DuckDB migration patterns for when Pandas runs out of headroom. Each section ships a worked interview answer with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Practice Pandas + SQL on PipeCode.

Jun 5, 202650 min read
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ETL vs ELT: Architecture, Trade-offs & When Each Wins
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ETL vs ELT: Architecture, Trade-offs & When Each Wins

Gowtham Potureddi

ETL vs ELT — the architectural difference, the cost profiles, the trade-offs, and when each wins in 2026. Walks through classic ETL (transform-first on a dedicated server), modern ELT (load-first, transform inside the warehouse), the 6-dimension decision matrix, and the EtLT hybrid that has become the default for production multi-source pipelines. Each section ships a worked example with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Practice data engineeri

Jun 5, 202665 min read
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Enterprise Data Warehouse Design (Inmon-Style): Multi-Source, Conformed, Governed
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Enterprise Data Warehouse Design (Inmon-Style): Multi-Source, Conformed, Governed

Gowtham Potureddi

Enterprise data warehouse design the Inmon way — Corporate Information Factory layers, 3NF EDW modelling with surrogate keys and SCD Type-2 history, conformed dimensions feeding dependent Kimball marts, and ship-grade governance covering lineage, audit, PII tagging, and compliance. Each section ships a worked design answer with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Practice data modelling on PipeCode.

Jun 5, 202665 min read
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SQL for Data Analytics & Data Analysts: Cohorts, Funnels, Retention
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SQL for Data Analytics & Data Analysts: Cohorts, Funnels, Retention

Gowtham Potureddi

SQL for data analytics and data analysts — cohort analysis with DATE_TRUNC, funnel waterfalls with FILTER, day-N retention curves, MAU / DAU stickiness and LTV in pure SQL. Every section ships a worked example with code, step-by-step trace, output table, and a concept-by-concept why-this-works breakdown calibrated for analytics-engineer interviews. Practice analyst-grade SQL on PipeCode.

Jun 4, 202663 min read
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SQL IF / IIF / NULLIF / NULL-Handling Cheat Sheet for Data Engineers
De InterviewSql

SQL IF / IIF / NULLIF / NULL-Handling Cheat Sheet for Data Engineers

Gowtham Potureddi

SQL IF / IIF / NULLIF / NULL-handling cheat sheet for data engineers — three-valued logic, the dialect matrix for IF / IIF / IFF / CASE WHEN across MySQL, SQL Server, Snowflake, Postgres and BigQuery, NULLIF for safe division, COALESCE for default values, the IS NULL vs = NULL trap, the NOT IN with NULL classic interview gotcha, and the NULL contract in JOIN / GROUP BY / COUNT. Each section ships a worked interview answer with code, a step-by-step trace, an output table, and a concept-by-concept

Jun 4, 202666 min read
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SQL LIKE, REGEXP & Wildcard Pattern Matching for Data Engineers
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SQL LIKE, REGEXP & Wildcard Pattern Matching for Data Engineers

Gowtham Potureddi

SQL LIKE, REGEXP, and wildcard pattern matching for data engineers — % and _ wildcards, escape characters, ILIKE and case-insensitive matching across Postgres / MySQL / SQL Server / Snowflake / BigQuery, REGEXP capture groups and back-references, trigram indexes with pg_trgm + GIN, FULLTEXT in MySQL, and the production 'small dataset OK, big dataset trigram' rule. Each section ships a worked interview answer with code, a step-by-step trace, an output table, and a concept-by-concept why-this-work

Jun 4, 202668 min read
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SQL DISTINCT + COUNT(DISTINCT): Deduplication, Approximate Counts, HyperLogLog
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SQL DISTINCT + COUNT(DISTINCT): Deduplication, Approximate Counts, HyperLogLog

Gowtham Potureddi

SQL DISTINCT and SQL COUNT DISTINCT under the hood — how SELECT DISTINCT and GROUP BY compare, why COUNT(DISTINCT) costs O(distinct cardinality) memory and spills to disk, how HyperLogLog and APPROX_COUNT_DISTINCT give ~1.6% error in ~1.5 KB, and the three deduplication patterns every interview probes (ROW_NUMBER, DISTINCT ON, GROUP BY + ARG_MAX). Each section ships a worked teaching example and a Solution-Tail interview answer with code, a step-by-step trace, an output table, and a concept-by-c

Jun 4, 202667 min read
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SQL Subqueries: Correlated, Scalar, Derived Tables & EXISTS
De InterviewSql

SQL Subqueries: Correlated, Scalar, Derived Tables & EXISTS

Gowtham Potureddi

SQL subqueries deep dive for data engineering interviews — scalar subqueries with NULL gotchas, derived tables in FROM, correlated subqueries that reference the outer row, and the EXISTS / NOT EXISTS / IN / NOT IN decision matrix. Every section ships a worked example plus a Solution-Tail interview answer with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Practice SQL subqueries on PipeCode.

Jun 3, 202665 min read
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SQL ORDER BY, NULLS FIRST/LAST & Multi-Column Sorts
De InterviewSql

SQL ORDER BY, NULLS FIRST/LAST & Multi-Column Sorts

Gowtham Potureddi

Master sql order by — ASC vs DESC defaults, NULLS FIRST / NULLS LAST across Postgres, MySQL, SQL Server, Snowflake and BigQuery, multi-column tie-breakers, ORDER BY with LIMIT and indexes, and keyset pagination. Each section ships a worked interview answer with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Practice sorting on PipeCode.

Jun 3, 202672 min read
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SQL BETWEEN & Range Queries: Numeric, Date, Inclusive vs Exclusive
De InterviewSql

SQL BETWEEN & Range Queries: Numeric, Date, Inclusive vs Exclusive

Gowtham Potureddi

SQL BETWEEN and range queries explained — numeric BETWEEN over INT, NUMERIC, and FLOAT (and the IEEE-754 precision trap); the date BETWEEN time-truncation pitfall that silently drops 23 hours of records; half-open intervals [start, end) as the production default; OVERLAPS and tstzrange; and BETWEEN performance with B-tree, BRIN, and partition pruning. Each section ships a worked SQL example with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Practice SQL ra

Jun 3, 202666 min read
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SQL Aggregate Functions Deep Dive: SUM, AVG, MIN, MAX, COUNT(DISTINCT)
De InterviewSql

SQL Aggregate Functions Deep Dive: SUM, AVG, MIN, MAX, COUNT(DISTINCT)

Gowtham Potureddi

SQL aggregate functions deep dive — COUNT(*) vs COUNT(col) vs COUNT(DISTINCT), SUM/AVG NULL handling and BIGINT overflow, MIN/MAX on strings, dates, and NULLs, APPROX_COUNT_DISTINCT and HyperLogLog, GROUPING SETS, ROLLUP, CUBE, and FILTER (WHERE …). Every section ships a worked interview answer with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Practice analytics SQL on PipeCode.

Jun 3, 202665 min read
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SQL Data Types Deep Dive: INT, NUMERIC, VARCHAR, JSON, ARRAY, TIMESTAMP
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SQL Data Types Deep Dive: INT, NUMERIC, VARCHAR, JSON, ARRAY, TIMESTAMP

Gowtham Potureddi

SQL data types deep dive for data engineers — INT family, NUMERIC vs FLOAT for money, CHAR / VARCHAR / TEXT with encoding pitfalls, DATE / TIMESTAMP / TIMESTAMPTZ / INTERVAL across time zones, and semi-structured JSON / JSONB / ARRAY / STRUCT for evolving schemas. Each section ships a worked teaching example with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Practice SQL on PipeCode.

Jun 2, 202668 min read
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SQL Cheat Sheet: Clause Order, Joins, Aggregates, Windows (2026)
De InterviewSql

SQL Cheat Sheet: Clause Order, Joins, Aggregates, Windows (2026)

Gowtham Potureddi

SQL cheat sheet for data engineers — clause execution order (FROM, WHERE, GROUP BY, HAVING, SELECT, DISTINCT, ORDER BY, LIMIT), eight joins (INNER, LEFT, RIGHT, FULL, SELF, ANTI, SEMI, CROSS), five standard aggregates plus GROUPING SETS, ROLLUP, CUBE, and FILTER, and every window-function family (ranking, offset, frame, aggregate-as-window). Each section ships a worked interview answer with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Practice SQL on Pipe

Jun 2, 202665 min read
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Data Engineering Internship Guide: Resume, Projects & Interview Loops
De InterviewSql

Data Engineering Internship Guide: Resume, Projects & Interview Loops

Gowtham Potureddi

A 2026 playbook for landing a data engineering internship — the three-tier internship landscape, a one-page intern resume blueprint with before/after rewrites, a month-by-month application timeline, cold-outreach DM templates, the 3-round intern interview loop with sample SQL + Python questions, and a 12-week internship survival kit that converts the intern role into a return offer. Calibrated for undergrads, MS students, and bootcamp switchers chasing FAANG, scale-up, and startup DE seats.

Jun 2, 202667 min read
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What is Data Engineering? Role, Stack, Day-in-the-Life (2026)
De InterviewSql

What is Data Engineering? Role, Stack, Day-in-the-Life (2026)

Gowtham Potureddi

What is data engineering — a 2026 deep dive into the data engineer role, responsibilities, and definition. Compare data engineering vs data science and data engineering vs software engineering, then walk through the 5-layer modern DE stack, an hour-by-hour day in the life of a mid-level data engineer, and the L3 → L7 career ladder. Each section ships a worked example with a step-by-step trace, an output table, and a concept-by-concept why-this-works. Practice on PipeCode.

Jun 2, 202664 min read
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Data Engineering Skills: 10 Technical + 5 Soft Skills Hiring Managers Test
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Data Engineering Skills: 10 Technical + 5 Soft Skills Hiring Managers Test

Gowtham Potureddi

Data engineering skills hiring managers test in 2026 — the 10 technical skills (SQL, Python, dimensional modelling, Spark, Kafka, Airflow, cloud, warehouses, dbt, system design) and the 5 soft skills (stakeholder management, on-call discipline, communication, mentoring, prioritisation) that gate every DE loop. Each section ships a 30-second self-test, a code snippet or STAR template, and a concept-by-concept why-this-works. Practice on PipeCode.

Jun 2, 202664 min read
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Data Engineering Salary 2026: Levels, Locations & Comp Breakdowns
De InterviewSql

Data Engineering Salary 2026: Levels, Locations & Comp Breakdowns

Gowtham Potureddi

Data engineering salary in 2026 — levels (Junior → Principal), US tier 1/2/3 cities, Europe and India bands, comp structure (base, bonus, equity, sign-on), and a six-step negotiation playbook. Every section ships worked sample comp tables, location multipliers, total-comp grids, and step-by-step negotiation scripts with output tables and a why-this-works breakdown. Benchmark, negotiate, and practice DE interviews on PipeCode.

Jun 2, 202663 min read
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Data Engineering Projects: 8 Portfolio Projects to Land Your First DE Job
De InterviewSql

Data Engineering Projects: 8 Portfolio Projects to Land Your First DE Job

Gowtham Potureddi

Data engineering projects you can actually ship — eight portfolio projects laid out as a four-tier pyramid: a SQL warehouse and a dbt analytics repo for foundations, an Airflow daily ETL and a Kafka + Flink streaming aggregator for orchestration, a Spark batch and a dbt + Snowflake medallion for the modern stack, and a product analytics platform plus an ML feature pipeline for end-to-end. Each project ships with stack, sample code, build path, and the exact signals hiring managers look for.

Jun 2, 202665 min read
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Data Engineering Jobs: How to Land Your First DE Role in 2026
De InterviewSql

Data Engineering Jobs: How to Land Your First DE Role in 2026

Gowtham Potureddi

Data engineering jobs in 2026 — the hiring market, the application-to-offer funnel, the resume anatomy hiring managers actually scan, the LinkedIn + recruiter outreach cadence that books interviews, the 5-round DE interview loop end-to-end, and a first-90-days plan that turns the offer into a promotion path. Worked examples with templates, traces, output benchmarks, and concept-by-concept why-this-works callouts.

Jun 1, 202669 min read
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Data Engineering Courses & Self-Study Roadmap (2026): From SQL to Your First DE Job
De InterviewSql

Data Engineering Courses & Self-Study Roadmap (2026): From SQL to Your First DE Job

Gowtham Potureddi

Data engineering courses and a 2026 self-study roadmap — the 5-tier DE stack (SQL, Python, Spark, cloud + warehouse, orchestration + streaming), a 24-week week-by-week timeline, a free vs paid course matrix, a certification decision tree, and a starter stack per regional market. Every section ships a concrete checklist, a worked example, an output card, and a concept-by-concept why-this-works. Practice data engineering on PipeCode.

May 31, 202673 min read
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Apache Iceberg vs Delta Lake vs Hudi: Table Formats Compared for Data Engineering
De InterviewSql

Apache Iceberg vs Delta Lake vs Hudi: Table Formats Compared for Data Engineering

Gowtham Potureddi

Apache Iceberg vs Delta Lake vs Hudi — a complete deep-dive comparison of the three open table formats that power the modern lakehouse. Iceberg snapshots and manifest layers, Delta Lake transaction log and checkpoints, Hudi Copy-on-Write vs Merge-on-Read, catalog stories, engine reach, streaming upserts, schema and partition evolution, and a five-dimension decision matrix every data engineer needs. Practice on PipeCode.

May 31, 202664 min read
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Kimball Dimensional Modeling for Data Engineering Interviews: Facts, Dimensions, Grain & SCDs
De InterviewSql

Kimball Dimensional Modeling for Data Engineering Interviews: Facts, Dimensions, Grain & SCDs

Gowtham Potureddi

Kimball dimensional modeling for data engineering interviews — facts, dimensions, grain, surrogate keys, conformed dimensions, the Kimball bus matrix, and SCD Types 1/2/3/6 with full SQL. Walks the canonical 4-step design process (business process → grain → dimensions → facts) and ships every interview answer with code, traced execution, and a sample output. Practice on PipeCode.

May 31, 202672 min read
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Data Lakehouse vs Data Warehouse vs Data Lake: Which Architecture Wins
De InterviewSql

Data Lakehouse vs Data Warehouse vs Data Lake: Which Architecture Wins

Gowtham Potureddi

Data lakehouse vs data warehouse vs data lake — a deep-dive comparison of the three modern analytical architectures. Warehouse (schema-on-write, ETL, star schema, BI-first), lake (schema-on-read, ELT, open formats, cheap raw storage), and lakehouse (Delta / Iceberg / Hudi open tables + multi-engine compute) — with a five-dimension decision matrix, worked migration scenarios, and interview-grade SQL. Practice on PipeCode.

May 31, 202661 min read
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ACID, BASE & Transactions in SQL for Data Engineers
De InterviewSql

ACID, BASE & Transactions in SQL for Data Engineers

Gowtham Potureddi

ACID, BASE and transactions in SQL — a deep-dive guide for data engineers. Atomicity, Consistency, Isolation, Durability with SQL examples; isolation levels from Read Uncommitted to Serializable and the anomalies each blocks; BASE properties, CAP theorem and eventual consistency; an ACID vs BASE decision matrix; plus a cheat sheet and interview-ready Q&A. Practice on PipeCode.

May 30, 202665 min read
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SQL Query Optimization: EXPLAIN Plans, Indexes & Tuning Techniques for Data Engineers
De InterviewSql

SQL Query Optimization: EXPLAIN Plans, Indexes & Tuning Techniques for Data Engineers

Gowtham Potureddi

am", "short_description": "SQL query optimization — a complete deep-dive on EXPLAIN plans, index types (B-tree, Hash, Partial, Covering), join algorithms (Nested Loop, Hash, Merge), and the six-step tuning playbook every data engineer should run from slow query to sub-second. Worked examples, traces, cost models, and SARGable rewrite patterns. Practice on PipeCode.

May 30, 202665 min read
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Databricks Lakehouse + Medallion Architecture: Bronze, Silver, Gold with Delta
De InterviewSql

Databricks Lakehouse + Medallion Architecture: Bronze, Silver, Gold with Delta

Gowtham Potureddi

Databricks lakehouse and the medallion architecture, end to end. Lakehouse anatomy (storage + transactional Delta + multi-engine compute + Unity Catalog), Bronze raw → Silver cleansed → Gold business marts, Delta Lake mechanics (ACID, time travel, OPTIMIZE, Z-ORDER, MERGE), Delta Live Tables, Auto Loader, and a production sources → BI pipeline — every concept rebuilt as a worked interview-grade question. Practice on PipeCode.

May 30, 202660 min read
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Data Orchestration Compared: Airflow vs Dagster vs Prefect — A Modern Stack Guide
De InterviewSql

Data Orchestration Compared: Airflow vs Dagster vs Prefect — A Modern Stack Guide

Gowtham Potureddi

Data orchestration deep dive — Airflow vs Dagster vs Prefect compared anatomy-first. DAGs, operators, scheduler + executor + metadata DB (Airflow); software-defined assets, IO managers, the data catalog (Dagster); flows, tasks, work pools, deployments (Prefect); plus a five-dimension decision matrix and worked migration examples. Practice on PipeCode.

May 30, 202663 min read
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Star Schema vs Snowflake Schema: Dimensional Modeling for Data Engineering
De InterviewSql

Star Schema vs Snowflake Schema: Dimensional Modeling for Data Engineering

Gowtham Potureddi

Star schema vs snowflake schema — the definitive dimensional modeling guide for data engineering interviews. Fact tables, dimension tables, grain, conformed dimensions, SCD types, normalisation trade-offs, and a five-dimension query-speed / ETL / storage / BI / use-case matrix with a four-question decision tree and a worked SQL playbook. Practice on PipeCode.

May 29, 202661 min read
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Databricks Certification (Data Engineer Associate): Full Prep Guide
De InterviewSql

Databricks Certification (Data Engineer Associate): Full Prep Guide

Gowtham Potureddi

Databricks Certification — the Data Engineer Associate full prep guide. The five exam domains and their weights (Lakehouse Platform 24%, ELT with Spark SQL + Python 29%, Incremental Data Processing 22%, Production Pipelines 16%, Data Governance 9%), a six-week study plan, six minimum-viable hands-on labs, the Spark + Delta Lake primitives every question tests, the practice-exam stack, exam-day Kryterion proctoring, and the DE Associate to DE Professional career path. Practice on PipeCode.

May 29, 202663 min read
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dbt for Data Engineering: Models, Tests, Macros & Production Patterns
De InterviewSql

dbt for Data Engineering: Models, Tests, Macros & Production Patterns

Gowtham Potureddi

dbt for data engineering — the complete deep-dive guide: project structure and profiles, models with ref / source / materializations and layered DAGs, the three test families (generic, singular, contracts), macros and Jinja templating, the community package ecosystem (dbt_utils, dbt_expectations, dbt_audit_helper, Elementary), and production CI/CD patterns (Slim CI, dbt Cloud vs Core, Airflow orchestration). Practice on PipeCode.

May 29, 202666 min read
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Data Pipeline Design: Batch vs Streaming, Idempotency, Backfills
De InterviewSql

Data Pipeline Design: Batch vs Streaming, Idempotency, Backfills

Gowtham Potureddi

Data pipeline design — a 7-section deep dive. Batch architectures (Airflow DAG + dbt + warehouse), streaming architectures (Kafka + Flink Kappa with replay), idempotency patterns (MERGE INTO, dedup keys, deterministic hash), backfill strategies (full-table, partition-aware, log replay), observability + SLOs, and the eight production failure modes every senior pipeline-design loop tests. Practice on PipeCode.

May 29, 202676 min read
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ETL Testing Interview Questions & Answers — A Complete Deep-Dive Guide
De InterviewSql

ETL Testing Interview Questions & Answers — A Complete Deep-Dive Guide

Gowtham Potureddi

ETL testing interview questions and answers — a complete deep-dive guide. Metadata + schema testing, completeness and row-count parity, transformation-logic testing, performance / reconciliation / regression rounds, DQ frameworks (Great Expectations, Soda Core, dbt tests, Monte Carlo), and the career playbook every ETL tester needs. Practice on PipeCode.

May 28, 202666 min read
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ETL Tools Compared: Airflow, dbt, Fivetran, Glue, Talend, Informatica — A Deep Engineering Guide
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ETL Tools Compared: Airflow, dbt, Fivetran, Glue, Talend, Informatica — A Deep Engineering Guide

Gowtham Potureddi

ETL tools comparison guide — Airflow, dbt, Fivetran, AWS Glue, Talend, and Informatica covered as a deep engineering tour. Tool taxonomy (orchestration / transform / EL / full ETL), Airflow DAG anatomy and executors, dbt project layering and materializations, Fivetran connector model and MAR billing, Glue / Talend / Informatica internals, six pricing shapes with a worked monthly-cost example, and three production stack patterns for 2026. Practice on PipeCode.

May 28, 202667 min read
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Apache Flink for Data Engineering Interviews: Streaming, Watermarks, State & Exactly-Once
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Apache Flink for Data Engineering Interviews: Streaming, Watermarks, State & Exactly-Once

Gowtham Potureddi

Apache Flink interview questions for data engineers — the DataStream API and the streaming dataflow graph, event-time vs processing-time, watermarks and allowed lateness, windows (tumbling, sliding, session, global), keyed state and operator state with hashmap vs RocksDB backends, checkpointing and savepoints, exactly-once via two-phase commit, and Flink SQL plus Flink CDC. Each section ships a worked interview answer with code, a step-by-step trace, an output table, and a concept-by-concept why

May 28, 202643 min read
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Slowly Changing Dimensions (SCD) for Data Engineering Interviews: Type 1, 2, 3, 6 with SQL & dbt
De InterviewSql

Slowly Changing Dimensions (SCD) for Data Engineering Interviews: Type 1, 2, 3, 6 with SQL & dbt

Gowtham Potureddi

Slowly Changing Dimensions interview questions for data engineers — Type 1 (overwrite), Type 2 (add row with valid_from / valid_to / is_current), Type 3 (add column for previous value), Type 6 (hybrid), with SQL MERGE INTO implementations, surrogate-key strategies, effective-date join patterns, dbt snapshot strategies (timestamp vs check), and the late-arriving / retroactive-delete / surrogate-key-collision gotchas every senior round probes. Each section ships a worked interview answer with code

May 28, 202644 min read
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Change Data Capture (CDC) for Data Engineering Interviews: Debezium, Log-Based vs Trigger-Based, Kafka Connect
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Change Data Capture (CDC) for Data Engineering Interviews: Debezium, Log-Based vs Trigger-Based, Kafka Connect

Gowtham Potureddi

Change Data Capture interview questions for data engineers — the three CDC strategies (query-based, trigger-based, log-based), Debezium architecture, snapshot vs streaming modes, CDC into Kafka via Kafka Connect, the dual-writes trap and the outbox pattern, schema evolution, and op-aware MERGE sinks into Snowflake and BigQuery. Each section ships a worked interview answer with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Practice streaming on PipeCode.

May 27, 202646 min read
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Apache Airflow Interview Questions: DAGs, Operators, Sensors, XComs & Schedulers
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Apache Airflow Interview Questions: DAGs, Operators, Sensors, XComs & Schedulers

Gowtham Potureddi

Apache Airflow interview questions for data engineers — DAGs and task dependencies, operators and sensors, XComs and the TaskFlow API, the scheduler with Local/Celery/Kubernetes executors, retries and SLAs and backfills, and modern Airflow (dynamic task mapping, datasets, deferrable operators). Each section ships a worked interview answer with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Practice workflow orchestration on PipeCode.

May 27, 202647 min read
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Apache Kafka Interview Questions for Data Engineers: Topics, Partitions, Consumer Groups & Exactly-Once Semantics
De InterviewSql

Apache Kafka Interview Questions for Data Engineers: Topics, Partitions, Consumer Groups & Exactly-Once Semantics

Gowtham Potureddi

Apache Kafka interview questions for data engineers — topics and partitions, replication and ISR, producer acks and idempotence, consumer groups and cooperative-sticky rebalancing, exactly-once semantics with transactions, Kafka Connect, and Kafka Streams. Each section ships a worked interview answer with code, a step-by-step trace, an output table, and a concept-by-concept why-this-works. Practice streaming on PipeCode.

May 27, 202649 min read
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Snowflake vs Databricks vs BigQuery vs Synapse: Choosing a Data Warehouse
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Snowflake vs Databricks vs BigQuery vs Synapse: Choosing a Data Warehouse

Gowtham Potureddi

Snowflake vs Databricks vs BigQuery vs Synapse — a 2026 decision-help comparison across architecture (compute / storage / catalog), pricing (credits, DBUs, $/TB scanned, DWUs), workload fit (BI, ELT, streaming, ML, sharing, Iceberg/Delta), and a two-question decision tree. With worked cost examples and scenario walk-throughs. Practice on PipeCode.

May 27, 202644 min read
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GCP Data Engineering: BigQuery, Dataflow, Pub/Sub, Composer
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GCP Data Engineering: BigQuery, Dataflow, Pub/Sub, Composer

Gowtham Potureddi

GCP data engineering end-to-end — BigQuery (Dremel, Colossus, slots, partitioning + clustering, Editions), Cloud Dataflow (Apache Beam, batch + streaming, windows + watermark + triggers, autoscaling), Pub/Sub (topics, push vs pull, at-least-once + ordering keys + DLQ), Cloud Composer (managed Airflow on GKE, DAG operators), and the wider GCP ecosystem. Practice on PipeCode.

May 26, 202646 min read
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AWS Data Engineer Associate (DEA-C01) Certification: Prep Roadmap
De InterviewPython

AWS Data Engineer Associate (DEA-C01) Certification: Prep Roadmap

Gowtham Potureddi

AWS Data Engineer Associate (DEA-C01) certification prep roadmap — exam overview (~85 scored questions, 130 minutes, pass ~720 / 1000), the four exam domains and their weighting, an 8-week study plan, six minimum-viable hands-on labs, a four-tier resource stack, and exam-day tips. Practice on PipeCode.

May 26, 202645 min read
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AWS Data Engineering: Glue, EMR, Athena, Kinesis — End-to-End Guide
De InterviewSql

AWS Data Engineering: Glue, EMR, Athena, Kinesis — End-to-End Guide

Gowtham Potureddi

AWS data engineering end-to-end guide — AWS Glue (Crawlers, Data Catalog, Jobs, bookmarks, Iceberg), Amazon EMR (Master / Core / Task, EMRFS, YARN, Serverless), Amazon Athena + Kinesis streaming patterns (Data Streams, Firehose, S3, projection pushdown), and the wider AWS lakehouse ecosystem. Practice on PipeCode.

May 26, 202646 min read
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Azure Data Engineering Interview Questions: Lake Design, Streaming, Scenarios, Security + Cost
De InterviewSql

Azure Data Engineering Interview Questions: Lake Design, Streaming, Scenarios, Security + Cost

Gowtham Potureddi

Azure data engineer interview questions — the rounds that decide offers: ADLS Gen2 lake design (hierarchical namespace, medallion, partition keys, ACL vs RBAC, lifecycle policies), Event Hubs streaming (partitions, consumer groups, Capture, Stream Analytics vs Structured Streaming), scenario rounds (incremental load with watermarks, CDC with Debezium, SCD Type 2 MERGE INTO), and senior-grade security + governance + cost-optimization (Managed Identity, Private Endpoint, Key Vault, Purview lineage

May 26, 202654 min read
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Azure Data Engineering: Synapse, ADF, Databricks — Full Guide
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Azure Data Engineering: Synapse, ADF, Databricks — Full Guide

Gowtham Potureddi

Azure data engineering full guide — Azure Data Factory (pipelines, activities, integration runtimes), Azure Synapse Analytics (dedicated SQL, serverless SQL, Spark pools), Azure Databricks (clusters, Delta Lake, Unity Catalog), ADLS Gen2 medallion lakehouse, and the layered Azure data platform every modern DE team ships on. Practice on PipeCode.

May 26, 202652 min read
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Hadoop Interview Questions for Data Engineers: HDFS, YARN, MapReduce
De InterviewSql

Hadoop Interview Questions for Data Engineers: HDFS, YARN, MapReduce

Gowtham Potureddi

Hadoop interview questions for data engineering — HDFS architecture (NameNode, DataNode, blocks, replication), YARN resource manager (ResourceManager, NodeManager, ApplicationMaster), MapReduce execution model (mappers, combiners, partitioners, reducers, shuffle and sort), Hive vs Pig, the Hadoop ecosystem, and the configuration patterns every data-engineering loop tests. Practice on PipeCode.

May 24, 202637 min read
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Apache Spark Interview Questions: Architecture, Shuffle, Caching, Tuning
De InterviewSql

Apache Spark Interview Questions: Architecture, Shuffle, Caching, Tuning

Gowtham Potureddi

Apache Spark interview questions for data engineering — Spark architecture (driver, executors, cluster manager), the DAG and stage boundary, shuffle internals, caching and persistence, partitioning strategy, the Catalyst optimizer and Tungsten engine, Adaptive Query Execution (AQE), Spark SQL vs DataFrame vs RDD, and the cluster-tuning patterns every senior Spark loop tests. Practice on PipeCode.

May 24, 202636 min read
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PySpark Interview Questions: Top DataFrame, RDD & Optimization Patterns
De InterviewSql

PySpark Interview Questions: Top DataFrame, RDD & Optimization Patterns

Gowtham Potureddi

PySpark interview questions for data engineering — DataFrame API vs RDD, lazy evaluation and the DAG, transformations vs actions, joins and shuffle, broadcast joins, caching and persist, partitioning, the Catalyst optimizer, and the optimization patterns every PySpark data-engineering loop tests. Practice on PipeCode.

May 24, 202636 min read
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Python for Data Engineering: A Complete Beginner's Guide
De InterviewSql

Python for Data Engineering: A Complete Beginner's Guide

Gowtham Potureddi

Python for data engineering — a complete beginner's guide. Core Python data structures (lists, dicts, sets, tuples), file I/O and CSV / JSON parsing, list comprehensions and generators, pandas DataFrames for ETL, working with APIs and databases via SQLAlchemy / psycopg2, error handling, and the patterns every junior data engineer needs to land their first DE role. Practice on PipeCode.

May 24, 202638 min read
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PL/SQL Interview Questions: Procedures, Cursors, Triggers & Packages
De InterviewSql

PL/SQL Interview Questions: Procedures, Cursors, Triggers & Packages

Gowtham Potureddi

PL/SQL interview questions for data engineering interviews — stored procedures and functions, explicit and implicit cursors, FOR cursor loops, triggers (BEFORE / AFTER / INSTEAD OF), packages and package bodies, exception handling, bulk collect, and Oracle-specific patterns every data-engineering loop tests. Practice SQL on PipeCode.

May 23, 202638 min read
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MySQL Interview Questions & Answers: Top Patterns for Data Engineers
De InterviewSql

MySQL Interview Questions & Answers: Top Patterns for Data Engineers

Gowtham Potureddi

MySQL interview questions and answers for data engineering interviews — InnoDB vs MyISAM, indexing strategy, query optimization with EXPLAIN, common MySQL-specific syntax (LIMIT, IFNULL, GROUP_CONCAT), transactions, locks, JSON columns, replication, and the dialect quirks every MySQL data-engineering loop tests. Practice SQL on PipeCode.

May 23, 202638 min read
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CREATE TABLE & ALTER TABLE in SQL: Schema Design for Data Engineers
De InterviewSql

CREATE TABLE & ALTER TABLE in SQL: Schema Design for Data Engineers

Gowtham Potureddi

SQL CREATE TABLE and ALTER TABLE for data engineering interviews — column data types, constraints (PRIMARY KEY, FOREIGN KEY, NOT NULL, UNIQUE, CHECK), DEFAULT values, indexes, ALTER TABLE ADD/DROP/MODIFY/RENAME column, online migrations, and dialect quirks across PostgreSQL, MySQL, SQL Server, Oracle, Snowflake. Maps to common sql interview questions. Practice SQL on PipeCode.

May 23, 202640 min read
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INSERT, UPDATE, DELETE in SQL: Safe CRUD Patterns for Data Engineers
De InterviewSql

INSERT, UPDATE, DELETE in SQL: Safe CRUD Patterns for Data Engineers

Gowtham Potureddi

SQL INSERT, UPDATE, DELETE for data engineering interviews — safe CRUD patterns, INSERT INTO SELECT, UPDATE FROM JOIN, DELETE vs TRUNCATE vs DROP, MERGE / UPSERT / ON CONFLICT, transactions, rollback safety, and dialect quirks across PostgreSQL, MySQL, SQL Server, Oracle, Snowflake. Maps to common sql interview questions. Practice SQL on PipeCode.

May 23, 202643 min read
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SQL CAST, CONVERT & Type Coercion: Safe Conversions for Data Engineers
De InterviewSql

SQL CAST, CONVERT & Type Coercion: Safe Conversions for Data Engineers

Gowtham Potureddi

SQL CAST, CONVERT, and implicit type coercion for data engineering interviews — explicit vs implicit conversion, TRY_CAST / TRY_CONVERT for safe parsing, numeric / string / date conversions, dialect quirks across PostgreSQL, MySQL, SQL Server, Oracle, Snowflake, and the lossy-cast gotchas that fail candidates. Maps to common sql interview questions. Practice SQL on PipeCode.

May 23, 202640 min read
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SQL PIVOT and UNPIVOT: Reshape Rows ↔ Columns for Analytics
De InterviewSql

SQL PIVOT and UNPIVOT: Reshape Rows ↔ Columns for Analytics

Gowtham Potureddi

SQL PIVOT and UNPIVOT for data engineering interviews — reshape long-format rows into wide-format columns and back, with native PIVOT / UNPIVOT (SQL Server, Oracle, Snowflake) and the portable SUM(CASE WHEN …) + UNION ALL idioms (PostgreSQL, MySQL). Maps to common sql interview questions. Practice SQL on PipeCode.

May 23, 202643 min read
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SQL UNION vs UNION ALL vs INTERSECT vs EXCEPT
De InterviewSql

SQL UNION vs UNION ALL vs INTERSECT vs EXCEPT

Gowtham Potureddi

SQL set operations for data engineering interviews — UNION, UNION ALL, INTERSECT, EXCEPT (MINUS) semantics, deduplication, column-count and type-compatibility rules, dialect quirks across PostgreSQL, MySQL, SQL Server, Oracle, Snowflake. Maps to common sql interview questions. Practice SQL on PipeCode.

May 23, 202642 min read
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SQL String Functions: CONCAT, SUBSTRING, REPLACE, TRIM, REGEXP
De InterviewSql

SQL String Functions: CONCAT, SUBSTRING, REPLACE, TRIM, REGEXP

Gowtham Potureddi

SQL string functions guide for data engineering interviews — CONCAT, SUBSTRING, REPLACE, TRIM, REGEXP, LIKE vs regex, and dialect quirks across PostgreSQL, MySQL, SQL Server, Snowflake. Maps to common sql interview questions. Practice SQL on PipeCode.

May 22, 202636 min read
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SQL Date Functions: DATEDIFF, DATE_FORMAT, EXTRACT & Date Math
De InterviewSql

SQL Date Functions: DATEDIFF, DATE_FORMAT, EXTRACT & Date Math

Gowtham Potureddi

SQL date functions guide for data engineering interviews — DATEDIFF, DATE_FORMAT, EXTRACT, DATE_TRUNC, INTERVAL arithmetic, time zones, and dialect quirks across PostgreSQL, MySQL, SQL Server, Snowflake. Maps to common sql interview questions. Practice SQL on PipeCode.

May 22, 202642 min read
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GROUP BY and HAVING in SQL: Aggregation Patterns for Interviews
De InterviewSql

GROUP BY and HAVING in SQL: Aggregation Patterns for Interviews

Gowtham Potureddi

GROUP BY and HAVING in SQL for data engineering interviews — row-collapse model, sql aggregate functions (COUNT, SUM, AVG, MIN, MAX), WHERE vs HAVING execution-order trap, group by multiple columns sql, ROLLUP / CUBE / GROUPING SETS subtotals. Maps to common sql interview questions. Practice SQL on PipeCode.

May 22, 202643 min read
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SQL CASE WHEN Statement: Conditional Logic for Data Engineering
De InterviewSql

SQL CASE WHEN Statement: Conditional Logic for Data Engineering

Gowtham Potureddi

SQL CASE WHEN guide for data engineering — searched vs simple form, conditional aggregation (SUM CASE WHEN), pivot rows to columns, NULLIF safe math, dialect alternatives (FILTER, IIF, DECODE). Maps to common sql interview questions. Practice SQL on PipeCode.

May 22, 202649 min read
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SQL Joins Interview Questions: INNER, LEFT, RIGHT, FULL, SELF & ANTI Joins
De InterviewSql

SQL Joins Interview Questions: INNER, LEFT, RIGHT, FULL, SELF & ANTI Joins

Gowtham Potureddi

SQL joins interview guide — INNER, LEFT, RIGHT, FULL OUTER, CROSS, SELF, and ANTI joins with PostgreSQL examples, traces, outputs, and the ON vs WHERE trap. Maps to common sql interview questions. Practice SQL on PipeCode.

May 22, 202645 min read
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SQL Window Functions for Data Engineering Interviews: ROW_NUMBER, RANK, LAG/LEAD, and Running Totals
De InterviewSql

SQL Window Functions for Data Engineering Interviews: ROW_NUMBER, RANK, LAG/LEAD, and Running Totals

Gowtham Potureddi

SQL window functions for DE interviews — OVER, PARTITION BY, ORDER BY, frame clause, ROW_NUMBER vs RANK vs DENSE_RANK, LAG/LEAD, running totals, moving averages, Top-N per group. Maps to common sql interview questions. Practice SQL on PipeCode.

May 22, 202656 min read
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capital one Data Engineering Interview Questions: Full Prep Guide
De InterviewSql

capital one Data Engineering Interview Questions: Full Prep Guide

Gowtham Potureddi

capital one DE interview prep: indexed company hub first, then SQL joins, aggregations, streaming literacy, window functions, dimensional modeling, and Python-friendly widen lanes.

May 20, 202627 min read
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instacart Data Engineering Interview Questions: Full Prep Guide
De InterviewSql

instacart Data Engineering Interview Questions: Full Prep Guide

Gowtham Potureddi

instacart DE interview prep: indexed company hub first, then SQL joins, aggregations, streaming literacy, window functions, dimensional modeling, and Python-friendly widen lanes.

May 20, 202627 min read
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open ai Data Engineering Interview Questions: Full Prep Guide
De InterviewSql

open ai Data Engineering Interview Questions: Full Prep Guide

Gowtham Potureddi

open ai DE interview prep: indexed company hub first, then SQL joins, aggregations, streaming literacy, window functions, dimensional modeling, and Python-friendly widen lanes.

May 20, 202627 min read
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ziprecruiter Data Engineering Interview Questions: Full Prep Guide
De InterviewSql

ziprecruiter Data Engineering Interview Questions: Full Prep Guide

Gowtham Potureddi

ziprecruiter DE interview prep: indexed company hub first, then SQL joins, aggregations, streaming literacy, window functions, dimensional modeling, and Python-friendly widen lanes.

May 20, 202626 min read
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chime Data Engineering Interview Questions: Full Prep Guide
De InterviewSql

chime Data Engineering Interview Questions: Full Prep Guide

Gowtham Potureddi

chime DE interview prep: indexed company hub first, then SQL joins, aggregations, streaming literacy, window functions, dimensional modeling, and Python-friendly widen lanes.

May 20, 202626 min read
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nyctimes Data Engineering Interview Questions: Full Prep Guide
De InterviewSql

nyctimes Data Engineering Interview Questions: Full Prep Guide

Gowtham Potureddi

nyctimes DE interview prep: indexed company hub first, then SQL joins, aggregations, streaming literacy, window functions, dimensional modeling, and Python-friendly widen lanes.

May 20, 202626 min read
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shaw Data Engineering Interview Questions: Full Prep Guide
De InterviewSql

shaw Data Engineering Interview Questions: Full Prep Guide

Gowtham Potureddi

shaw DE interview prep: indexed company hub first, then SQL joins, aggregations, streaming literacy, window functions, dimensional modeling, and Python-friendly widen lanes.

May 20, 202626 min read
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Spur Data Engineering Interview Questions: Full DE Prep Guide
De InterviewSql

Spur Data Engineering Interview Questions: Full DE Prep Guide

Gowtham Potureddi

Spur DE prep from the live PipeCode hub — Medium SQL anchor (#195 grouping · aggregation · joins themes) plus grouping, aggregation, and joins lane drills.

May 17, 202620 min read
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zeta Data Engineering Interview Questions: Full Prep Guide
De InterviewSql

zeta Data Engineering Interview Questions: Full Prep Guide

Gowtham Potureddi

zeta DE interview prep: indexed hub plus Stable Atom Selection Python card, then SQL joins, aggregations, streaming literacy, window functions, dimensional modeling, and Python widen lanes.

May 17, 202626 min read
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pelaton Data Engineering Interview Questions: Full Prep Guide
De InterviewSql

pelaton Data Engineering Interview Questions: Full Prep Guide

Gowtham Potureddi

pelaton DE interview prep: indexed company hub first, then SQL joins, aggregations, streaming literacy, window functions, and dimensional modeling widen lanes.

May 17, 202626 min read
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tiktok Data Engineering Interview Questions: Full Prep Guide
De InterviewSql

tiktok Data Engineering Interview Questions: Full Prep Guide

Gowtham Potureddi

tiktok DE interview prep: indexed company hub first, then SQL joins, aggregations, streaming literacy, window functions, dimensional modeling, and Python-friendly widen lanes.

May 17, 202625 min read
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LeetCode Data Engineering Interview Questions: Full DE Prep Guide
De InterviewSql

LeetCode Data Engineering Interview Questions: Full DE Prep Guide

Gowtham Potureddi

LeetCode DE prep from the live PipeCode hub — Medium Python anchor #272 Stop Words (split · strip · tokens) plus SQL, Python, and data modeling lanes.

May 17, 202625 min read
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Exodus Point Data Engineering Interview Questions: Full Prep Guide
De InterviewSql

Exodus Point Data Engineering Interview Questions: Full Prep Guide

Gowtham Potureddi

Exodus Point DE prep—exoduspoint hub plus exodus-point Python & sorting lanes; SQL grain, heaps, merge sorts, window ranks.

May 16, 202625 min read
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Aircall Data Engineering Interview Questions: Full Prep Guide
De InterviewSql

Aircall Data Engineering Interview Questions: Full Prep Guide

Gowtham Potureddi

Aircall DE interview prep: indexed company hub first, then SQL joins, aggregations, streaming literacy, windows, and dimensional modeling widen lanes.

May 16, 202632 min read
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Harvey Nash Data Engineering Interview Questions: Full Prep Guide
De InterviewSql

Harvey Nash Data Engineering Interview Questions: Full Prep Guide

Gowtham Potureddi

Harvey Nash DE prep on PipeCode—hub, SQL lane, medium slice, indexed joins & aggregations topics, then global SQL widen lanes.

May 16, 202624 min read
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Agoda Data Engineering Interview Questions: Full Prep Guide
De InterviewSql

Agoda Data Engineering Interview Questions: Full Prep Guide

Gowtham Potureddi

Agoda DE interview prep mapped to PipeCode’s hub, Python lane, and indexed array/sorting slices—plus SQL widen drills for pipeline screens.

May 16, 202625 min read
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Tiger Analytics Data Engineering Interview Questions: Full Prep Guide
De InterviewSql

Tiger Analytics Data Engineering Interview Questions: Full Prep Guide

Gowtham Potureddi

Tiger Analytics DE prep on PipeCode—anchor on the indexed company hub plus medium-difficulty slice, then deepen SQL aggregates, joins, windows, and dimensional modeling reps clients expect.

May 16, 202624 min read
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LinkedIn Data Engineering Interview Questions: Full Prep Guide
De InterviewSql

LinkedIn Data Engineering Interview Questions: Full Prep Guide

Gowtham Potureddi

LinkedIn DE prep on PipeCode: start from the hub + data-modeling lane, then dimensional, SCD, event, and cardinality drills with SQL join bridges.

May 15, 202623 min read
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Roblox Data Engineering Interview Questions: Full DE Prep Guide
De InterviewSql

Roblox Data Engineering Interview Questions: Full DE Prep Guide

Gowtham Potureddi

Roblox DE prep from the live PipeCode hub — two Hard anchors (#301 Python strings/hash-table themes, #337 SQL windows + aggregation + string functions) plus topic lane drills.

May 15, 202621 min read
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Tesla Data Engineering Interview Questions: Full DE Prep Guide
De InterviewSql

Tesla Data Engineering Interview Questions: Full DE Prep Guide

Gowtham Potureddi

Tesla DE prep from the live hub — Python hash-table counting plus API Integration merges; two Medium anchors and topic lanes on PipeCode.

May 15, 202628 min read
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Exodus Point Data Engineering Interview Questions: Full DE Prep Guide
De InterviewSql

Exodus Point Data Engineering Interview Questions: Full DE Prep Guide

Gowtham Potureddi

Exodus Point data engineering prep — SQL grain & joins, window ranks, Python heaps & merge, sorting & top-K. Company-tagged practice on PipeCode.

May 15, 202633 min read
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Senior SQL: Advanced Joins, Window Analytics, Plans, Indexing & Production Mindset
De InterviewSql

Senior SQL: Advanced Joins, Window Analytics, Plans, Indexing & Production Mindset

Gowtham Potureddi

Senior SQL for data engineers — join cardinality & strategies, window frames, recursive CTEs, EXPLAIN plans, indexing & partitions, isolation & locks, modeling & ETL SQL. Interview depth. Practice on PipeCode.

May 13, 202630 min read
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Reporting Services in SQL (SSRS): Architecture, Report Types, RDL & Interview Notes
De InterviewSql

Reporting Services in SQL (SSRS): Architecture, Report Types, RDL & Interview Notes

Gowtham Potureddi

Reporting services in SQL — SSRS architecture, report server & RDL, datasets vs data sources, parameters, scheduling, security, SSRS vs Power BI, and interview-ready SQL patterns. Practice SQL on PipeCode.

May 13, 202632 min read
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SQL for Developers: Relational Foundations, Safe CRUD, Joins, Aggregates & Performance Muscle Memory
De InterviewSql

SQL for Developers: Relational Foundations, Safe CRUD, Joins, Aggregates & Performance Muscle Memory

Gowtham Potureddi

SQL for developers — tables and keys, safe SELECT/UPDATE/DELETE, WHERE and NULL pitfalls, INNER vs LEFT joins, GROUP BY/HAVING vs windows, indexes plus ACID transactions, and EXPLAIN-friendly habits. Postgres-first examples. Practice on PipeCode.

May 13, 202628 min read
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CTE in SQL for Data Engineering Interviews: WITH Clauses, Recursive CTEs, and Window SQL Patterns
De InterviewSql

CTE in SQL for Data Engineering Interviews: WITH Clauses, Recursive CTEs, and Window SQL Patterns

Gowtham Potureddi

CTE in SQL guide for interviews — Common Table Expressions, WITH chaining, aggregates + joins, sql window functions (ROW_NUMBER rank-then-filter), WITH RECURSIVE hierarchies, CTE vs subquery vs temp table. Maps to common sql interview questions. Practice SQL on PipeCode.

May 13, 202632 min read
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Data Warehouse Design for Data Engineering Interviews: A Beginner's Guide to Fact Tables, Star Schemas, and Grain
De InterviewSql

Data Warehouse Design for Data Engineering Interviews: A Beginner's Guide to Fact Tables, Star Schemas, and Grain

Gowtham Potureddi

Data warehouse design guide for beginners — OLTP vs OLAP, fact tables, dimension tables, star schema vs snowflake schema, grain, surrogate keys, slowly changing dimensions, partitioning, and the Kimball six-step design process. Practice SQL on PipeCode.

May 12, 202673 min read
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ETL Pipeline for Data Engineering: A Beginner's Guide to Extract, Transform, and Load
De InterviewSql

ETL Pipeline for Data Engineering: A Beginner's Guide to Extract, Transform, and Load

Gowtham Potureddi

ETL pipeline guide for beginners — Extract from databases / APIs / files / SaaS, Transform with cleaning / deduplication / standardization / aggregation, Load into Redshift / Snowflake / data lakes, ETL vs ELT, orchestration with Airflow / dbt / Spark / AWS Glue, and a runnable Python pandas pipeline. Practice on PipeCode.

May 12, 202670 min read
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Snowflake for Data Engineering Interviews: A Beginner's Guide to the Cloud Data Warehouse
De InterviewSql

Snowflake for Data Engineering Interviews: A Beginner's Guide to the Cloud Data Warehouse

Gowtham Potureddi

Snowflake data engineering guide for beginners — 3-layer architecture, separation of compute and storage, virtual warehouses, COPY INTO, Time Travel, zero-copy cloning, micro-partitions, query pruning, and Snowflake vs Redshift vs BigQuery. Practice on PipeCode.

May 12, 202671 min read
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Amazon Redshift for Data Engineering — Columnar Storage, MPP, COPY, Distribution Keys, Spectrum
De InterviewSql

Amazon Redshift for Data Engineering — Columnar Storage, MPP, COPY, Distribution Keys, Spectrum

Gowtham Potureddi

Amazon Redshift for data engineering interviews — columnar storage and massively parallel processing for fast analytics, distribution styles (EVEN, KEY, ALL) and sort keys for join and filter performance, the COPY command and leader/compute node architecture for loading and executing queries, and Redshift Spectrum plus VACUUM and ANALYZE for querying S3 and maintaining the warehouse.

May 12, 202663 min read
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PostgreSQL SQL Data Types: Practical Column-Type Guide
De InterviewSql

PostgreSQL SQL Data Types: Practical Column-Type Guide

Gowtham Potureddi

PostgreSQL SQL data types — numeric, text, dates, JSONB, casts. Pick safer columns, avoid rounding and timezone traps, and reason about implicit coercion so joins and indexes behave. Practice SQL on PipeCode.

May 11, 202667 min read
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PostgreSQL SQL Cheat Sheet — Clause Order, Joins, Aggregates, Windows
De InterviewSql

PostgreSQL SQL Cheat Sheet — Clause Order, Joins, Aggregates, Windows

Gowtham Potureddi

PostgreSQL SQL cheat sheet for real queries — logical clause order from FROM through LIMIT, INNER/LEFT/RIGHT/FULL/SELF/CROSS joins with grain control, GROUP BY with HAVING and conditional aggregates, and window functions with ROW_NUMBER/RANK/DENSE_RANK/LAG/LEAD for ranking and running totals.

May 11, 202655 min read
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Data Lake Architecture for Data Engineering Interviews
De InterviewSql

Data Lake Architecture for Data Engineering Interviews

Gowtham Potureddi

Data lake architecture for data engineering interviews — bronze/silver/gold medallion zones, ingestion through metadata catalog into Spark and SQL compute, lake vs cloud warehouse vs lakehouse trade-offs with Iceberg/Delta/Hudi, and a five-step interview answer template covering grain, idempotency, lineage, and reconciliation.

May 11, 202663 min read
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Data Engineering Roadmap for Freshers (2026): A 13-Step Beginner's Guide from SQL to Your First Data Engineering Job
De InterviewSql

Data Engineering Roadmap for Freshers (2026): A 13-Step Beginner's Guide from SQL to Your First Data Engineering Job

Gowtham Potureddi

Data engineering roadmap for freshers (2026) — a 13-step beginner guide from SQL fundamentals through Python, databases, data warehousing, ETL/ELT, Apache Spark, Airflow orchestration, AWS cloud, data modeling, Kafka streaming, portfolio projects, Git, and SQL + Python + system-design interview prep, with worked examples and a 6 to 12-month timeline.

May 11, 202665 min read
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SQL Interview Questions for Data Engineering
De InterviewSql

SQL Interview Questions for Data Engineering

Gowtham Potureddi

SQL interview questions for data engineering — INNER vs LEFT JOIN with the IS NULL anti-join for orphan customers, GROUP BY with HAVING for duplicates and aggregate filters, ROW_NUMBER vs RANK vs DENSE_RANK for second-highest salary and top-N per group, and CTE composition with recursive CTEs and correlated subqueries, with worked examples and full traces.

May 10, 202649 min read
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COALESCE in SQL — First Non-NULL, LEFT JOIN Defaults, and Interview Patterns
De InterviewSql

COALESCE in SQL — First Non-NULL, LEFT JOIN Defaults, and Interview Patterns

Gowtham Potureddi

COALESCE in SQL deep-dive — left-to-right first-non-NULL evaluation, LEFT JOIN default values for analytics and BI, COALESCE vs CASE / ISNULL / NVL portability, and pitfalls (NULL semantics, type coercion, empty strings, NULLIF) with worked examples.

May 3, 202650 min read
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Facebook Data Engineering Interview Questions & Prep Guide
De InterviewSql

Facebook Data Engineering Interview Questions & Prep Guide

Gowtham Potureddi

Facebook (Meta) data engineering interview prep — Python array sum-formula and XOR for missing-number, array + math + bit + string parser for arithmetic formula evaluation, SQL EXISTS month-over-month MAU retention, and CTE + self-join for friend recommendations and post-hiatus aggregation, with worked examples.

May 3, 202645 min read
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Square Data Engineering Interview Questions & Prep Guide
De InterviewSql

Square Data Engineering Interview Questions & Prep Guide

Gowtham Potureddi

Square (Block) data engineering interview prep — SQL ranking + aggregation for top-N invoice senders, date-function cohort analysis for 30-day-post-signup activity, window functions (AVG OVER, ROW_NUMBER OVER) for monthly aggregates and duplicates, and payment-flow COUNT DISTINCT with status filters, with worked examples.

May 3, 202644 min read
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Snowflake Data Engineering Interview Questions & Prep Guide
De InterviewSql

Snowflake Data Engineering Interview Questions & Prep Guide

Gowtham Potureddi

Snowflake data engineering interview prep — Python array + set validation for SET card games, hash-table sliding-window for maximum substring occurrences, SQL window functions (LAG/LEAD/AVG OVER PARTITION BY), and Snowflake architecture (micro-partitions + clustering + Time Travel), with worked examples.

May 3, 202650 min read
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Robinhood Data Engineering Interview Questions & Prep Guide
De InterviewSql

Robinhood Data Engineering Interview Questions & Prep Guide

Gowtham Potureddi

Robinhood data engineering interview prep — Python hash-table dict counting for stock purchases, SQL inner join + GROUP BY for trade aggregations, window-function LAG for daily volume change, and HAVING-based threshold checks for notional limits, with worked examples.

May 3, 202649 min read
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Bloomberg Data Engineering Interview Questions
De InterviewSql

Bloomberg Data Engineering Interview Questions

Gowtham Potureddi

Crack the Bloomberg data engineering interview with worked Python two-pointer, abstract-class, and SQL window-function solutions, plus the DE process.

May 2, 202644 min read
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Rivian Data Engineering Interview Questions
De InterviewSql

Rivian Data Engineering Interview Questions

Gowtham Potureddi

Crack the Rivian data engineering interview with worked SQL aggregation, JOIN, and vanilla Python string-padding solutions, plus the full Rivian DE process.

May 2, 202644 min read
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Figma Data Engineering Interview Questions
De InterviewSql

Figma Data Engineering Interview Questions

Gowtham Potureddi

Crack the Figma data engineering interview with worked SQL window-function and Python string-parsing solutions, plus the full Figma DE process.

May 2, 202640 min read
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HackerRank Data Engineering Interview Questions: 7 SQL & PySpark Patterns to Master
De InterviewSql

HackerRank Data Engineering Interview Questions: 7 SQL & PySpark Patterns to Master

Gowtham Potureddi

ackerRank data engineering interview prep — JOINs, multi-table aggregation, GROUP BY/HAVING, window functions, CASE on hierarchies, scalar subqueries with strict output formatting, and PySpark dataframe transforms — with worked solutions, traces, and engine-portable patterns.

May 2, 202644 min read
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Cisco Data Engineering Interview Questions
De InterviewSql

Cisco Data Engineering Interview Questions

Gowtham Potureddi

Cisco data engineering interview prep — Python dict-comprehension key-value inversion, status-filter dicts, functools-wrapped decorators with perf_counter timing, and greedy comparator-sort for the maximum concatenated substring, with worked examples.

May 1, 202641 min read
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Intuit Data Engineering Interview Questions & Prep Guide
De InterviewSql

Intuit Data Engineering Interview Questions & Prep Guide

Gowtham Potureddi

Intuit data engineering interview prep — SQL window-function ranking over aggregates, JOIN + subquery for same-salary employees, SQL regex for numeric authorization codes, Python regex for the largest odd substring, and Python Counter for country-count rollups, with worked examples.

May 1, 202645 min read
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Shopify Data Engineering Interview Questions
De InterviewSql

Shopify Data Engineering Interview Questions

Gowtham Potureddi

Shopify data engineering interview prep — pure-SQL merchant analytics with monthly and daily session counts, days-to-first-session JOINs, 7-day rolling-average window functions, and UTM source extraction via regex, with worked examples.

May 1, 202646 min read
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PayPal Data Engineering Interview Questions
De InterviewSql

PayPal Data Engineering Interview Questions

Gowtham Potureddi

PayPal data engineering interview prep — Python list comprehensions over arrays, type-operation semantics, defaultdict aggregation for catering reports, set-intersection for recommendations, and bipartite graph validation for seating arrangements, with worked examples.

Apr 30, 202652 min read
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Hyper Data Engineering Interview Questions
De InterviewSql

Hyper Data Engineering Interview Questions

Gowtham Potureddi

Hyper data engineering interview prep — pure-SQL retail analytics with aggregation, monthly date_trunc revenue, multi-table joins, partitioned ROW_NUMBER for top-seller-per-state, HAVING completeness checks, and LAG-driven weekly growth tracking, with worked examples.

Apr 30, 202655 min read
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Lyft Data Engineering Interview Questions
De InterviewSql

Lyft Data Engineering Interview Questions

Gowtham Potureddi

Lyft data engineering interview prep — Python multi-stream queues, autocomplete hash tables, two-pointer array intersection, binary-search Nth-missing-integer, plus SQL time-series and consecutive-day window functions with worked examples.

Apr 30, 202663 min read
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Stripe Data Engineering Interview Questions
De InterviewSql

Stripe Data Engineering Interview Questions

Gowtham Potureddi

Stripe data engineering interview prep — Python tiered pricing with sort + greedy, transaction-fee aggregation, idempotent event apply, bounded producer/consumer queues, event-time watermarks, end-to-end ETL, and SQL JSONB key extraction with worked examples.

Apr 30, 202664 min read
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Oracle Data Engineering Interview Questions
De InterviewSql

Oracle Data Engineering Interview Questions

Gowtham Potureddi

Oracle data engineering interview prep—Python stacks, hash tables, JSON, SQL aggregation, self-joins, ranking, date functions with worked examples.

Apr 29, 202654 min read
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Salesforce Data Engineering Interview Questions
De InterviewSql

Salesforce Data Engineering Interview Questions

Gowtham Potureddi

Salesforce data engineering interview prep — SQL subqueries, retention cohorts, self-joins, window functions for MoM growth, aggregation, hash-table design, and Python closures with worked examples.

Apr 28, 202660 min read
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Atlassian Data Engineering Interview Questions
De InterviewSql

Atlassian Data Engineering Interview Questions

Gowtham Potureddi

Atlassian data engineering interview prep—SQL window functions, ranking, gaps-and-islands, moving averages, time-series, plus Python stacks and binary search.

Apr 28, 202646 min read
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Techpath Data Engineering Interview Questions
De InterviewSql

Techpath Data Engineering Interview Questions

Gowtham Potureddi

Techpath data engineering interview prep—Python fundamentals, queue simulation, hash-table set ops, conditionals, error handling, file I/O.

Apr 28, 202648 min read
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Walmart Data Engineering Interview Questions
De InterviewSql

Walmart Data Engineering Interview Questions

Gowtham Potureddi

Walmart data engineering interview prep—SQL string parsing, time-series rollups, anti-joins, Python hash tables, BFS pathfinding, and dynamic programming.

Apr 28, 202650 min read
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Databricks Data Engineering Interview Questions
De InterviewSql

Databricks Data Engineering Interview Questions

Gowtham Potureddi

Databricks data engineering interview prep—Python algorithms, sweep-line intervals, hash tables, binary search, bit manipulation, DP, Morris traversal.

Apr 28, 202653 min read
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Netflix Data Engineering Interview Questions & Prep
De InterviewSql

Netflix Data Engineering Interview Questions & Prep

Gowtham Potureddi

Netflix data engineering interview prep—SQL window functions, anti-join, set operations, Python sliding window, streaming, deque, ETL with checkpoints.

Apr 27, 202652 min read
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Google Data Engineering Interview Questions & Prep
De InterviewSql

Google Data Engineering Interview Questions & Prep

Gowtham Potureddi

Google data engineering interview prep—SQL self-joins, recursive CTEs, aggregation, Python hash maps, pandas, decorators, and file I/O with worked examples.

Apr 27, 202662 min read
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Microsoft Data Engineering Interview Questions & Prep
De InterviewSql

Microsoft Data Engineering Interview Questions & Prep

Gowtham Potureddi

Microsoft data engineering interview prep—SQL, Python, windows, ETL, and data modeling with worked examples; practice links in the article.

Apr 27, 202641 min read
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Airbnb Data Engineering Interview Questions & Prep
De InterviewSql

Airbnb Data Engineering Interview Questions & Prep

Gowtham Potureddi

Airbnb data engineering interview prep—SQL, joins, windows, sessionization, Python, and data modeling with worked examples and practice links.

Apr 26, 202643 min read
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Uber Data Engineering Interview Questions & Prep
De InterviewSql

Uber Data Engineering Interview Questions & Prep

Gowtham Potureddi

Uber data engineering interview questions: SQL, Python, modeling, pipelines—practice with Uber-tagged problems on PipeCode.

Apr 26, 202679 min read
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ByteDance Data Engineering Interview Questions
De InterviewSql

ByteDance Data Engineering Interview Questions

Gowtham Potureddi

ByteDance data engineering interview: SQL & Python patterns, sub-topics, original Q&A, and ByteDance company hub links on PipeCode.

Apr 25, 202648 min read
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Amazon Data Engineering Interview Questions & Prep
De InterviewSql

Amazon Data Engineering Interview Questions & Prep

Gowtham Potureddi

Amazon data engineering interview prep : SQL, joins, windows, dates, Python patterns, and dimensional modeling with worked examples

Apr 23, 202652 min read
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DoorDash Data Engineering Interview Questions & Prep
De InterviewSql

DoorDash Data Engineering Interview Questions & Prep

Gowtham Potureddi

DoorDash data engineering interview questions: SQL, Python, and modeling patterns with practice links to 63+ DoorDash-tagged problems on PipeCode.

Apr 23, 202648 min read
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Meta Data Engineering Interview Questions: Top Topics, Problems & Solutions
De InterviewSql

Meta Data Engineering Interview Questions: Top Topics, Problems & Solutions

Gowtham Potureddi

Meta data engineering interview questions: top SQL & Python topics from the Meta practice set, explained for beginners, with sample problems and solutions.

Apr 20, 202671 min read
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Data Engineering Interviews: 5 Python Skills You Need to Nail
De InterviewPython

Data Engineering Interviews: 5 Python Skills You Need to Nail

Gowtham Potureddi

Data engineering interviews test Python on ETL, files, and speed. Five skills: error handling, context managers, I/O, performance, batch vs stream.

Apr 16, 202617 min read
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SQL Interview Questions for Data Engineers: 30 Real Questions with Solutions (2026)
New Tags

SQL Interview Questions for Data Engineers: 30 Real Questions with Solutions (2026)

PipeCode Team

Master 30 real SQL interview questions asked at FAANG companies with full solutions. Practice DE SQL problems interactively.

Apr 14, 202625 min read
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Top 50 Data Engineering Interview Questions & Answers (2026 Guide)
New Tags

Top 50 Data Engineering Interview Questions & Answers (2026 Guide)

Harry Peter

Master your 2026 data engineering interview with 50 real questions from Meta, Amazon, Google and more.

Apr 1, 202620 min read
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The Only 5 Skills You Need to Become a Data Engineer in 2026

Nick

A definitive guide to the 5 core skills required for modern Data Engineering. Learn how Extraction, ETL, Warehousing, Delivery, and Orchestration fit together to build your career.

Mar 5, 202612 min read
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