Most Prominent Time Series Databases For Data Scientists

From self-driving cars and financial trading algorithms to accessing IoT data and monitoring sophisticated applications, require blocks of data that can critically measure the changes that happened over time — called time-series data. Though the concept started with the financial application, time-series databases have now become a de facto for enterprises to store, manage, mine and monitor real-time data. 

As the name suggests, time-series databases are the software systems optimised for time-stamp or time-series data. In an era where enterprises are looking to create smarter products to keep up, it is important now more than ever to choose the right time-series database.

Here, we will list down eight time-series databases software for businesses to check out in 2021.

Also Read: What Is A Time Series GAN?


InfluxDB is a time-series database platform designed to empower developers to build IoT, analytics and monitoring software. Built specifically to handle the massive volumes of time-stamped data produced by sensors, applications and infrastructure, InfluxDB leverages powerful APIs and tools to assist developers with their application building process. InfluxDB focuses on open source and open standards with seamless product integration with 200+ plugins.

Key Features:

  • Enterprise-grade security allowing developers to build anywhere — edge, cloud or on-premise.
  • InfluxDB high-performing time-series database capable of ingesting millions of data points per second.
  • InfluxDB allows developers to start from the UI or look under the hood to access raw code and the API. 

Know more here.


Timestream is a fast, scalable, and serverless time-series database by Amazon, explicitly designed for IoT and operational applications. This time-series database makes it easy to store and analyse trillions of events faster and less the cost of relational databases. Amazon Timestream’s purpose-built query engine lets developers access and analyse recent and historical data together. Alongside, it has built-in time-series analytics functions, that help developers identify trends and patterns in the data in near real-time.

Key Features:

  • Timestream offers up to 1,000x faster query performance with 1/10th of relational databases’ cost.
  • Timestream is serverless for developers to focus on building their applications. 
  • It ensures to encrypt time series data, whether at rest or in transit. 

Know more here.


TimescaleDB is a fully managed or self‑hosted open-source relational database for time-series data. A multi-cloud time-series database service, TimescaleDB is a fast, easy, and reliable way to store all your time-series data. It helps developers to identify and predict performance anomalies and service outages for future capacity needs.

Key Features:

  • A multi-cloud time-series database service powered by TimescaleDB and PostgreSQL.
  • Fully managed on AWS, Azure, or GCP in 75+ regions.
  • Spend less with 94 – 97% compression rates from best-in-class algorithms and other performance improvements.

Know more here.


QuestDB claims to be the fastest open-source time-series database. A relational column-oriented database designed for real-time analytics on time series and event data, QuestDB uses the SQL language with some extensions for time series. It uses heavy parallelised vectorised execution, SIMD instructions, and many low-latency techniques for faster performance.

Key Features:

  • SIMD optimised analytics and vectorised queries execution.
  • It is a relational model for time series, where the data is stored in chronological order.
  • Postgres compatibility with unlimited sub-queries

Know more here.


Another open-source and community-driven time series database software, Prometheus fundamentally stores all data as time series provides a functional query language called PromQL. Prometheus has multiple modes for visualising data such as built-in expression browser, Grafana integration and a consulate template language. Further, it stores time series in memory and on-local disk in an efficient custom format.

Key Features:

  • Prometheus’s local time-series database stores data in a custom, highly efficient format on local storage.
  • Comes with a flexible query language, PromQL, to leverage this dimensionality
  • Prometheus is a multi-dimensional data model with time series data identified by metric name and key/value pairs

Know more here.


RedisTimeSeries streamlines and eases the use of Redis for time-series use cases like IoT, stock prices, and telemetry. With RedisTimeSeries, developers can ingest and query millions of samples and events at the speed of light. Advanced tooling such as downsampling and aggregation ensures a small memory footprint without impacting performance. It also uses various queries for visualisation and monitoring with built-in connectors to popular tools like Grafana, Prometheus, and Telegraf.

Key Features:

  • The easiest and most efficient way to store time-series data in Redis.
  • Works well with RedisAI and RedisGears, enabling anomaly detection and predictive maintenance.
  • Rapidly integrates with tools like Grafana, Prometheus, StatsD, and Telegraf to monitor, visualise, and migrate data.

Know more here.


Apache Druid is an open-source distributed data store that combines ideas from data warehouses, time-series databases, and search systems to create a high-performance real-time analytics database for a broad range of use cases. The software merges critical characteristics of each system into its ingestion layer, storage format, querying layer, and core architecture. It further stores and compresses each column individually, for developers to read the ones required for a particular query.

Key Features:

  • Column-oriented storage.
  • The software creates inverted indexes for string values for fast search and filter.
  • Druid comes with out-of-the-box connectors for Apache Kafka, HDFS, AWS S3, stream processors, and more.

Know more here.


Another free, open-source time-series database software tool, Graphite has been designed to monitor and analyse numeric time-series data such as computer systems’ performance. Graphite is an enterprise-ready monitoring tool that runs equally well on cheap hardware or cloud infrastructure. Released under the open-source Apache 2.0 license, Graphite is currently used by numerous large companies to monitor e-commerce services and plan for growth.

Key Features:

  • Store numeric time-series data, and render graphs of this data on demand.
  • Can be used to track the performance of the websites, applications, business services, and networked servers.
  • Graphite offers the simplest path for getting your measurements into a time-series database.

Know more here.

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Sejuti Das
Sejuti currently works as Associate Editor at Analytics India Magazine (AIM). Reach out at

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