Timescale Newsletter Roundup: April 2021 Edition

Timescale Newsletter Roundup: 
April 2021 Edition

In this edition, we’re sharing 3+ new product updates, ways to generate realistic sample data for demos, benchmarks, and more. This one is also packed with our favorite PostgreSQL and time-series resources and awesome technical content from community members.

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]: New Timescale Cloud Plans starting at USD $24/mo >>

Many of you have asked about smaller Timescale Cloud plans for development and test purposes. We’re excited to share that we just launched a new lower-cost Timescale Cloud plan, available immediately. (And, as a reminder, you can scale up to 4TB at any time).

[Product Update #2]: TimescaleDB 2.2 is available for self-managed, Timescale Cloud, and Managed Service for TimescaleDB >>

We recently shipped TimescaleDB 2.2, which includes two new features (Skip Scan optimization and distributed restore point) to help you store and analyze time-series data at petabyte scale, as well as various bug fixes.

[Product Update #3: Promscale Release (v 0.3.0) >>

We’re excited to announce a new major release for Promscale, the open-source long-term store for Prometheus metrics, designed for analytics. This release makes Promscale even more scalable for storing, analyzing, and acting on your Prometheus data.

[Product Update #4]: Timescale Analytics 0.2 release >>

Timescale Analytics’ latest release includes enhancements to existing capabilities and four new experimental features for visualizations (ASAP smoothing), working with resettable counters (Counter Aggregates), and advanced downsampling (Largest Triangle 3 Buckets and Time Bucket Range). Timescale Analytics comes pre-installed on Timescale Cloud.

New technical content, videos & tutorials

[▶ Watchlist #1]: Fun with SQL Analytic Functions Part 2: First() and Last() >>

Learn how to use EXPLAIN with BUFFERS and create continuous aggregates with data from FIRST() and LAST() to significantly speed up your queries.

[▶ Watchlist #2]: Creating sample data: what are your options? >>

Developer Advocate, Ryan Booz breaks down four ways we (Team Timescale) generate sample time-series data for benchmarks, blogs, tutorials, demos, and more. You’ll see how to use PostgreSQL generate_series, Time Series Benchmark Suite, APIs & SDKs, and our go-to sample datasets – and when and how you can use them for your own projects.

[▶ Watchlist #3]: Creating sample data, part 2: intro to generate_series >>

Ryan Booz continues the sample dataset series, starting with how to use the Postgres function generate_series to create sample data for your projects. Once you have the basics, he'll show you how to combine multiple sets of generated data for complex, larger-scale testing scenarios.

[▶ Watchlist #4]: Creating sample data: doing more with generate_series >>

Ryan Booz continues to dive into PostgreSQL generate_series, focusing on more advanced scenarios. You'll create new, reusable PostgreSQL functions to produce sample data in less time, make your generate_series data more realistic, create visualizations, and imitate seasonality in your demo data.

[Time-Series Fun]: Explore the price of Bitcoin and Ethereum over time >>

Get detailed instructions for connecting to TimescaleDB, designing your schema, and creating your dataset (plus sample queries to kick off your analysis).

New #remote-friendly events & community

[Office Hours]: Join us for Office Hours on Tuesday, June 1st >>

Sign up for our June Office Hours to chat with our product & engineering team, ask questions, and more. Whether you’re new to TimescaleDB, an experienced database pro, or somewhere in the middle, everyone is welcome to join in on the fun!

[Community Spotlight]: How WsprDaemon combines TimescaleDB and Grafana to analyze radio transmissions >>

Time-series data is everywhere – and, in this Developer Q & A, our friends at WsprDaemon share how they use TimescaleDB, Grafana, and various datasets to analyze radio transmissions, spot and compare trends in space weather, and more (and why they chose TimescaleDB over InfluxDB 🙌.

[▶ Watchlist - Community]: TimescaleDB for Finance: Python, Docker, Alpaca & more >>

@PartTimeLarry’s 10-episode series takes you from “what is time-series?” to building an ETF database, designing your schema, analyzing ARK buy/sell trends, building dashboards with Streamlit, and more - complete with commentary and explanations.

[Tools - Community]: Looking for new tools or resources for your startup? Check out StartupToolchain >>

StartupToolchain is a roundup of awesome tools and resources for startup teams, from software development to design, payments, fundraising, and more. (We’re honored to be included!)

Screenshot of StartupToolchain website showing Software Development tools

TimescaleDB tips, reading list & more

[TimescaleDB Tip #1]: Speed up batch inserts with parallel-copy >>

Use this handy tool to speed up inserts and data migrations for large time-series workloads (100K+ row CSVs). Our goal: you spend more time analyzing and querying your data, not executing single COPY commands.

[TimescaleDB Tip #2]: Explore TimescaleDB quick starts (Golang, Node, Python & Ruby) >>

We’ve created a few framework/language-specific quick-starts to help you get you up and running with TimescaleDB. Each guide takes you through connecting your app, creating tables, inserting rows, and executing your first time-series analysis query.

[Reading List]: Rethinking the database materialized view as an Index >>

Why do materialized views and indexes *seem* so different, despite essentially doing the same thing? In this classic post, we detail how we designed TimescaleDB, rethinking the materialized view as an index (returning the latest data).

[Reading List]: When Boring is Awesome: Building a scalable time-series database on PostgreSQL >>

In honor of Timescale’s 4th birthday (on April 4th) we’re rewinding it back to share our very first blog post. Our co-founders share our origin story, why we set out to build a relational database for time-series, and what makes TimescaleDB unique.

[Reading List]: How and why to use SQL for time-series data >>

Get 4 quick tips for using your SQL skills for time-series data analysis, complete with technical guidance and advice.

[Reading List]: Speeding up Grafana by auto-switching between different aggregations, using PostgreSQL >>

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]: Top 5 PostgreSQL extensions >>

We love PostgreSQL for many reasons, but a big one is its extensibility and vast ecosystem of 20K+ extensions. We round up a few of our favorites, why you’d use them, and how to install each one (plus a few sample queries and pro tips to get you started).

  • 🔎  See @avthars Twitter thread for an at-a-glance breakdown.
  • 🙏 Thank you to all of the TimescaleDB community members who provided input.

[Reading List]: Time-series compression algorithms, explained >>

What are compression algorithms and how do they work? While they’re not quite magic, they can speed up your queries. In this post, we break down various types, including how they work, when and how you’d use them, and ways to get started.

[Reading List]: TimescaleDB vs. InfluxDB: Purpose built differently for time-series data >>

In this article, we benchmark TimescaleDB and InfluxDB, comparing them across 7 factors critical to time-series workloads, including data model, query language, reliability, and performance.

[Reading List - Community]: Deploying Zabbix on PostgreSQL with Timescale DB plugin >>

Our friends at @Zabbix detail how to combine Zabbix and TimescaleDB – including why they recommend TimescaleDB vs. other supported database backends (Spoiler: TimescaleDB’s compression and native partitioning ✨).

[Reading List - Community]: Building a global environmental datastack for climate action >>

Learn how our friends – and long-time TimescaleDB community members – Blue Sky Analytics make sense of terabytes of raw data, turn them into environmental insights, and drive sustainable decision making.

  • 📈 Check out BreeZo, Blue Sky’s Analytics tool for monitoring air pollution in India.
  • 👉 Visit Blue Sky Analytics to learn more about their mission to be the go-to source for all environmental data.

Twitter fun

Screenshot of tweet by Jason Warner giving shoutout to TimescaleDB for being compatible with PostgreSQL
🌟 Tweet of the week #1: big thank you to @jasoncwarner & @JustJake for the shoutout (check out Jake's latest project: railway.app/)

Screenshot of tweet by CRN mentioning TimescaleDB and TigerGraphDB as the coolest database system companies
🌟 Tweet of the week #2: We are honored to be featured in @CRN´s Coolest Database System Companies 2021 list 🎉 Have a look at the full list for details and to view all categories.

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.
This post was written by
8 min read

Related posts