Data/AI Engineering

Real numbers from running 8 AI applications on local hardware vs cloud APIs. Spoiler: on-premise hit breakeven in 3 months and saves us $2,800/month.

A senior data engineer's hands-on comparison of Claude Code, Cursor, and GitHub Copilot for SQL, dbt, Airflow, and pipeline work with real prompts and productivity metrics.

After 6 months running MotherDuck in production, we cut our Snowflake bill from $8K to $400/month. Here's our honest review, migration playbook, and decision framework.

Honest benchmarks comparing Pinecone, Weaviate, Qdrant, Milvus, pgvector, and Chroma with real latency numbers, recall scores, and a decision framework for production RAG systems.

AI agents are automating data pipeline maintenance in 2026. Learn how we built pipeline repair, schema migration, and data quality agents that cut incident response from 45 minutes to 3.

A hands-on guide to building MCP servers that connect Claude and LLMs to your databases, dbt catalogs, and Airflow pipelines. Complete Python code included.

A dbt power user's honest review of SQLMesh after six months. Virtual environments, plan/apply workflow, and why it might be the best dbt alternative in 2026.

A practical guide to deploying open-source LLMs in production. GPU requirements, vLLM setup, cost comparison with GPT-4, and when self-hosting actually pays off.

Learn how to build fast, memory-efficient ETL pipelines with Polars. Covers lazy evaluation, streaming, Airflow integration, and when Spark is still better.

An honest evaluation of GPU databases like HeavyDB, Kinetica, and SQream plus RAPIDS cuDF for GPU analytics. Benchmarks, costs, and when the ROI works.

A deep dive into Apache Arrow, Parquet internals, Arrow Flight, and emerging formats like Lance and Vortex - with Python examples and benchmarks.

A practical guide to ML-specific CI/CD pipelines with data validation, automated evaluation gates, canary deployments, and rollback strategies.