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DATA ENGINEERING
The Case for Using Timeseries Databases
A brief introduction to the time-series databases — InfluxDB, TimescaleDB, and QuestDB
This article was last updated on 1 September 2021.
A plethora of new databases have evolved from relational databases based on specific business requirements and use-cases. From in-memory key-value stores to graph databases, from geospatial databases to time-series databases. All of these different types of databases serve a specific use where the general solution of using a relational database isn’t very efficient.
Although there are a lot of different types of databases, here we’re going to look at time-series databases — the databases required to handle time-series data.
Data that consists of successive measurements of something over a time interval is time series data.
With the modernization of financial trading and IoT's advent, the need for time-series databases is evident. Stock and cryptocurrency prices change every second. To measure this changing data and to perform analysis on that data, we need an efficient way of storing and retrieving data.
