From the Blog
What we have learned building and tuning data platforms — written for the engineers and leads who have to run them.
LLMs Inside the Pipeline: Turning Tickets, Contracts, and Call Notes into Tables
The most practical AI use case for a data team — batch LLM extraction that turns unstructured text into typed Silver tables. Architecture, validation, the human review loop, and the cost rules that keep it safe.
Read articleNL-to-SQL That Gives the Right Number: Architecture and the Semantic Layer
Why 'chat with your warehouse' returns wrong numbers, and the architecture that fixes it — gold tables only, a versioned semantic layer with metric definitions and verified queries, and an evaluation loop in CI.
Read articleRAG in Production: Treat Documents Like a Data Pipeline
A reference architecture for a RAG system that stays correct after launch — incremental document ingestion, structure-aware chunking, hybrid retrieval, permission filtering, and the monitoring that catches rot.
Read articleHow to Review a Data Lakehouse PoC Before the Board Funds It
An independent review of a lakehouse proof of concept: what to check, how to score it, and what the steering committee should get at the end.
Read articleAWS Lakehouse Checklist: Where Iceberg, Athena, Glue, and MWAA Usually Break
The common failure points we look for when we review an AWS lakehouse built on S3, Apache Iceberg, Athena, Glue, MWAA, Spark, and Lambda.
Read articleServerless Data Lake on AWS: Lessons from a Large Telecom
How a telecom ran a config-driven S3 data lake where every Spark job creates its own EMR cluster and then stops it — and what we would keep today.
Read articleThe Hidden Cost of an Open-Source Data Stack
Two real platforms — Athena with dbt and Meltano at a SaaS company, Iceberg with Trino on Kubernetes at a FinTech — and what open source really costs a small data team.
Read articleData Observability When a Late File Costs Real Money
A reverse ETL pipeline delivered location data to a paying client every 30 minutes. Missing the SLA meant a penalty. Here is how 90% of the work became observability.
Read articleOracle to AWS Redshift in 8 Months with 3 People
A realistic timeline and team size for moving a legacy Oracle data warehouse to AWS — and how the platform then grew into a data lake with EMR, Spark, and Athena.
Read articleAI Data Engineering: Why Your AI Project Is Really a Data Project
What AI data engineering means in practice — AI-ready data layers, chunking and embeddings, semantic layers for NL-to-SQL, evaluation sets, and the failures that kill AI pilots.
Read articleDatabricks Lakehouse Architecture: What Actually Matters in Production
A practical guide to medallion architecture on Databricks — bronze, silver, gold layers, Unity Catalog governance, job compute, and the mistakes that cost the most.
Read articleHow to Cut Your Snowflake Bill Without Slowing Anyone Down
A practical checklist for Snowflake cost optimization: warehouse sizing, auto-suspend, incremental dbt models, clustering, and cost attribution that actually holds up.
Read articleLet's talk about your data platform
Tell us what you are working on. We reply within one business day, and we will tell you plainly if we are not the right fit.