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Data Engineering

Build reliable data pipelines. ETL, warehousing, data modeling, and the modern data stack.

Browse 19 tutorials → Other topics

Why learn Data Engineering?

  • One of the highest-demand roles in tech — companies drown in data they cannot use.

  • Bridges the gap between raw data sources and analytics/ML teams.

  • Pays well and offers clear career progression into staff/principal roles.

  • Combines software engineering rigor with data domain expertise.

What you can build with Data Engineering

ETL/ELT pipeline design Data warehouse and lakehouse architecture Data quality and governance Batch and stream processing Analytics engineering with dbt

Data Engineering tutorials

19 articles · page 1 of 1

Hand-written tutorials, ordered as a recommended learning path.

  1. 01 What Is Data Engineering? Understand what data engineers do, how they differ from data scientists, the core skills required, and why this role is one of the most in-demand in tech.
  2. 02 ETL vs ELT Pipelines Understand the difference between ETL and ELT pipeline patterns, when to use each, and how modern cloud warehouses changed the default choice.
  3. 03 Data Warehouse Concepts Learn the fundamentals of data warehousing — star schemas, snowflake schemas, fact tables, dimension tables, and slowly changing dimensions with examples.
  4. 04 Lakes vs Warehouses vs Lakehouses Understand the differences between data lakes, data warehouses, and the modern lakehouse architecture — when to use each and how they work together.
  5. 05 Batch vs Stream Processing Understand the difference between batch and stream processing, when to use each pattern, and how Lambda and Kappa architectures combine them.
  6. 06 Data Modeling for Analytics Learn Kimball, Inmon, and Data Vault modeling approaches for analytics — star schemas, normalized models, and modern patterns like the Activity Schema.
  7. 07 Data Quality & Governance Learn the six dimensions of data quality, how to implement quality checks with Great Expectations and dbt, and the pillars of data governance.
  8. 08 Tools Ecosystem A comprehensive guide to the modern data engineering tool landscape — ingestion, transformation, orchestration, storage, quality, and governance tools.
  9. 09 dbt Fundamentals Master dbt (data build tool): project structure, models, materializations, testing, Jinja templating, and how dbt became the standard for analytics engineering.
  10. 10 Pipeline Testing Learn the data testing pyramid, unit testing transformations, contract testing between stages, and how to build reliable CI/CD test suites for pipelines.
  11. 11 Pipeline CI/CD Build CI/CD workflows for data pipelines: lint SQL, validate DAGs, run tests, deploy dbt models, and manage dev/staging/prod environments.
  12. 12 Dimensional Modeling Master Kimball dimensional modeling: fact table types, advanced dimension techniques, the bus matrix, grain decisions, and a complete e-commerce example.
  13. 13 Data Observability Learn the five pillars of data observability, anomaly detection, lineage tracking, incident response, and tools like Elementary, Monte Carlo, and Soda.
  14. 14 Spark Fundamentals Learn Apache Spark: RDDs, DataFrames, SparkSQL, the execution model, PySpark basics, platform comparisons, and essential performance optimization tips.
  15. 15 Interview Prep Prepare for data engineering interviews: SQL deep dives, Python coding, system design, data modeling, behavioral questions, and take-home project tips.
  16. 16 Real-Time Streaming Learn real-time streaming architectures like Lambda, Kappa, CDC, and event sourcing. Understand when to choose streaming over batch processing.
  17. 17 Partitioning Strategies Master hash, range, and list partitioning strategies. Learn to choose partition keys, avoid hot partitions, and scale your data systems.
  18. 18 Orchestration: Dagster Learn Dagster's software-defined assets, ops, jobs, schedules, and sensors. Includes a practical comparison with Apache Airflow.
  19. 19 Apache Iceberg Understand Apache Iceberg's architecture, schema evolution, time travel, and hidden partitioning. Learn why table formats matter for data lakes.