Timescale Newsletter Roundup: January 2021 Edition

Timescale Newsletter Roundup: January 2021 Edition

We’re sharing our plans for Timescale Analytics, ways to get up and running with TimescaleDB 2.0, 12 of our favorite resources – and celebrating a big milestone: hitting 10K GitHub stars ⭐.

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

[NEW]: Introducing Timescale Analytics: Time-series Analytics for PostgreSQL >>

SQL is powerful (we’re clearly big fans), and we believe that by adding a set of specialized functions for time-series analysis, we can make it even better. Enter Timescale Analytics: a project that aims to identify, create, and combine all of the capabilities SQL needs to perform time-series analytics into a single Postgres extension.

Grafana dashboard UI showing initial and downsampled (1/10th) datasets.
Timescale Analytics includes proposals for enhanced queries and graphing tools. In this example, we downsample data (dataset), keeping the large-scale features, with an order-of-magnitude fewer data points

[NEW]: MongoDB vs. TimescaleDB: 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. 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.

Graph showing Ingest rate comparison between two methods of storing time-series data in MongoDB and TimescaleDB
TimescaleDB shows 169% (vs. method 1) to 260% (vs. method 2) the write performance of MongoDB.

New technical content, videos & tutorials

[TimescaleDB 2.0]: How and why to use user-defined actions >>

User-defined actions - new in TimescaleDB 2.0 - allow you to refine our existing policies and write your own custom, automated procedures and functions to suit your needs. @avthars gives you a whirlwind tour, including ways to set automated data retention policies, implement downsampling, and oh so much more.

Watch our overview demo video to see how to apply user-defined actions to your projects ✨

[Quickstart]: TimescaleDB for Go developers >>

We have quite a few Gophers at Team Timescale, and we put together this handy quickstart to help Go developers get up and running with TimecaleDB. In a series of 5 steps, you’ll connect your apps, run your first time-series query, and get ways to analyze results.

[Real-Time Aggregation]: How to ensure up-to-date results with real-time aggregation >>

Mike Freedman - Timescale co-founder & CTO - breaks down why real-time aggregates are the best both worlds: query your latest (raw) and aggregated data *and* return results quickly. Get the basics, sample queries to help you get started, performance benchmarks, and more.

  • 🍿 Watch our demo to see how to apply real-time aggregates in a DevOps scenario (<7 mins).

New #remote-friendly events & community

[Community Spotlight]: Learn how SciPrime powers their stack with Grafana and TimescaleDB >>

We love to see how community members combine TimescaleDB with other open-source tools, and SciPrime’s Storage Network Status Grafana dashboards deserve all the kudos.

  • 🙏 Thanks to our friends @sciprime for sharing this (amazing) example of how to make time-series data clear and actionable.
  • 📊 Want to build your own Grafana visualizations or enhance existing ones? Check out 5+ step-by-step tutorials.

[Office Hours]: Join us for community Q&A and time-series watercooler >>

Join to ask questions about TimescaleDB, get advice about optimizing your queries, or simply to chat all things databases.

[Community News]: TimescaleDB hit 10K GitHub stars! 🥳 >>

We've come a long way since making our first commit – and biggest thank you to everyone who’s starred us, submitted issues, and helped make TimescaleDB better.

Mike - our co-founder & CTO - sharing the news, just in time for the new year 🎉 (see tweet)

TimescaleDB tips, reading list & more

[TimescaleDB Tip #1]: Use Continuous aggregates to speed up your queries >>

Fact: queries that touch large amounts of data can take a long time to compute. Continuous aggregates solve this problem, expanding on PostgreSQL materialized views so you can continuously and incrementally refresh aggregates. Learn how they work, walk through simple and complex examples, and get best practices.

[TimescaleDB Tip #2]: How to build your own monitoring & alerting solution >>

Grafana is known for visualizations, but it’s a powerful alerting tool too. Learn how to combine TimescaleDB, Grafana, and 5+ notification channels to define and trigger custom alerts for the metrics you care about.

[Reading List]: 4 ways to use SQL for time-series data >>

SQL is 💯 for times-series workloads, and we share a few quick tips and resources for joining time-series and relational data, using the 80-20 rule for your database schema, and speeding up queries (plus our favorite open-source tools).

[Reading List]: 12 things you need to know about time-series data >>

To help you kick off the new year and new projects, we’ve rounded up our top advice and recommended time-series resources. Get an all-in-one guide, from PostgreSQL pro tips and example projects to critical features, database evaluation criteria, and more.

[Reading List]: How we're building a self-sustaining open-source business in the cloud era >>

In October 2020, we shared that we're making (and have since made) all of our software free, expanded our license to give community members more flexibility and control, and abolished the notion of “enterprise” features. Learn about our journey, why we’re going all-in on cloud, and why we believe Cloud Protection Licenses are critical for open-source businesses.

[Reading List - Community]: The case for using time-series databases >>

We love this overview and quick comparison of time-series databases, when you need one, and why specialized databases are becoming ubiquitous, complete with 💯 analogies and concrete examples.

  • 🙏 to Kovid and Toward Data Science for sharing and giving us a shoutout!
  • 🔎 For more comparisons, check out TimescaleDB vs. alternatives.
VentureBeat tweet: Database trends highlighting time-series databases trending upward
Time-series databases are the fastest growing DB category. Read VentureBeat’s take on why.

[Team Timescale]: If you schedule it, they will come >>

Watch Timescale People Manager @melsavoia (aka our remote-team building champion)’s Princeton Keller Center Q & A to learn how to overcome scheduling woes, get remote team bonding ideas, and walk through real examples.

🙌 Want to see her work in action? We’re hiring!

Wrapping Up

And, that concludes this month’s newsletter roundup. We’ll continue to release new content, events, and more - posting monthly updates for everyone.

If you’d like to get updates as soon as they’re available, subscribe to our newsletter (2x monthly emails, prepared with 💛 and no fluff or jargon, promise).

Happy building!

The open-source relational database for time-series and analytics.
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