Tiger Data Ads

Plexigrid x Tiger Data: zero database crashes, 350x faster queries.

Plexigrid monitors power grids across North America and Europe. Then their database started collapsing under scale. InfluxDB crashed. Data stalled. Operators lost visibility. So they moved to TigerData. Now queries run in 0.5 seconds instead of 5 minutes. That’s 350x faster. Read how they fixed it.

Lag costs. Tiger Data doesn’t.

Most databases make you choose: keep your history, or keep queries fast. Tiger Data doesn’t. Native compression of up to 95% and automatic data tiering mean years of sensor history stay fully queryable. All without the usual sky-high storage bill. Long retention, fast queries. On one platform.

Case Study | High ingest time-series for power telemetry

Axpo, Switzerland’s largest power producer, uses Tiger Data to ingest 150 million time-series rows from over 20 power plant systems daily. That’s without performance degrading. See how they stuck with Postgres without adding pipelines. Read the case study.

Built for your current workload and the one you’ll grow into

When Postgres performance hits a wall, you don’t need a new database. You need Postgres to work with your growing workload. Tiger Data offers up to 95% compression and 480x faster queries. Without the growing pains of replatforming or learning a new language.

Want Lower Time-Series Costs Like Flogistix?

Safety, production efficiency, predictive maintenance. Oil and gas telemetry depends on fast queries to keep costs low. Other stacks charge a high price for the same results that Tiger Data offers on Postgres.

How Plexigrid slashed their database bill with TigerData.

350 GB of storage. 4 databases. And a terrifying InfluxDB licensing fee bill. That was Plexigrid’s data stack before Tiger Data. Now it’s: 3 GB of storage. 1 database. No licensing costs. Same data. Fraction of the footprint. See how they did it.

Postgres is now a search engine!!! With pg_textsearch that was just open sourced. Postgres finally has real keyword search. No Elasticsearch. No data sync pipelines. Just SQL. For years, Postgres search had a problem. It worked, but the ranking quality was brittle. If a document repeated a word 50 times, It often ranked higher than the actually relevant one. That is not how modern search should behave. 📌 pg_textsearch brings true BM25 ranking directly into Postgres. This is the same ranking algorithm used by modern search engines like Google. BM25 understands: → Which terms actually matter in your corpus → Term frequency saturation → Document length normalization With pg_textsearch, your data stays in Postgres. Your search index updates transactionally. When you update a row, the index updates in the same transaction. Search is no longer just for humans. We are in the RAG era. AI systems need: → Semantic vector search → Precise keyword matching pg_textsearch pairs naturally with: → pgvector → pgvectorscale 📌 Getting started is boringly simple: 𝙲𝚁𝙴𝙰𝚃𝙴 𝙴𝚇𝚃𝙴𝙽𝚂𝙸𝙾𝙽 𝚙𝚐_𝚝𝚎𝚡𝚝𝚜𝚎𝚊𝚛𝚌𝚑; Then sort by relevance using SQL. No new infrastructure to learn. pg_textsearch was open-sourced by Tiger Data (creators of TimescaleDB), the team behind TimescaleDB. Built by people who actually understand Postgres internals. 👉 Star the GitHub repo and try it yourself: https://lnkd.in/dCACZ3zU —— ♻️ Repost to help others start using keyword search in Postgres ➕ Follow me ( Anton Martyniuk ) to improve your .NET and Architecture Skills Many thanks to Tiger Data (creators of TimescaleDB) for sponsoring this post!

The No. 1 Time-Series Database Azure Developers Have Been Waiting For | Tiger Data

Azure now has real time-series Postgres. Tiger Data (creators of TimescaleDB), is live on Azure Marketplace with a managed service that delivers fast analytics, 95% average compression, and consistent low latency for time-series workloads. 🎉✨ If you work with telemetry, IoT pipelines, SaaS usage analytics, or any system that needs to store and query large volumes of time stamped data inside Azure, this is worth your attention. Tiger Data shows strong benchmark results with sub 10 ms responses and significant gains over standard Azure Postgres. You can read more about the benchmarks here: https://tsdb.co/adora

The bottleneck for AI agents is not prompting. It’s DB design. Everyone is obsessed with prompts. Meanwhile, their AI agent is plugged into a data stack that can’t explain its own context. MCPs help agents connect to databases. But raw DB values don’t work well for AI. An agent can’t reason properly if it doesn’t know: - where the data came from - what it represents - what “normal” looks like Agents need context. That’s what I liked in this article by Tiger Data (creators of TimescaleDB) It shows why combining time-series data + relational context in one PostgreSQL architecture makes data far more usable for AI agents. No unnecessary middleware. Just a system an agent can actually reason over. Read more here: https://tsdb.co/danielm-nl Have you tried connecting an AI agent to your DB? What broke first? Sponsored by TigerData #ad #TigerDataPartner

Live webinar: Modernizing 800+ SCADA systems

If you work in industrial monitoring, IoT, or observability, this is for you. Join CERN’s engineers and walk through how, with TimescaleDB, they: → Cut storage by up to 95% → Sped up historical analytics by up to 40% → Eliminated lock-in and long-term technical debt Live: Thursday, June 25 at 9:00 AM ET. Register now.

The bottleneck for AI agents is not prompting. It’s DB design. Everyone is obsessed with prompts. Meanwhile, their AI agent is plugged into a data stack that can’t explain its own context. MCPs help agents connect to databases. But raw DB values don’t work well for AI. An agent can’t reason properly if it doesn’t know: - where the data came from - what it represents - what “normal” looks like Agents need context. That’s what I liked in this article by Tiger Data (creators of TimescaleDB) It shows why combining time-series data + relational context in one PostgreSQL architecture makes data far more usable for AI agents. No unnecessary middleware. Just a system an agent can actually reason over. Read more here: https://tsdb.co/danielm-nl Have you tried connecting an AI agent to your DB? What broke first? Sponsored by TigerData #ad #TigerDataPartner

Azure now has real time-series Postgres. Tiger Data (creators of TimescaleDB), is live on Azure Marketplace with a managed service that delivers fast analytics, 95% average compression, and consistent low latency for time-series workloads. 🎉✨ If you work with telemetry, IoT pipelines, SaaS usage analytics, or any system that needs to store and query large volumes of time stamped data inside Azure, this is worth your attention. Tiger Data shows strong benchmark results with sub 10 ms responses and significant gains over standard Azure Postgres. You can read more about the benchmarks here: https://tsdb.co/adora

Tiger Data. Sub-second latency on Postgres. Start free.

In oil and gas, milliseconds could be the difference between safe and catastrophic. IoT systems that track sensor data in real time catch anomalies before they become incidents. See how low-latency Tiger Data protects your bottom line. Get 30 days free, no credit card needed.

Case study | WaterBridge uses Tiger Data for real-time data consistency

Managed SQL server instances degrade slowly over time. Tiger Data doesn’t. Even under the scale of 10,000 data points per second. Discover how WaterBridge keeps performance steady.

Lag costs. Tiger Data doesn’t.

Some DBs buckle under the weight of high-volume sensor writes. Tiger Data’s hypertables automatically partition data by time, keeping ingest fast and queries bounded. No manual tuning or performance degradation as data piles up. Write at scale. Query at speed. Never choose between the two.

TimescaleDB is built on one idea: you should be able to start on Postgres and keep scaling on Postgres, without introducing a second system. Our latest release, TimescaleDB 2.26, makes that promise more concrete. ColumnarIndexScan lets COUNT, MIN, MAX, FIRST, and LAST queries read directly from chunk-level sparse index metadata. No decompression required. A query that completed in 940 ms now completes in 13 ms. Up to 70x faster, and the gain scales with your dataset. time_bucket() aggregations now stay in the vectorized columnstore path end-to-end. Before this release, even queries on compressed columnar data would fall back to row-based processing partway through. 350 ms to 85 ms. Composite bloom filters extend batch pruning to multi-column predicates. Queries with compound conditions like (sensor_id, location_id) can now skip compressed batches before decompression. Over 2x faster for applicable SELECT and UPSERT workloads, with no manual configuration required in most cases. All three improvements take effect on upgrade. No schema changes, no query rewrites. TimescaleDB 2.26 is available now for all Tiger Cloud users. https://lnkd.in/gX4C5pdB #TimescaleDB #PostgreSQL #TimeSeriesData #DatabasePerformance

Case study | WaterBridge uses Tiger Data for real-time data consistency

Do you enjoy poor time-series performance? Then you’ll HATE Tiger Data. Hey %FIRSTNAME%, enjoy poor time-series performance? Then you’ll HATE Tiger Data.

Fast, efficient time-series querying and analytics.

Tired of Class 53 Vanilla Postgres errors? Tiger Data won’t buckle under the scale but still keeps you on Postgres. See how Axpo inserts 150 million rows of time-series data without performance degrading.

CERN modernized their legacy archiving stack. Will you?

CERN runs 800+ SCADA systems generating hundreds of gigabytes of time-series data every single day. They did that by moving from Oracle to TimescaleDB: ✔ Up to 40x faster queries ✔ Up to 95% storage reduction Join the live webinar to discover how. Save your seat now.

Live webinar: CERN engineers on rebuilding their data stack

Two CERN engineers and TimescaleDB’s Director of Product are going live on June 25 to show exactly how they cut storage by 95% 60 minutes with the engineers who actually did it. Without slides full of vendor claims. Register now. Can’t make it live? We’ll send you the recording.

[Case study] How to manage 275 GB of daily sensor data for 66% less cost.

Flogistix processes 275 GB of sensor data daily across remote U.S. oilfields. But with fleet growth at 10–15% a year, their old data stack was buckling under the weight. Costs were getting out of control. Tiger Data changed that. 84% compression. 66% infrastructure savings. Up and running in under a month. If your data costs are growing as fast as your business, this one might be worth the read.

From constant data gaps to 99% reliability. Here's how.

Flogistix was drowning in 140 million records and constant data gaps. Their 3700+ oil field assets crew needed live answers. But the data infrastructure kept failing them. After moving to TigerData, they modernized their pipeline, accelerated dashboards with continuous aggregates. And their data reliability improved from 95% to 99%. See the breakdown.

CERN generates hundreds of gigabytes of time-series data every day from 800+ SCADA systems supporting some of the world’s most complex physics experiments. Their legacy archiving stack couldn’t keep up. By rebuilding on TimescaleDB (Tiger Data), CERN achieved 95% storage reduction and 40% faster historical analytics, while keeping dashboards responsive across decades of data. Join CERN engineers Rafal Kulaga and Martin Zemko on June 25 at 9 AM ET as they share the architecture, design decisions, and lessons learned from building the NextGen Archiver. Free to attend. Register below. https://tsdb.co/7r16uanf #PostgreSQL #TimeSeriesData #IndustrialData #IoT #TimescaleDB

Postgres is now a search engine!!! With pg_textsearch that was just open sourced. Postgres finally has real keyword search. No Elasticsearch. No data sync pipelines. Just SQL. For years, Postgres search had a problem. It worked, but the ranking quality was brittle. If a document repeated a word 50 times, It often ranked higher than the actually relevant one. That is not how modern search should behave. 📌 pg_textsearch brings true BM25 ranking directly into Postgres. This is the same ranking algorithm used by modern search engines like Google. BM25 understands: → Which terms actually matter in your corpus → Term frequency saturation → Document length normalization With pg_textsearch, your data stays in Postgres. Your search index updates transactionally. When you update a row, the index updates in the same transaction. Search is no longer just for humans. We are in the RAG era. AI systems need: → Semantic vector search → Precise keyword matching pg_textsearch pairs naturally with: → pgvector → pgvectorscale 📌 Getting started is boringly simple: 𝙲𝚁𝙴𝙰𝚃𝙴 𝙴𝚇𝚃𝙴𝙽𝚂𝙸𝙾𝙽 𝚙𝚐_𝚝𝚎𝚡𝚝𝚜𝚎𝚊𝚛𝚌𝚑; Then sort by relevance using SQL. No new infrastructure to learn. pg_textsearch was open-sourced by Tiger Data (creators of TimescaleDB), the team behind TimescaleDB. Built by people who actually understand Postgres internals. 👉 Star the GitHub repo and try it yourself: https://lnkd.in/dCACZ3zU —— ♻️ Repost to help others start using keyword search in Postgres ➕ Follow me ( Anton Martyniuk ) to improve your .NET and Architecture Skills Many thanks to Tiger Data (creators of TimescaleDB) for sponsoring this post!

Faster Sensor Data Insights Tiger Data: Ingest, store, and analyze sensor data in real time with SQL built for scale.

Agent Database Postgres - Open Source Agentic DB Tiger Data: Agentic Postgres is built for Al agents with native vector + hybrid search. Free Postgres for... Tiger Data vs. Mongo DB Forkable Postgres for Al The Agentic Database for Al Tiger Data vs. Influx DB Tiger Data vs. Cassandra

www.tigerdata.com/ Performance Optimization DB - Trusted by 1000s in lo T & Web3 - St... The #1 time-series database built on Postgres. Start free, skip learning other languages. Join 1,000s of devs, engineers, and data scientists reimagining Postgre SQL possibilities.

www.tigerdata.com/ Performance Tuning DB - Trusted by 1000s in lol & Web3 - Start Free No... The #1 time-series database built on Postgres. Start free, skip learning other languages. Get up to 1000x faster queries, 90% data compression, and 100+ SQL hyperfunctions.

Tiger Data™ Cloud-native Postgre SQL for time series and events. Speed, scale, and savings.

www.tigerdata.com/ Performance Optimization DB - Trusted by 1000s in lo T & Web3 - St... The #1 time-series database built on Postgres. Start free, skip learning other languages. Join 1,000s of devs, engineers, and data scientists reimagining Postgre SQL possibilities.

Streamlined Data Ingestion With Tiger Data, ingest, store, and query event data in real time with Postgre SQL.

Timescale ™ Cloud-native Postgre SQL for time series and events. Speed, scale, and savings.

DB Monitoring System - Always Open Source at Core The #1 time-series database built on Postgres. Start free, skip learning other languages. Store, analyze, and run...

Time-Series Analysis Database - Dramatically Fast Query Time The #1 time-series database built on Postgres. Start free, skip learning other languages. Store, analyze, and run RT analytics on massive data volumes. Start free, no CC needed. nw Time Series & Analytics Tiger Data vs. Mongo DB Tutorials Docs

The #1 Time-Series Database - Always Open Source at Core The #1 time-series database built on Postgres. Start free, skip learning other languages. Store, analyze, and run RT analytics on massive data volumes. Start free...

Postgre SQL for Time Series - Postgre SQL, Now Supercharged Tiger Data: Run faster analytics at scale with -time- series, SQL, & real-time performance. Tiger Data... Tiger Data vs. Mongo DB Fast. Scalable. Postgres. 1,000x Faster Queries. Scale Postgre SQL Tiger Data vs. AMZ RDS

Time-Series Analysis Database - Faster Time-Series Analysis The #1 time-series database built on Postgres. Start free, skip learning other languages. Store, analyze, and run RT analytics on massive data volumes. Start free, no CC needed. Time Series & Analytics Tiger Data vs. Mongo DB Pricing Tutorials Docs

#1 Time-Series Post Gres DB - Always Open Source at Core The #1 time-series database built on Postgres. Start free, skip learning other languages. nw Tiger Data vs. Influx DB Pricing Time Series & Analytics Scale Postgre SQL Agentic Postgres

Agentic Posigres for Al Use Agentic Postgres: Al-Native Database with MCP, Search & Instant Forks - Tiger Data

www.tigerdata.com/ Performance Optimization DB - Trusted by 1000s in lo T & Web3 - St... The #1 time-series database built on Postgres. Start free, skip learning other languages. Join 1,000s of devs, engineers, and data scientists reimagining Postgre SQL possibilities.

#1 Hosted Postgre SQL - Partition Automatically Get high performance data analysis on an infinitely scalable Postgre SQL tool. Start free. Get up to 1000x faster queries, 90% data compression, and 100+ SQL hyperfunctions. Try it Free for 30 Days (Pricing ) Tutori

Best Postgre SQL Hosting - Scale Postgres Painlessly Get high performance data analysis on an infinitely scalable Postgre SQL tool. Start free. Get up to 1000x faster queries, 90% data compression, and 100+ SQL...

Vectorized Postgres Database Store and search vector embeddings in Postgres | 28x lower p95 latency than Pinecone.

www.tigerdata.com/ Top Postgres Performance - Trusted by 1000s in lol & Web3 - Start Now, No... All your tools, all existing libraries, and your all code already work on Tiger Data. " Get up to 1000x faster queries, 90% data compression, and 100+ SQL hyperfunctions.

Postgres Vector Similarity Store and query vector embeddings in Postgres. No separate vector storage layer required.

www.tigerdata.com/ Free-to-Start Postgres DB - Trusted by 1000s in lo T & Web3 - Start Now, No... All your tools, all existing libraries, and your all code already work on Tiger Data. " Get up to 1000x faster queries, 90% data compression, and 100+ SQL hyperfunctions.

#1 Online Postgre SQL Database - Get 200+ SQL Functions All your tools, all existing libraries, and your all code already work on Tiger Data. " Get up to 1000x faster queries, 90% data compression, and 100+ SQL... Pricing Tiger Data vs. Mongo DB Try it Free for 30 Days About Us

Alternative Database for Scale Build faster with a modern, scalable Postgres database —fully managed in the cloud.