Timescale Newsletter Roundup: August 2021

In this edition, we’re celebrating 7K+ members on TimescaleDB Slack, sharing the latest release updates, a new developer Q&A from our friends at METER Group, and a few of our favorite PostgreSQL and time-series resources.

We’re always releasing new features, creating new documentation and tutorials, and hosting virtual sessions to help developers do amazing things with their data. And, to make it easy for our community members to discover and get the resources they need to power their projects, teams, or business with analytics, we round up our favorite new pieces in our biweekly newsletter.

We’re on a mission to teach the world about time-series data, supporting and growing communities around the world. And, sharing educational resources as broadly as possible is one way to do just that :).

Here’s a snapshot of the content we shared with our readers this month (subscribe to get updates straight to your inbox).

Product updates & announcements

[Product Update #1]: Move fast, but don’t break things: Introducing the experimental schema (with new experimental features) in TimescaleDB 2.4 >>

TimescaleDB 2.4 just shipped and includes a new experimental schema, which enables us to develop and ship features at an even faster pace than we normally do – while still providing the stability we know users expect and rely on. (Experimental features will “graduate” out of the experimental schema once they reach full maturity for normal production usage.)

🚀 TimescaleDB 2.4 experimental features include an improved version of time_bucket - a top user request! - and functionality for elasticity and high availability in multi-node TimescaleDB.
👉 Read the blog post for details on the experimental schema and experimental features.
🐥 See our CEO's thread for more highlights on this release.
🔎 See the full release notes on GitHub.
🗂 Visit GitHub to join the discussion.
📚 Visit the docs to learn how to use the new experimental features.

[Product Update #2]: New SQL functions to simplify working with time-series data in PostgreSQL >>

We recently announced the release of TimescaleDB hyperfunctions, a series of SQL functions within TimescaleDB that make it easier to manipulate and analyze time-series data in PostgreSQL with fewer lines of code.

👉 Read the blog post for more on hyperfunctions and why we built them.
🗂 Explore the roadmap on GitHub for in-progress and future hyperfunctions.
🐥 See the Twitter thread for a summary of the new features.
📚 Visit the docs to get started with hyperfunctions.
🙏 Thank you to our friends at CDOTrends for highlighting TimescaleDB hyperfunctions.

[Product Update #3 – Promscale]: Simplified Prometheus monitoring for your entire organization with Promscale >>

Get a primer on why we’ve built support for Prometheus multi-tenancy in Promscale, the scenarios and challenges it solves, the types of queries it frees you to make, and how to set up multi-tenancy for your team or organization.

👉 Read the blog to learn how Promscale combines operational maturity and query flexibility to help you store and query metrics across your entire organization.
🔎 See the Promscale docs to get started with multi-tenancy support in your Promscale deployments today.

[Product Update #4 – Timescale Cloud]: New TimescaleDB interactive demo: Allmilk Factory >>

We just launched a new interactive demo (with 50 million data points!) on Timescale Cloud to help you get up and running with TimescaleDB and its most important features.

👉 To try it out, create a new Timescale Cloud service and select the "demo data" option. Then, start exploring!
📌 If you haven’t tried Timescale Cloud yet, sign up for a free 30-day trial.
🐥 See the Twitter thread for the highlights.

New technical content, videos & tutorials

[Time-Series Fun]: Hacking NFL data with PostgreSQL, TimescaleDB, and SQL >>

Learn how to analyze 18M+ rows of NFL player performance data, courtesy of the NFL and Kaggle, to uncover valuable insights – like which players are extreme outliers, sack percentage by quarterback, and plays per game vs. points scored – all with PostgreSQL, standard SQL, and freely available extensions.

👉 Check out the blog post
📃 See the tutorial to get the dataset and step-by-step guidance for ingesting, analyzing, and visualizing the data.
🐥 Read the Twitter thread for the highlights.
💬 Read the discussion on Hacker News.

New #remote-friendly events & community

[Office Hours]: Timescale monthly Office Hours (September 7th) >>

Join us Tuesday, September 7th for monthly Office Hours with Timescale Developer Advocate Ryan Booz. Catch up on the latest product updates and upcoming releases, watch demos, meet community members, and ask any questions you have for our engineers.

🗓 Reserve your spot for the upcoming session.
💬 If you can’t join but have a question, reach out to our engineering team on Slack.

[Community Spotlight]: How METER Group brings a data-driven approach to the cannabis production industry >>

Learn how our friends at the METER Group architected their data stack to collect and visualize massive amounts of data – and help customers make informed business decisions, maximize their crop yields, and thrive.

🙏 Huge thank you to Paolo Bergantino from METER Group.
👉 Learn more about METER Group and their AROYA solution.
📣 Want to share your story? Reply to this email and we’ll make it happen.

TimescaleDB tips, reading list & more

[TimescaleDB Tip]: Use Tableau to visualize data in TimescaleDB >>

Tableau is a powerful business intelligence tool – and an ideal companion to data stored in TimescaleDB. Follow this short step-by-step tutorial to learn how to set up Tableau to examine time-series data stored in TimescaleDB.

[Reading List]: What are time-weighted averages and why should you care? >>

Timescale engineer David Kohn details what time-weighted averages are, why they’re so powerful for data analysis, and how to use TimescaleDB hyperfunctions to calculate them faster - all while using SQL.

👉 Read the blog post for a time-weighted averages overview, sample queries, and more.
▶️ Watch the behind-the-scenes demo to see how time-weighted averages work and how to get started.
💬 Visit the GitHub discussion page to post comments, ask questions, and share feedback.
🔎 See the hyperfunctions docs to get started with time-weighted averages in your projects.
💬 Read the discussion on Hacker News.

[Reading List]: How PostgreSQL aggregation works and how it inspired our hyperfunctions’ design >>

In his latest blog post, Timescale engineer David Kohn breaks down how PostgreSQL aggregation works, how it inspired TimescaleDB hyperfunctions’ implementation & integration with advanced features, and how developers can leverage this in their projects.

👉 Read the blog post.
🐥 See the Twitter thread for the highlights.
💬 Visit the GitHub discussion page to post comments, ask questions, and share feedback.
▶️ Watch demo: Inside TimescaleDB: Two-step aggregation.

[Reading List]: Using PostgreSQL to speed up Grafana: auto-switching between different aggregations >>

Learn how to use UNION ALL to build graphs that allow you to “auto-switch” aggregated views of your data (e.g., daily, hourly, weekly) in the same Grafana visualization. The result: faster dashboards that allow you to drill into your metrics as quickly as possible & save time and CPU resources.

[Reading List]: TimescaleDB vs. MongoDB: a NoSQL vs. SQL comparison >>

@avthars covers two ways to store and query time-series data in MongoDB – and how TimescaleDB stacks up against MongoDB across several dimensions. In a nutshell, TimescaleDB outperforms both MongoDB methods (by a lot - up to 250%+ higher insert performance and 54x faster queries) and requires much less code and time to implement.

🧰 Check out the Time Series Benchmarking Suite to re-run the benchmarks yourself & compare other time-series databases.

[Watchlist]: Exploring NFL play-by-play data with TimescaleDB >>

Follow along with Timescale Developer Advocate Miranda Auhl as she explores and analyzes NFL data. In this recorded livestream, you’ll explore various topics - including Timescale's continuous aggregates, hyperfunctions, and much, much more.

▶️ Replay the livestream to join in on the fun.
👉 Check out the blog post to learn more about our NFL data insights.
📃 See the tutorial to get the dataset and step-by-step guidance for ingesting, analyzing, and visualizing the data.
💬 Ask your questions about TimescaleDB hyperfunctions in our GitHub discussion.
💬 Read the discussion on Hacker News.