Working with time-series data, building real-time apps, or scaling PostgreSQL for AI workloads? 🚀 This latest newsletter from TigerData (creators of Timescale (now TigerData)) is packed with developer gold: lnkd.in/gmBupqka 🔥 Performance that matters: 9,000x faster high-performance

RTABench was designed to reflect real-world analytics workloads. Think: ➡️ Highly selective queries ➡️ Complex joins ➡️ JSONB expressions ➡️ Pre-aggregations and filters you'd actually write If you want to understand how Postgres handles real analytical queries—not synthetic

My free course about fast analytics with Timescale (now TigerData) is near - after a long time. The design is almost finished and I am beautifying the first two modules now for a release at start of September. So happy the first things will finally be released 😃

“Queries dropped from 30s to instant.” — A very relieved ex-MongoDB user at Edeva They didn’t change their dashboard. They just changed their database. TigerData (creators of TimescaleDB): Built for time-series workloads that actually scale. 👉 Hear it straight from them:

SCHEMA CHAOS No schema on write. Surprise errors on read. MongoDB makes it easy to start. Then betrays you when you scale. For time-series, that’s a dealbreaker. TigerData (creators of TimescaleDB) Built for consistency you can trust. 👉 Try TigerData:

Axpo, Switzerland’s largest power producer and a global leader in energy trading and renewable marketing, uses TigerData to ingest 150 million time-series rows daily from over 20 power plant systems, enabling fast, efficient querying and analytics. Learn how in this video.

Need to add one field? Repull your entire historical time-series. MongoDB for time-series: That’s the “update” path. TigerData (creators of TimescaleDB): Built for painless backfills and evolving schemas. 👉 Try TigerData: console.cloud.timescale.com/signup 👉 Explore the benchmark

Add a node → Reshard everything. Spin up a test cluster → Surprise bills. MongoDB for time-series: You scale it, you pay for it. Built to punish growth. TigerData (creators of TimescaleDB): Built to scale—without the gotchas. 👉 Try TigerData: tsdb.co/w98v6s78 👉

“Postgres is a very viable option.” — MongoDB CEO, June 2025 earnings call Even Mongo knows: When you need scale, trust, and flexibility for time-series workloads… Postgres wins. TigerData (creators of TimescaleDB) Postgres, built for time-series and AI. 👉 Try TigerData:

1/4 After my AI search piece hit 1M+ impressions, everyone asked: what to use? Similarity != Relevance Sometimes you need similarity (topics) Sometimes you need relevance (exact IDs) Sometimes you need both - hybrid search

“We tried to replicate what TimescaleDB does—behind the scenes—in MongoDB. It sort of worked… but also didn’t.”  — Lead engineer @ Evergen They wired together Kafka, daily backfills, and one-day buckets. All just to make MongoDB usable for time-series. Then they switched to

Intrigued by Zed's announcement of DeltaDB, which mentions using CRDTs to synchronize code changes. The details are thin, but it touches on a deep and fascinating frontier. For decades we’ve searched for better ways to manage concurrency. — Databases solved it with