Egor Burlakov
••6 min readData Tool Signals: October 2026 — AI Got Cheaper, Agent Projects Sped Up
Four weeks of vendor pricing changes, open-source activity and search demand, with a check on what September's signals got right.
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Posts about data engineering practices, tools, and workflows
Four weeks of vendor pricing changes, open-source activity and search demand, with a check on what September's signals got right.
A September read on vendor price changes, pricing-led searches, and Postgres container growth.
Everyone asks which warehouse to buy. In one project, the right answer was none.
The stack recommender now ranks on how well a product fits your requirements rather than how popular it is, and prices a stack on the edition you would actually buy. Plus alternatives that really compete, comparisons that answer a real question, and a reordered profile page.
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A July read on developer packages, mature infrastructure scale, and the reader attention worth watching.
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The modern data stack promised simplicity, but in practice it often feels like IKEA furniture. Here’s how to build one that actually works, layer by layer.
Everyone's obsessed with AI models. After building a 500-tool data directory, we learned the model is 10% of the work. The other 90% is data collection, validation, and quality — and that's the real moat.
Save 84% on your data stack with open-source alternatives. ClickHouse instead of Snowflake, Airbyte instead of Fivetran, Metabase instead of Looker — with real cost comparisons.
A three-layer playbook — lifecycle policies, usage-based cleanup, and team rituals — to stop warehouse storage from compounding silently.
After scoring 500+ data tools on a 100-point framework, clear patterns emerge. Here are the ten that separate great tools from forgettable ones.
ETL transforms data before loading; ELT loads first and transforms in the warehouse. Learn when to use each approach with real examples, tool comparisons, and a decision framework.
A comprehensive guide to every layer of the modern data stack — ingestion, warehousing, transformation, orchestration, BI, data quality, reverse ETL, and streaming — with real tool recommendations and pricing.
Data pipelines pass all tests but silently lose millions in revenue. Discover Automated Data Tests (ADT)—lightweight checks that catch join drops, sum errors, and aggregation glitches across billions of rows. Python and SQL solutions coming next.