Druid vs TimescaleDB: What are the differences?
Developers describe Druid as "Fast column-oriented distributed data store". Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations. On the other hand, TimescaleDB is detailed as "Scalable time-series database optimized for fast ingest and complex queries. Purpose-built as a PostgreSQL extension". TimescaleDB is the only open-source time-series database that natively supports full-SQL at scale, combining the power, reliability, and ease-of-use of a relational database with the scalability typically seen in NoSQL databases.
Druid belongs to "Big Data Tools" category of the tech stack, while TimescaleDB can be primarily classified under "Databases".
Druid and TimescaleDB are both open source tools. It seems that Druid with 8.32K GitHub stars and 2.08K forks on GitHub has more adoption than TimescaleDB with 7.28K GitHub stars and 385 GitHub forks.
According to the StackShare community, Druid has a broader approval, being mentioned in 24 company stacks & 12 developers stacks; compared to TimescaleDB, which is listed in 15 company stacks and 3 developer stacks.
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