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The Internet of Things (IoT) and the sensor devices that power it are designed to be producers of massive amounts of data. Much of the data being produced is focuses on the changes of data over time.
Time series databases provide a powerful tool for monitoring how data sets change over time. Changes in standard databases are made by UPDATEs to the database where new data values overwrite previous values. In time-series databases, rather than doing UPDATEs, data is continually aggregated with INSERTs that timestamp each row of data collected.
Time-series databases focus on optimizing the management of large amounts of time-stamped data. While standard relational databases can perform the same task, time-series databases are much more performant because they’ve been optimized for analyzing time-series data.
Common data sets managed by time-series datbases include “logs, network data, sensor data, financial price data, and user click and event tracking.
Ajay Kulkarni, Timescale CEO, said that “this practice of recording each and every change to the system as a new, different row is what makes time-series data so powerful. It allows us to measure change: analyze how something changed in the past, monitor how something is changing in the present, predict how it may change in the future.”
Kulkarni said that “time-series data accumulates very quickly. (For example, a single connected car will collect 4,000 GB of data per day.) Normal databases are not designed to handle that scale. Relational databases fare poorly with very large datasets; NoSQL databases fare better at scale, but can still be outperformed by a database fine-tuned for time-series data. In contrast, time-series databases handle scale by introducing efficiencies that are only possible when you treat time as a first class citizen. These efficiencies result in performance improvements, including higher ingest rates, faster queries at scale. and better data compression.”