Time series databases allow businesses to store time-stamped data. A company may adopt a time series database if they need to monitor data in real time or if they are running applications that continuously produce data. Some examples of applications that product time series data include network or application performance monitoring (APM) software tools, sensor data from IoT devices, financial market data, and a number of security applications, among many others. Time series databases are optimized for storing this data so that it can be easily pulled and analyzed. Time series data is often used when running predictive analytics or machine learning algorithms, enabling users to understand historical data to help predict future outcomes. Some big data processing and distribution software may provide time series storage functionality.
To qualify for inclusion in the Time Series Databases category, a product must:
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Amazon Timestream is a fast, scalable, fully managed time series database service for IoT and operational applications that makes it easy to store and analyze trillions of events per day at 1/10th the cost of relational databases. Driven by the rise of IoT devices, IT systems, and smart industrial machines, time-series data, data that measures how things change over time, is one of the fastest growing data types.
Azure Time Series Insights is a fully managed analytics, storage, and visualization service for managing IoT-scale time-series data in the cloud. It provides massively scalable time-series data storage and enables you to explore and analyze billions of events streaming in from all over the world in seconds.
kdb+ is a high-performance column-store database with a built-in expressive query and programming language, q. Used as a central repository to store time-series data within an enterprise, kdb+ supports real-time analysis of billions of records and fast access to terabytes of historical data.
ATSD is a distributed NoSQL database designed from the ground up to store and analyze time-series data at scale. Unlike most other databases, ATSD comes with a robust set of built-in features including Rule Engine, Visualization, Data Forecasting, Data Mining and more.
QuasarDB is a high-performance, distributed, time series database. QuasarDB has been designed to handle the most extreme time series use cases in financial applications. It seamlessly combines, in a single product, in-memory database capabilities with efficient, reliable long-term storage. Scalability is transparent, whether it's for very large and dense time series or millions of smaller ones.