For some data problems, you may be more concerned with the state of data at a particular point. A ticket is booked, or it’s not. How many poetry submissions were made to the contest? This is relational data. For other problems, you’re concerned with the change in data over time. Solar energy consumption, for example, or price behavior. This is time-series data.
TimescaleDB resembles a traditional postgreSQL database, but is supercharged for time-series data. TimescaleDB has queries that are 10x faster, is optimized for time-series and advanced time-series analytics, has automated continuous aggregations, columnar storage, and uses best-in-class algorithms and memory efficient structures to compress your data so you can store more at a much cheaper price. And for your questions that are not time-series dependent, TimescaleDB is still an efficient and cost effective relational database.
In this episode we talk to Mike Freedman, Co-Founder and CTO of TimescaleDB.
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