Fuad Adebisi·1d agoLookback Windows for Accurate Incremental ModelsWhy processing only new records can quietly create incomplete data, and how to choose a lookback window using evidence instead of…
InCodeXbyShree Kavya·1d agoYou Finished the 90-Day Plan. What Should a New Data Engineer Learn Next?Four specialization paths, a simple way to choose one, and a practical plan for months 4 to 12 (and your first 90 days on the job)A response icon1A response icon1
Alejandro Román Román·1d agoThe Method Should Survive Its AuthorWhat happens when advanced statistics become reusable knowledge.
Nicolas Pogeant·1d agoWhy select * Is the Most Expensive Query in Your WarehouseUnderstanding why a design that looks wasteful on paper is cheap in practice tells you exactly what will make it expensive
InAmfeltbyMark Dumay·4d agoData Tools Are Getting Better. Data Teams Are Still Stuck.I write about what we’re learning while building Amfelt, a data platform driven by shared metadata. The other posts are on amfelt.com.
Marco Ciavarella·5d agoDex helped my agent cut Snowflake costs by 66% (conservative estimate)The more idle time you cut, the more you save.A response icon1A response icon1
Pascal Parlindungan Gultom·5d ago1. Stop Copy-Pasting SQL in Dataform: A Practical Guide to Reusable FunctionsSix reusable SQL patterns for incremental models, and how to change a shared macro without breaking production.
Jasoniel Santana·6d agoBuilding Reliable Data Pipelines: Data Quality, Validation, and Monitoring with PySparkA practical guide to preventing bad data from becoming bad business decisions.
Jasoniel Santana·6d agoFrom Raw Data to Business Insights: Building a Medallion Architecture with DatabricksAn end-to-end guide to transforming raw e-commerce data into reliable, business-ready analytics using PySpark, Delta Lake, and SQL.
Nicolas Pogeant·6d agodbt_utils, dbt_expectations, and Writing Your Own TestsPackage tests for the common cases, custom tests for your own rules, and severity to keep the noise down
