TimescaleDB System Properties

Timescale is the creator of TimescaleDB, the first open-source time-series database to offer the reliability, flexibility, query power, ease-of-use, and scalability that today's software applications, data analytics infrastructure, and complex systems require. 

All data is time-series data, and Timescale offers cloud-hosted, self-managed, and open source versions of TimescaleDB to help developers and data teams across all industries and use cases store, monitor, and analyze their time-series data (common scenarios include: DevOps, IT Monitoring, and IoT). 

Built on PostgreSQL and supporting full SQL, TimescaleDB works with the tools and skills teams already have – and TimescaleDB has an active community of developers and robust technical resources for ongoing support and education.

TimescaleDB at-a-glance

Supports full SQL and JOINS

TimescaleDB natively supports full SQL and adds specialized functions (and query optimizations) for working with time-series data. Support for SQL, a query language your developers and business analysts already know, means that teams don’t have to spend time learning a "SQL-like" syntax or custom query language. 

This also means JOINs with metadata are possible, so you simplify your stack and eliminate data silos between their relational and non-relational databases. More specifically, you’re able to consolidate time-series data (stored in hypertables) with the relational metadata, such as customer data, that gives it meaning (standard tables).

Looks and feels like PostgreSQL 

TimescaleDB scales PostgreSQL in ways previously reserved for non-relational systems and optimizes for fast ingestion on big data. In order to scale far beyond their relational counterparts, non-relational systems sacrifice query power and flexibility. 

TimescaleDB is the best of both worlds: ingests millions of data points per second; scales tables to 100s of billions of rows; and returns quick responses to complex queries.

Works with the entire PostgreSQL ecosystem and offers full extensibility

With its roots in PostgreSQL, TimescaleDB inherits an operational interface familiar to developers, 20+ years of reliability work, and ability to integrate with PostgreSQL extensions. This includes PostGIS for geo-temporal analytics and building a data pipeline with other open-source tools like Kafka, Grafana, Prometheus, and many more.

If it works with PostgreSQL, it works with Timescale.

Optimizes storage and query performance 

With built-in features like native compression, data retention policies, and real-time aggregates, you can apply best-in-class compression algorithms (internal benchmark reports show 90%+ lossless compression rates), execute queries against pre-calculated results (v. querying all underlying raw data), and automatically drop data as it ages. 

For more information about which TimescaleDB version is right for you and to get started, please view our product detail page, documentation, and join us in our active Slack Community.

TimescaleDB is purpose-built to scale and handle time-series data workloads and is intentionally designed as a PostgreSQL extension. 

Additionally, with fully managed (cloud), self-managed, and open source versions, developers can opt for the solution that works for their needs.

TimescaleDB is a category-defining time-series database for many reasons, including features like:

• Native support for ANSI SQL, including JOINs with relational metadata that can be stored right in standard PostgreSQL tables
• time_bucket and gap_fill, allowing users to query data at any time interval (v. SQL limitations) and choose how they solve for NULL values and missing data
• GIS support (via PostGIS, another PostgreSQL extension) to roll your own geo-temporal database
• Support for ACID operations and transactions 
• Secondary indexes, including composite indexes
• Secondary JOINs
• Transparent resource utilization and performance (e.g., many options for administration and tooling, offers the ability to run EXPLAIN, etc.)
• Native compression and real-time aggregation capabilities to reduce data stored on disk, while still allowing users to analyze historical data
• Automated data retention policies to remove the burden of manual data lifecycle management
• Integration with Grafana and Prometheus for data visualization and long-term data storage (roll your own IT Monitoring stack)

TimescaleDB combines the reliability and tooling of PostgreSQL with enterprise-grade security, production-ready SLAs, and community-based and professional support services. 

TimescaleDB is also flexible and customizable, supporting thousands of extensions, products, and integrations critical for data analysis, including: Tableau, Looker, Mode, Grafana, PowerBI, Apache Kafka, RabbitMQ, Apache Spark, Prometheus, Zabbix, JDBC/ODBC, Telegraf, and more.

IoT, Industrial Machine, and Sensor Data

Various industrial IoT use cases across manufacturing, mining, oil & gas, energy, retail, healthcare, and more.

Customer and Internal-facing SaaS applications and Data Pipelines

Surface time-series metrics to end-users, or use to power internal applications, business operations, and data intelligence (Machine Learning pipelines).

DevOps and Infrastructure Monitoring

DevOps and infrastructure monitoring, especially custom initiatives, where siloed metrics are a concern and metadata and trend analysis is important.

Financial Information

Financial information and services across segments such as market research, commodities, and cryptocurrencies.

Comcast, Cray, Cree, DNSFilter, LAIKA, Nutanix, Sakura Internet, Schneider Electric, TransferWise, and many more.

With over 8.5K GitHub stars and growing, 1.8 million+ downloads, and an active Slack community, TimescaleDB is trusted by developers and teams around the world.

TimescaleDB comes in 3 versions, including a fully managed Cloud-hosted solution, licensed under Apache 2 and Timescale License (TSL).

To learn more and find the version that’s best for you, please see our product matrix here (and read more about our support options).