Blue Sky Analytics is a big data and AI company that uses geospatial data to monitor environmental conditions. They are building the “Bloomberg for Environmental Data” to drive sustainable decision-making and safeguard the global market from climate-change-induced threats. To do this, Blue Sky Analytics collects geospatial and time-series data to create continuous high-resolution datasets to monitor pollution levels, water quality, emissions, fires, changes in soil composition, and more.
Previously, Blue Sky Analytics used MySQL for time-series data management—but this required them to use an ad hoc approach (i.e., null filling using multiple cron jobs) to query data and became increasingly difficult as they grew. They needed a solution that provided advanced analytics, scalable architecture, SQL support, and geospatial data capabilities. All of these requirements led them to choose Timescale.
Moving to Timescale has allowed Blue Sky Analytics to utilize several built-in functions, such as gap filling, time bucketing, and continuous aggregations, that perform the heavy lifting on the management side. Additionally, since Timescale works well with PostGIS (a PostgreSQL extension specifically designed to handle geospatial data), the Blue Sky Analytics team can create geospatial JOINs, perform time slicing, and implement null filling more effectively.
Timescale has been greatly beneficial to us and accelerated our development process. For us, the greatest advantage of using Timescale Cloud is that it enables us to do spatial-temporal queries with ease, which saves us a huge amount of time when we analyze data. No more reading 100s of files or writing custom scripts for each analysis – we just dump it into Timescale and query using SQL.
Kshitij Purwar, CTO at Blue Sky Analytics