How to Evaluate a Data Platform Without Lying to Yourself
A practical, evidence-based framework for evaluating data platforms against your team’s workloads, constraints, operating model, and total cost.
A practical, evidence-based framework for evaluating data platforms against your team’s workloads, constraints, operating model, and total cost.
A practical framework for choosing whether to build, adopt open source, or buy data and AI technology based on strategy, cost, control, and operational capacity.
Modern DataTools uses page caching, targeted invalidation, and shared data caches to reduce repeated work without allowing important information to remain outdated - in the article we cover our key lessons for these mechanisms.
Practical lessons from using coding agents on a growing codebase: repository rules, changelogs, scripts, testing, and context.
The vector database gold rush is in full swing. But for most teams, pgvector is all you need. Here's how to decide.
Three AI agents worth building on top of a modern data stack: analytics, incident triage, and internal knowledge assistants.
How to add AI on top of a modern data stack: the practical layers, tools, and guardrails for building useful agents, retrieval, and internal copilots.
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.
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.
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.