766 tutorials · free to read

A working handbook for data and software engineering.

Hands-on tutorials by Alex Merced on Apache Iceberg, the data lakehouse, pipelines, agentic AI, and the languages and tools that hold it all together.

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  1. 01Data Quality Is a Pipeline Problem, Not a Dashboard ProblemWhen an analyst finds null values in a revenue column, the typical response is to add a calculated field in the BI tool. That fix doesn't fix anything.
  2. 02Schema Evolution Without Breaking ConsumersA source team renames a column from user_id to customer_id. Twelve hours later, five dashboards show blank values, two ML pipelines fail, and the data engineering...
  3. 03Idempotent Pipelines: Build Once, Run Safely ForeverA pipeline runs, processes 100,000 records, and loads them into the target table. Then it fails on a downstream step. The orchestrator retries the entire job...
  4. 04Batch vs. Streaming: Choose the Right Processing ModelWe need real-time data. This is one of the most expensive sentences in data engineering - because it's rarely true, and implementing it when it's not needed...
  5. 05Partition and Organize Data for PerformanceA table with 500 million rows takes 45 seconds to query. After partitioning it by date, the same query : filtering on a single day, returns in 2 seconds...
  6. 06Testing Data Pipelines: What to Validate and WhenAsk an application developer how they test their code and they'll describe unit tests, integration tests, CI/CD pipelines, and coverage metrics. Ask a data e...