Timescale is a modern cloud platform built on PostgreSQL for time series, events, and analytics. It provides a time-series SQL database, TimescaleDB, which offers fast analytics, scalability, and automated data management on a proven storage engine.

Timescale is addressing one of the largest challenges (and opportunities) in databases for years to come: helping developers, businesses, and society make sense of the data that humans and their machines are generating in copious amounts. 

TimescaleDB is the only open-source time-series database that natively supports full-SQL, combining the power, reliability, and ease-of-use of a relational database with the scalability typically seen in NoSQL systems. It is built on PostgreSQL and optimized for fast ingest and complex queries. TimescaleDB is deployed for powering mission-critical applications, including industrial data analysis, complex monitoring systems, operational data warehousing, financial risk management, and geospatial asset tracking across industries as varied as manufacturing, space, utilities, oil & gas, logistics, mining, ad tech, finance, telecom, and more. 

Timescale is backed by NEA, Benchmark, Icon Ventures, Redpoint Ventures, Two Sigma Ventures, and Tiger Global.

Documentation: https://docs.timescale.com
GitHub: https://github.com/timescale/timescaledb
Twitter: https://twitter.com/timescaledb

New Enterprise Associates

Two Sigma Ventures

Benchmark

Icon Ventures

Michael Walrath

Redpoint

Tiger Global Management

Timescale

We're Timescale, of course, we're going to tell you to watch this video and click the link below. 🚀👇
 https://tsdb.co/postgres

🤔 How does the BETWEEN operator simplify data filtering in PostgreSQL?

🐯🐘Timescale – Weekly PG Tips

Struggling with complex data filtering?

The BETWEEN operator in PostgreSQL offers a streamlined solution!

(But, there is a trap you need to watch out for 👀).

🔍💻 By using BETWEEN, you can specify a range of values directly in your WHERE clause, reducing the need for multiple conditions. 

⏳🎯 Check out this example:
sql
SELECT * FROM employees
WHERE salary BETWEEN 50000 AND 80000;

This query retrieves all employees with salaries >= $50,000 AND <= $80,000. 

But beware 😵💫, because the overlapping upper and lower bounds BETWEEN isn’t for every use-case.

In time-series data, you wouldn’t use it for a graph, because the upper and lower bound would be double plotted in adjacent time ranges.

🚀💡 Ready to simplify your SQL queries? 
Explore the power of the BETWEEN operator today! 💪🔑

(but watch out for the 🐉)

#Postgres #PostgreSQL #PostgresTips #SQL

📅 Join Jônatas Paganini's live session with the Timescale community in less than 1️⃣ 0️⃣ minutes! 🚀 

We'll be walking through the #finance framework and exploring why default choices matter. 📊 

Don't miss out on valuable insights for finance professionals and enthusiasts!

🌐 All events will be on Thursdays at 11:00 AM GMT-3. Check out the other upcoming sessions:
* Feb 29, 2024 - Hierarchical continuous aggregates for candlestick processing
* March 7, 2024 - Tracking last price and the trigger side effects
* March 14, 2024 - Tracking pair correlation
* March 21, 2024 - Downsampling techniques
* March 28, 2024 - Compression and side effects

🔗 Interested? Join our Slack community or DM us for more details: https://lnkd.in/deVTvUUB

Finance + Timescale + Jônatas Paganini -> bring it on 📈!

🧑💻 Introducing Weekly PostgreSQL Tips!

Every week, Doug Ortiz 🐘🐯 will share PostgreSQL insights and tips you won't want to miss.👇

🤔 Are your PostgreSQL queries running slower than expected? 
🚀 Boost their performance with a simple solution! 

By optimizing query performance with indexes, you can significantly enhance search speed. 

💡 Learn how to implement CREATE INDEX on frequently queried columns to unlock the full potential of your database. 

#PostgresTips #DatabasePerformance #PostgreSQL

Discover how to create a fast time-series graph with PostgreSQL materialized views.

Gain valuable insights on effectively visualizing and analyzing time-series data for real-time analytics. 👇

https://lnkd.in/g7_mxddZ 

#TimeSeries #PostgreSQL #DataAnalytics

Cool to see this OpenAI GPT that enables you to analyze 7M+ US court decisions to more easily find relevant court cases to your question. 

It's one of an increasing number of Legal AI applications being built with pgvector and Timescale Vector.

Long #PostgreSQL 🐘📈!

#legalai #vectordatabase #pgvector #gptstore #openai

🤨 Struggling with slow aggregated data in PostgreSQL? 

💡 Read about the challenges with data aggregation and how to overcome them using continuous aggregates. 

💪 Learn how to improve query response times with this incremental and automatically updated version of PostgreSQL materialized views. 

Check it out here 👉 https://tsdb.co/speed

#PostgreSQL #DataAggregation #ContinuousAggregate

Are you curious about time-series analysis and its practical applications? 

Explore the fundamentals, methodologies, examples and use cases of time-series analysis. 

Learn more ⬇️ 
https://lnkd.in/gBAsu_uq 

 #DataAnalysis #TimeSeries #DataInsights #PostgreSQL

🤔 Curious about the battle between Postgres TOAST and Timescale Compression? 

We conducted a comprehensive comparison to uncover the limitations of TOAST and showcased the power of Timescale's columnar compression. 

✨ Explore the findings and uncover how you can optimize your PostgreSQL database storage. 👇

https://tsdb.co/toast 

#DataManagement #TimescaleDB #PostgresSQL

COM:TIMESCALE

Timescale

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