Everything we have written about data engineering.
What dbt does, how models, tests and docs work, a simple model example you can follow, what changed with dbt v2 and the Fivetran merger, and how to start.
A practical roadmap to become a data engineer: which skills to learn in order, the cloud certifications worth knowing, portfolio projects that prove you can build pipelines and common paths in.
A plain-English comparison of batch and stream processing, the real costs of going real-time, a decision checklist and the middle-ground options most teams overlook.
A practical guide to data pipelines: the stages from ingestion to delivery, batch vs streaming, a real business example and the failure-handling habits that keep dashboards trustworthy.
ETL and ELT explained with simple flow diagrams, a side-by-side comparison, why cloud warehouses pushed teams to ELT, and when ETL is still the better choice.
A side-by-side comparison of data lakes and data warehouses covering structure, users, cost and performance, plus when a lakehouse makes sense and how to choose.
A neutral comparison of Snowflake and Databricks covering workloads, skills, pricing structure and lock-in, with a decision checklist for choosing the right data platform.
What a data lakehouse is, how open table formats like Delta Lake and Iceberg combine lake flexibility with warehouse reliability, and how to tell whether your business needs one.
What a data warehouse is, how it works, what cloud warehouses cost, and 7 signs a growing business is ready for one, explained with a real-world style example.