Data Engineering & Big Data Pipelines (Apache Spark & Snowflake)
Build the pipelines that power analytics and ML at scale. Ingest and transform terabytes with Apache Spark, design a Snowflake warehouse, and orchestrate batch and streaming ELT with data-quality gates and cost control. Modelled on the modern data stack and the failures (skew, small files, runaway compute) that break it in production.
What You'll Learn
What's Included
Interactive learning path with AI-generated lessons read aloud by your personal tutor
Step-by-step lab instructions with AI guidance — run in your own IDE or sandbox.
AI interviewer asks tailored questions, gives real-time feedback and a score report
MCQ questions (theory, practical, code) to test your knowledge and earn a badge
Compete head-to-head against AI on real production problems — judged on correctness, performance, scalability, code quality, and edge cases
Course Syllabus
Module 1: Data Engineering & Spark Foundations
- •The modern data stack: sources, lake, warehouse, marts
- •Spark architecture: driver, executors, partitions, shuffle
- •DataFrames, transformations vs actions, lazy evaluation
- •Reading/writing Parquet, JSON, and columnar formats
- •Partitioning and file layout for read performance
Module 2: Scalable Transformations with Spark
- •Joins at scale: broadcast vs shuffle joins
- •Diagnosing and fixing data skew (salting)
- •Window functions and aggregations on big data
- •Caching, persistence, and avoiding the small-files problem
- •Reading the Spark UI to debug memory spill and stages
Module 3: The Lakehouse & Streaming
- •Delta Lake / ACID tables on a data lake
- •Incremental loads, upserts (MERGE), and time travel
- •Structured Streaming: sources, sinks, checkpoints
- •Exactly-once semantics and watermarking for late data
- •Batch vs streaming trade-offs and the Lambda/Kappa debate
Module 4: Snowflake & Warehouse Modelling
- •Snowflake architecture: storage, virtual warehouses, cloud services
- •Loading data: stages, COPY, and Snowpipe
- •ELT modelling: staging → intermediate → marts
- •Performance: clustering, pruning, and result caching
- •Cost control: warehouse sizing, auto-suspend, credits
Module 5: Orchestration, Quality & Production
- •Orchestrating pipelines as DAGs (Airflow-style)
- •Idempotency, backfills, and retry semantics
- •Data quality tests and pipeline observability
- •Schema evolution and contract enforcement
- •Capstone: a monitored batch + streaming production pipeline
You're all set
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