intermediateSystem Design⏱ 4 hours📚 7 modulesPremium

ETL System Design for Data Engineering Interviews

Learn how to design scalable ETL systems from scratch — batch pipelines, streaming architectures, and the exact system design frameworks top companies test in Data Engineering interviews.

About This Course

System design is the round that separates senior offers from senior rejections. And most DE candidates walk in with zero preparation. "Design a real-time analytics pipeline for 10M events/second." "How would you build a batch ETL that processes 5TB daily?" "Walk me through data quality, reliability, and scaling." If your answer is "uh… Spark?", this course is for you. We start with the fundamentals — batch vs streaming, when to use what — then go deep into every major tool: Apache Spark, Kafka, Flink, Spark Streaming, Airflow, AWS Glue, S3/Parquet, Redshift, BigQuery, Snowflake, and Redis. You'll learn not just what each tool does, but when interviewers expect you to choose one over another. Then we teach you the design framework — how to approach any system design question, handle data quality and reliability, and design for scale. Finally, you'll watch 2 full mock system design interviews to see the framework applied under pressure. 7 chapters. 20+ videos. Quiz after every concept. 2 mock interviews. Built for the system design round.

What You'll Learn

✓Design Batch & Streaming Architectures from Requirements — Given a business problem, decide between batch, streaming, or lambda architecture and justify your choice — the first question in every system design round.

✓Choose the Right Tool for Every Layer of the Pipeline — Spark vs Flink, Kafka vs Kinesis, Airflow vs Step Functions, S3 vs Redshift — the technology selection questions interviewers use to test depth.

✓Architect End-to-End ETL Pipelines at Scale — Design multi-stage pipelines with ingestion, transformation, storage, and serving layers — the full architecture diagram interviewers expect.

✓Apply the System Design Framework to Any Problem — Learn the structured approach to scoping, designing, and defending your architecture — the meta-skill that turns a rambling answer into a strong one.

✓Handle Data Quality, Reliability & Performance — Schema validation, data contracts, idempotency, exactly-once semantics, partitioning, and scaling — the production concerns that separate senior from mid-level answers.

✓Walk Through Mock System Design Interviews Confidently — Watch 2 full mock interviews and internalize the pacing, depth, and communication patterns that get offers.

Course Curriculum (7 Modules)

System Design Fundamentals — What Interviewers Expect

What system design is, how interviewers evaluate it, and the mental framework for approaching any data architecture problem — the foundation for every question in this course.

2 lessons

Intro System Design

video6m

Intro to System Design Quiz

quiz3m

Batch Architecture — When & How to Design Batch Pipelines

Batch processing fundamentals — when to use batch, the 5-layer architecture (sources → ingestion → processing → storage → orchestration), and the batch vs streaming tradeoffs interviewers test.

2 lessons

Batch Systems

video6m

Batch Systems Quiz

quiz3m

Streaming Architecture — Real-Time Pipelines & Event-Driven Design

Real-time streaming fundamentals — event-driven architectures, stream processing patterns, and when interviewers expect streaming over batch.

2 lessons

Real time Streaming Systems

video6m

Real-Time Streaming Systems Quiz

quiz3m

Batch Stack Deep Dive — Spark, Airflow, Glue, Data Lakes & Warehouses

Deep dives into Apache Spark (architecture, execution model, when to use), AWS Glue/EMR/Databricks (platform comparison & tradeoffs), Airflow (orchestration & DAGs), S3/Parquet (data lakes), and Redshift/BigQuery/Snowflake (data warehouses).

13 lessons

Apache Spark — Architecture & Distributed Processing

video2m

Apache Spark — Execution Model, DAG & Shuffles

video5m

Apache Spark — When to Use & Batch Pipeline Patterns

video4m

Apache Spark Quiz

quiz3m

AWS Glue, EMR & Databricks — Managed Spark Platforms

video6m

AWS Glue, EMR & Databricks — Comparison & Tradeoffs

video4m

AWS Glue Quiz

quiz3m

Apache Airflow

video10m

Apache Airflow Quiz

quiz3m

Data Lake Storage (S3 + Parquet)

video10m

Data Lake Storage (S3 + Parquet) Quiz

quiz3m

Data Warehouse (Redshift / BigQuery / Snowflake)

video8m

Data Warehouse (Redshift / BigQuery / Snowflake) Quiz

quiz3m

Streaming Stack Deep Dive — Kafka, Flink, Spark Streaming & Redis

Deep dives into Apache Kafka (architecture, partitions, ordering), Apache Flink (stateful streaming & exactly-once), Spark Streaming (micro-batch), and Redis (in-memory serving & caching).

11 lessons

Apache Kafka — Introduction & Core Concepts

video3m

Apache Kafka — Topics, Partitions & Message Flow

video4m

Apache Kafka — Features, Use Cases & Production Patterns

video3m

Apache Kafka — Ordering, Offsets & Delivery Guarantees

video3m

Apache Kafka Quiz

quiz3m

Apache Flink

video7m

Apache Flink Quiz

quiz3m

Apache Spark Streaming

video6m

Apache Spark Streaming Quiz

quiz3m

Redis

video8m

Redis Quiz

quiz3m

System Design Framework — Quality, Reliability & Scaling

The structured 6-step approach to system design interviews — scoping, requirements, architecture, tradeoffs — plus data quality, security, reliability, and scaling patterns.

6 lessons

Approach to System Design

video7m

Approach to System Design Quiz

quiz3m

Data Quality, Security & Reliability

video4m

Data Quality, Security & Reliability Quiz

quiz3m

Scaling & Performance

video6m

Scaling & Performance Quiz

quiz3m

Mock Interviews — Full System Design Walkthroughs

Two full-length mock system design interviews — one streaming (real-time analytics), one batch (daily metrics pipeline) — watch the framework applied under pressure with detailed commentary.

4 lessons

Mock Interview — Real-Time Streaming Analytics Pipeline

video14m

Mock System Design Interview 1 Quiz

quiz3m

Mock Interview — Daily Batch Analytics System

video15m

Mock System Design Interview 2 Quiz

quiz3m

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