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.
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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.
InfluxData is the creator of the leading time series database, InfluxDB. The open-source software helps developers and enterprises alike to collect, store, process and visualize time series data and to build next-generation applications — providing monitoring and insight on IoT, application, system, container, and infrastructure metrics — quickly and easily without complexities or compromises in scale, speed or productivity. For more information regarding InfluxData's products, visit www.influxdata.com/products or contact sales at firstname.lastname@example.org Founded in 2012 and based in San Francisco, InfluxData is Series D-funded. InfluxData is first and foremost an open source developer-focused company; committed to the collaboration and innovation of the open source community, and has a strong customer base with growing revenue. Follow InfluxData on: -Twitter: https://twitter.com/InfluxDB -Facebook: www.facebook.com/influxdb -LinkedIn: hwww.linkedin.com/influxdb -Github: github.com/influxdata
QuasarDB is a high-performance, distributed time series database that seamlessly combines in-memory capabilities with reliable storage. It’s built on a vertical approach with a single software package that provides storage, distribution, standardization and analysis. Data can be ingested at several hundred millions entries per second and is available immediately for querying. Data is processed in real time and distributed transparently to disks and memory; and is easily accessible using a query language such as SQL.
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.
IRONdb is a highly resilient time-series database designed to scale to billions of events per day and trillions of datapoints for regression. IRONdb allows organizations to handle a massive amount of data - reliably, efficiently and cost-effectively - to pull out the operational insights needed to run their businesses and get ahead of the competition.
Offload non-trivial operational tasks for metrics storage such as replication, backup and seamless scalability to VictoriaMetrics. Query all the metrics your Prometheus instances collect via a single Prometheus-compatible datasource. Fast query engine. It excels on heavy queries over thousands of metrics with millions of metric values. VictoriaMetrics supports native PromQL. There is no need in learning yet another query language.