January 2019 Summaries

Icon Ventures has led a new round of funding for Timescale, a company developing the foundational technology for businesses storing, analyzing, and acting on time-series data, with $15 million in Series A1 financing. This brings the total funding to over $31 million, as Timescale continues to grow its presence in the accelerating time-series data market. The company has also released a new version of its open-source database, TimescaleDB 1.2, which includes community features and enterprise features, complementing the existing open-source core. With this release, TimescaleDB is now available with a free, open-source license and a paid, commercial license, allowing businesses to choose their level of investment in the technology. The company's growth has been driven by its ability to help organizations analyze the past, monitor the present, and predict the future through time-series data, which is becoming increasingly important as storage becomes cheaper and computing becomes more powerful. TimescaleDB is now being used by a wide range of businesses, including industrial IoT data analysis, IT and network monitoring systems, financial systems, in-application metrics, geospatial asset tracking, and more. The company's growth has been fueled by its community momentum, with over 1 million downloads, 6,500+ GitHub stars, and ~2,000 members in its Slack community. With this new funding, Timescale plans to accelerate its growth, add fuel to the fire, and tackle what it believes is a fundamental problem for the future of data and computing.

The new time-series analytic tools included in TimescaleDB 1.2 enable users to write more efficient and readable queries for time-series analysis using SQL. These tools include `time_bucket_gapfill`, `interpolate`, and `locf` functions, which work together to create contiguous, ordered time buckets with gap filling capabilities. The `time_bucket_gapfill` function guarantees a contiguous set of time buckets by providing additional rows in place of gaps, ensuring the returned rows are in chronological order and contiguous. The `locf` function imputes missing values by assuming the previous known value, while the `interpolate` function performs linear interpolation between the previous and next known value. These techniques can be combined as needed in a single time bucketed query, and best practices for using them include choosing the right approach based on data assumptions and analytical goals. The new tools are not only more readable but also less error-prone, more flexible with regard to grouping, and faster to execute than previous methods.

Three years after its launch, TimescaleDB has achieved significant milestones, including over 1 million downloads, 15 releases, production deployments worldwide, and the launch of the TSBS. The company secured $16M in Series A funding, hiring a distributed team to accelerate development, and released version 1.0, marking it as enterprise-ready. Additionally, TimescaleDB introduced the Time Series Benchmark Suite, a collection of Go programs for benchmarking time-series databases, and partnered with various ecosystem providers such as Prometheus, Grafana, and Hasura. The company is now preparing for 2019, planning moonshots including scale-out clustering, adding enterprise-grade features, and bringing TimescaleDB to cloud platforms.