From Google- and Meta-Like CSVs to a Trusted Schema: Building Fail-Closed Python Adapters
A hands-on Python ingestion tutorial: map simulated ad-platform CSVs, normalize micro-USD with Decimal, reject duplicates, write atomically, and test in CI.
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A hands-on Python ingestion tutorial: map simulated ad-platform CSVs, normalize micro-USD with Decimal, reject duplicates, write atomically, and test in CI.

This article was first published on the Bruin blog. Quick answer: CDC stands for change data...

Data arrives as files. A vendor drops a CSV in a container, an upstream system exports...
Notes from the second course: source systems, batch vs streaming ingestion, ETL vs ELT, and orchestrating pipelines with Airflow on AWS.

Migrating Legacy ETL Pipelines to Palantir Foundry: Incremental Processing, Schema...
What if you could build complete, production-grade ETL pipelines in Pure SQL… without Jinja...
Imagine your orders are stored in Amazon Aurora PostgreSQL, but customer information lives in...
If you upgrade a DAG like this from Airflow 2 to Airflow 3 without changing a line, it can start...
SSIS is not dead. It has stopped moving. A lot of production ETL still lives in SQL Server...
Introduction to Neptunus v1.17.0 and Ollama Integration Neptunus v1.17.0 marks a...

Legacy ETL replacements rarely fail because the new platform can't run the transformations. They fail...
If you've ever inherited a folder of .dtsx files with zero documentation, you know the drill. Before...