Why AI capacity stopped behaving like elastic compute and started depending on physical infrastructure, power, and place.

We covered ten events this spring, from Hannover Messe to AWS Summit London. Three patterns kept showing up, and they say a lot about where operational analytics is headed.

How Danish startup Float hit 99.3% compression on 1Hz smart meter data with Tiger Data, powering appliance-level energy analytics for hundreds of homes.

Stale statistics on small Postgres metadata tables can silently throttle your largest queries. Learn to spot it with EXPLAIN and fix it—no schema migration.

Why operating TimescaleDB for mission-critical applications becomes a sustained platform engineering investment. Written by the team that builds TimescaleDB and operates Tiger Cloud across thousands of production deployments.

At scale, Postgres ORDER BY on time-series data spills to disk and slows queries. See how time_bucket and continuous aggregates cut a 4s query to 9ms.

A skewed partition key can route 80% of your writes to one partition. Learn how to diagnose PostgreSQL partition hot spots and fix them at the schema level.

Great models aren't enough for Physical AI. Real deployments are gated by regulation, safety, operations, and the data your machines produce.

Postgres handles 90% of workloads well. Here's how to tell if yours is in the 10% — and what the diagnostic query that confirms it looks like.

A UNS puts your historian downstream of continuous tag churn. Learn the schema that survives renames, reorgs, and retirements without rewriting history.

Up to 160x more efficient UPDATE/DELETE, 30% to 2x faster vectorized execution, and skip unnecessary decompression + compression. Here's what's new in TimescaleDB 2.27.

Learn why PostgreSQL reads 16x more data than your queries need, and how a hybrid row-columnar storage layout eliminates the bottleneck without changing your SQL.

The cost of the Optimization Treadmill doesn't show up on the database bill. It shows up on the engineering calendar. And it compounds in ways that are easy to miss until someone actually adds it up.

Learn how to use Claude Code Agent Teams and TimescaleDB to build a unified plant-floor data pipeline — one governed table, three protocols, zero middleware.

See how open source TimescaleDB powers a year of hourly building energy data in a single heat map view — rendered in under a second across 38 live sites.

Vector search becomes an index design problem as your data grows. Here's how to make the right call without leaving Postgres.

PostgreSQL and your OS cache the same data twice. Learn how double buffering degrades performance at scale and how the 25% shared_buffers rule fixes it.

Giant `IN` clauses inflate PostgreSQL planning time and spike p99 latency. Learn how `ANY(ARRAY[])` cuts the hidden planning tax and keeps your app fast at scale.

The true cost of Postgres optimization isn't the cloud bill. It's 12-16 engineer-weeks per year that never show up on a budget report.

Joins on billion-row Postgres tables crush real-time dashboards. Flatten your schema to cut shared buffer hits 70–90% and restore dashboard speed.

There's a category of Postgres performance issue that no amount of tuning will fix. Here's how to tell if you're already in it.

Your append-only table doesn't need autovacuum. It runs anyway. Here's what that actually costs your team.

Working SQL examples for replacing Elasticsearch, Pinecone, InfluxDB, Redis, and more, using Postgres extensions you can enable today.

For IIoT engineers: a personal take on integrating HiveMQ and TimescaleDB. One Postgres connector, no custom pipeline, no extra database to run.

Random UUIDv4 keys cause B-tree page splits and bloat indexes up to 35%. See how UUIDv7's time-ordered IDs keep Postgres ingestion fast at scale.

Optimization problems stay fixed. Architectural ones come back. A framework for knowing which you're dealing with before you've spent months on the wrong fix.

Normalized schemas create latency at scale. This guide shows when to flatten your tables and use columnar compression to cut join overhead and reclaim query speed.

How the Embodied Carbon Observatory uses TimescaleDB to cut queries from 6s to under 100ms, separating real decarbonization from grid improvement.

TimescaleDB 2.26 delivers 3.5x faster time_bucket() aggregations, 70x faster summary queries, and 2x faster multi-column lookups. No query rewrites needed.

Every 1 KB insert in Postgres becomes ~2.5 KB of committed I/O before it's done. Here's where the multiplier comes from, and where the tuning knobs run out.