pg_vectorsearch is a new open-source Postgres extension. Its PRISM index serves 100M vectors in 6 ms on 4x less RAM than HNSW, with builds in under 2 hours.

Stop waiting for the dashboard to get slow. Record latency against row count, fit the curve, and get a date when your query breaches its target.

Five refresh policy shapes for solar and wind SCADA: frequent, daily, hierarchical, retention-aware, and manual. Pick the one that fits each job.

Reshape a wide oilfield SCADA table into a narrow hypertable plus tag metadata, in place, with dashboards running and history left where it is.

One Postgres table with two indexes beat every published RAG pipeline on MuSiQue. Every architectural addition is a bet the model stays weak.

No fact extraction, no graphs, no memory manager. One Postgres table and three search tools scored F1 0.665 on LoCoMo.

41 experiments in 6 days took LoCoMo memory from F1 0.392 to 0.666. Claude wrote the code; the human caught the metrics that were lying.

A 20-byte sensor reading costs 95.6 on disk. Measure bytes per row, project 631 billion rows, and test your ingest ceiling before the schema review.

Composite indexes charge every insert and pay back only some queries. Four steps to price both sides and audit the indexes you already run.

Meshtastic Metrics Exporter reduced 500K+ Prometheus metric series to one Postgres database: consolidated eight queries into one with Tiger Data for 10,000 mesh radio nodes.

PostgreSQL write amplification explained: why indexes multiply WAL volume, how to measure it on your tables, and four ways to reduce it without schema changes.

Giving an AI agent unrestricted database access isn't safe. Scoped, read-only, database-enforced access can be. See the framework and guardrails.

Tiger Cloud on Azure vs self-hosting TimescaleDB: skip-scan, compression, continuous aggregates, tiered storage, private endpoints, and cross-region backups—fully managed.

PostgreSQL table bloat: detect it before it kills performance. Measure heap and index density, set autovacuum thresholds, and alert on dead tuple ratios and vacuum lag.

Why Postgres wins in the AI era: 40 years of reliability, extensibility, and an open ecosystem. Agents need trusted foundations, not fragile architectures.

How Companion.energy optimized real-time energy data: 25x faster queries, 43.5x compression, continuous aggregates, and unified Tiger Cloud architecture.

Cardinality is a consequence of how many dimensions you index your readings by. Here is where the curve bends, measured, and what you can do about it without leaving Postgres. Someone adds a firmware_ver column to the sensor table. It is one line of DDL, and it clears review in a minute. Two weeks l...

TimescaleDB's DeferredChunkAppend keeps planning time constant for ORDER BY LIMIT queries, no matter how many chunks a hypertable has.

The bottleneck isn't the brain. It's dexterity, reliability, and data, which is why purpose-built robots are winning first.

How to tell if your slow Postgres query is a planning or execution problem: read EXPLAIN ANALYZE's Planning Time and Execution Time to diagnose the bottleneck.

TimescaleDB continuous aggregates and late-arriving data: invalidation tracking, lookback windows, and the two offsets that prevent stale aggregate buckets.

Your slowest query is rarely your most expensive. See how pg_stat_statements ranks queries by total cost and exposes planning time that mean latency hides.

Industrial IoT on TimescaleDB: ControlCom streams 1K-10K points/sec from thousands of assets, powering instant dashboards and AI that caught $160K billing errors.

TimescaleDB 2.28 adds schema evolution for continuous aggregates, lighter refreshes, faster compressed queries, and ends PostgreSQL 15 support.

Your Postgres constraints may be capping ingest throughput. Learn how FOREIGN KEY and UNIQUE checks slow high-rate inserts, plus four fixes that protect integrity.

Tiger Cloud ships up to 160x faster writes, native BM25 search, more storage, new regions, and better visibility on Postgres.

A TimescaleDB technical reference architecture for operational time-series data across AI data centers, from control layer to enterprise rollup.

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.