You can integrate your Timescale Cloud service with third-party solutions to expand and extend what you can do with your data.
A Timescale Cloud service is a PostgreSQL database instance extended by Timescale with custom capabilities. This means that any third-party solution that you can integrate with PostgreSQL, you can also integrate with Timescale Cloud. See the full list of PostgreSQL integrations here.
Some of the most in-demand integrations for Timescale Cloud are listed below, with links to detailed integration steps.
| Name | Description |
|---|---|
| Azure Data Studio | Query, manage, visualize, and develop databases across SQL Server, Azure SQL, and PostgreSQL. |
| DBeaver | Connect to, manage, query, and analyze multiple database in a single interface with SQL editing, visualization, and administration tools. |
| pgAdmin | Manage, query, and administer PostgreSQL databases through a graphical interface. |
| psql | Run SQL queries, manage databases, automate tasks, and interact directly with PostgreSQL. |
| qStudio | Write and execute SQL queries, manage database objects, and analyze data in a user-friendly interface. |
| PostgreSQL | Access and query data from external sources as if they were regular PostgreSQL tables. |
| Name | Description |
|---|---|
| Amazon Cloudwatch | Collect, analyze, and act on data from applications, infrastructure, and services running in AWS and on-premises environments. |
| Datadog | Gain comprehensive visibility into applications, infrastructure, and systems through real-time monitoring, logging, and analytics. |
| Grafana | Query, visualize, alert on, and explore your metrics and logs. |
| Prometheus | Track the performance and health of systems, applications, and infrastructure. |
| Tableau | Connect to data sources, analyze data, and create interactive visualizations and dashboards. |
| Name | Description |
|---|---|
| Terraform | Safely and predictably provision and manage infrastructure in any cloud. |
| Name | Description |
|---|---|
| Amazon SageMaker | Build, train, and deploy ML models into a production-ready hosted environment. |
| Apache Airflow | Programmatically author, schedule, and monitor workflows. |
| AWS Lambda | Run code without provisioning or managing servers, scaling automatically as needed. |
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