Icon Ventures leads a new round of funding for Timescale to develop the foundational technology for businesses storing, analyzing, and acting on time-series data
Today, as we approach the 2nd anniversary of when we first released TimescaleDB to the public, we’re proud to share two major announcements.
First, we’ve raised another $15 million in a Series A1 financing led by Icon Ventures with participation from existing investors Benchmark, NEA, and Two Sigma Ventures, just one year after we announced our previous Series A financing.
Altogether, Timescale has now raised over $31 million to build the foundational database for the accelerating time-series data market.
Second, we’re also publishing a new release of TimescaleDB that marks a major milestone in our journey to a self-sustaining open-source business. Since its inception, TimescaleDB has been an open-source software project (Apache 2.0). With the release of TimescaleDB 1.2, that open-source core is now complemented with community features (free, Timescale License) and enterprise features (paid, Commercial License). All of that code base has been made source-available in the TimescaleDB GitHub repository. (More on TimescaleDB 1.2 can be found in this separate post by our CTO.)
Helping organizations analyze the past, monitor the present, and predict the future
Time-series data keeps cropping up in more and more places. As storage becomes cheaper, and computing becomes more powerful, businesses and organizations across a broad set of industries are storing data at higher fidelities.
Time-series is the highest fidelity of data you can store. Every data point has a timestamp. Storing your data in a time-series format allows you to analyze not just what your data looks like right now, but how it is changing over time. Put another way: Why look at a point, when you can study a line? Why look at a still image when you can watch the movie of how your business and operations are doing over time?
Yes, all data is fundamentally time-series data.
We are already observing the transition from storing “snapshots” of data to time-series data, and this trend will only continue to grow as businesses realize the value they can derive from time-series data.
“We're very happy with TimescaleDB at Nutanix. We researched a number of different options to store and analyze the variety of metrics data we collect. Ultimately, we decided TimescaleDB, with the flexibility and reliability of PostgreSQL, was the best option. We're so happy with the results we're seeing from TimescaleDB that we're in the process of deploying it in other areas at Nutanix.”
- Dan Small, Performance Engineering, Nutanix
In an industry crowded with database options, TimescaleDB remains unique in sitting at that intersection of time-series, PostgreSQL, SQL, and scalability.
We recognize the critical fact that time-series data doesn’t exist in a vacuum, but must be understood within the context of the larger universe of a company’s data. The challenge with most time-series databases today is that they require you to silo your time-series data within a non-relational store with an unfamiliar query language and limited connectors to the outside world. On the other hand, TimescaleDB is built on the open standard of SQL, and easily integrates into the rest of your software and data infrastructure.
As a result, businesses large and small all over the world today trust TimescaleDB for powering mission-critical applications, including: industrial IoT data analysis, IT and network monitoring systems, financial systems, in-application metrics, geospatial asset tracking, and more. Production deployments include Altair, Charter, Comcast, Cray, Hexagon Mining, LAIKA, Nutanix, SoFi, Transferwise, LaunchDarkly, Consensys, and many more.
We are excited to have found exceptional investors who share our vision and have joined alongside us to support this foundational technology and scale this business.
"Driven by the needs of modern web and IoT-scale time data, time-series databases are in the process of blossoming from a niche technology to a mainstream product category, equal in importance to transactional and analytic databases. But before TimescaleDB, time-series databases were making unfortunate trade-offs that damaged their utility as a general purpose application platform. The enthusiasm we've seen for Timescale among its open source community, as well as its rapid adoption rate, make us extremely excited to back both the technology and this incredibly talented team."
- Michael Mullany, General Partner, Icon Ventures
Capitalizing on significant community momentum
Two years ago, we started working on TimescaleDB to scratch our own itch and build the first time-series database that scaled and supported full SQL, engineered on top of PostgreSQL.
Since then, the developer response to TimescaleDB has exceeded what we expected, with over 1 million downloads, 6,500+ GitHub stars, and ~2,000 members in our Slack community. TimescaleDB is now the most requested extension on Amazon RDS, and the most requested feature on Azure PostgreSQL.
"As an animation studio, we have render farms, workstations, virtual machines, and lots of miscellaneous devices producing large amounts of data. We could foresee that our internal database, InfluxDB, was not scalable nor resource-efficient for this workload. Since we are huge fans of PostgreSQL, TimescaleDB was a natural solution. We've moved many of our existing workloads to TimescaleDB, and have even moved all of our long-term software licensing data from InfluxDB to TimescaleDB. The transition allowed us to retire complex internal applications and immediately allowed us to correlate licensing data with farm usage. We are very pleased with TimescaleDB's ability to serve as a sophisticated database system that does not require much manual servicing."
- Mahlon Smith, Senior Technologist, LAIKA
It is this growth that led us to opportunistically raise this Series A1 round, just one year after our previous financing. This latest round of financing allows us to add fuel to the fire, accelerate our growth, add to our growing team, and aggressively tackle what we believe is a fundamental problem for the future of data and computing.
"The market has been hungry for a database that can address this ubiquitous need for time-series data with a standard SQL interface running on a proven database engine. Timescale has delivered against this promise, and the growth since our investment captured the attention of a number of investors. We are excited for this new financing and how it allows us to scale this foundational technology and company."
- Peter Fenton, General Partner, Benchmark
Building a self-sustaining business
Today we are also excited to share TimescaleDB 1.2, which is our first release to include open-source, community, and enterprise features. More about these features are in this separate blog post.
Our new enterprise tier includes automated data lifecycle management capabilities critical for production workloads in order to ensure efficient resource utilization and optimized query performance.
Purchasing an enterprise subscription also unlocks additional services, including:
- Expert help with schema design and query optimizations
- Production deployment assistance
- Enterprise-level SLAs
- Warrants and indemnifications
To sign-up for a free trial of enterprise features, or to learn more about our enterprise offering, please visit our pricing page.
While our past two years have seen significant growth and development in our product, company, and community, we expect 2019 to be even bigger. This year, we’re actively developing scale-out clustering, adding even more open-source, community, and enterprise-grade features, and bringing TimescaleDB to a cloud near you. And we’re planning more events to meet with our community face-to-face.
If our mission resonates with you, then please join our community. If you are just getting started, please download TimescaleDB and give it a whirl. Then join our Slack channel and let us know how we can help. And if you’d like to be a part of a hungry, humble, hard-working team hustling to solve a huge problem, then please say hi.
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