Data Engineering
Data Engineering on AI-ML Companion: Moving data reliably - modelling, pipelines, and scale. 22 interactive modules with live visualizations, quizzes, and hands-on Python coding.
Start free: What a Data Engineer Actually Does is fully open to everyone, no account required. The other 21 modules are part of AI-ML Companion Premium; every title and summary is listed below so you can see exactly what the track covers before deciding.
Modules in this track
- What a Data Engineer Actually Does (free) - The job, the stack, and who you serve
- OLTP vs OLAP (premium) - Why one database cannot do both jobs
- Batch vs Streaming (premium) - Latency you need against complexity you pay
- File Formats (premium) - Why Parquet won, and how teams still make it slow
- Lakes, Lakehouses and Table Formats (premium) - What a table format adds to a pile of files
- Dimensional Modelling (premium) - Facts, dimensions, and why the star schema persists
- Slowly Changing Dimensions (premium) - Keeping history when an attribute changes
- Normalisation Trade-offs (premium) - When redundancy is the correct answer
- ETL vs ELT (premium) - Why transformation moved to after the load
- Cloud Warehouses and the Bill (premium) - Separated compute, per-second billing, query cost
- Orchestration (premium) - DAGs, schedules, backfills, and what runs when
- Idempotency and Retries (premium) - Making a job safe to run twice
- Change Data Capture (premium) - Reading the database log instead of the table
- dbt and Analytics Engineering (premium) - Version-controlled SQL that tests itself
- Project: Model a Star Schema (premium) - Raw CSVs to tested dbt and DuckDB tables
- Spark Basics (premium) - Distributed processing, minus the folklore
- Kafka Basics (premium) - Topics, partitions, and the log as source of truth
- Project: Replay the Log (premium) - Build an idempotent sink that survives a rewind
- Data Quality and Contracts (premium) - Catching bad data before your users do
- Access, Privacy and Lineage (premium) - Who can see it, how it gets deleted, and where it came from
- Data Pipelines for AI (premium) - Chunking, embedding and vector sinks are still ETL
- Capstone: Build a Warehouse (premium) - Ingest, model, orchestrate and test a real pipeline