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.

A practical guide to data lakehouse architecture in 2026: core components, medallion pattern, implementation options, governance, and real cost modeling.

An honest review of Snowflake Cortex AI after 3 months in production: LLM functions, vector search, ML forecasting, cost analysis, and where it falls short.

A data engineer's honest exploration of Rust in the data ecosystem: Polars, DataFusion, Arrow-rs, Delta-rs, PyO3, performance benchmarks, and learning curve.

A hands-on guide to integrating LLMs into ETL pipelines for data classification, cleaning, and enrichment with Python code, cost analysis, and fallback patterns.

The Modern Data Stack promised simplicity but delivered cost explosions and tool sprawl. Here's what actually replaced it in 2026 and why consolidated wins.