| --- |
| title: "Introduction" |
| description: General introduction of Apache DevLake |
| sidebar_position: 1 |
| --- |
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| <head> |
| <title>Introduction to Apache DevLake and Implementing DORA Metrics</title> |
| <meta name='description' content={'Discover how to implement DORA metrics for your software development process. Improve your engineering performance with our open-source platform.'}/> |
| <meta name='keywords' content={'DORA Metrics, Open-Source DORA, DORA Metrics DevOps, Implement DORA Metrics, DevOps DORA Metrics'}/> |
| </head> |
| |
| ## What is Apache DevLake? |
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| Apache DevLake (Incubating) is an open-source dev data platform that ingests, analyzes, and visualizes the fragmented data from DevOps tools to extract insights for engineering excellence, developer experience, and community growth. |
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| Apache DevLake is designed for developer teams looking to make better sense of their development process and to bring a more data-driven approach to their own practices. You can ask Apache DevLake many questions regarding your development process. Just connect and query. |
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| ## What can be accomplished with DevLake? |
| - Unified data integration: Bring together DevOps data from across the Software Development Life Cycle (SDLC) with our [standard data model](https://devlake.apache.org/docs/DataModels/DevLakeDomainLayerSchema/). |
| - Out-of-the-box insights: Access key engineering metrics through intuitive, use-case driven dashboards. |
| - Customizable: Extend DevLake to align with your unique needs, adding [data sources](https://devlake.apache.org/docs/next/Overview/SupportedDataSources/), [metrics](https://devlake.apache.org/docs/next/Metrics/), and [dashboards](https://devlake.apache.org/livedemo/EngineeringLeads/DORA/) as required. |
| - Industry standards implementation: Use DevLake to apply recognized [DORA metrics](https://devlake.apache.org/docs/DORA/) to optimize DevOps performance. |
| - Create a thriving culture: DevLake is centred on healthy practises that may help teams adopt and build a practical data-driven culture. |
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| ## How do I use DevLake? |
| ### 1. Setting-up DevLake |
| - Create the proof of concept of Apache DevLake for your use-case by installing in your local machines via Docker Compose: [Docker Compose setup](../GettingStarted/DockerComposeSetup.md) |
| - Alternatively, if you're powered by Kuberenetes, then check out the [Helm setup](../GettingStarted/HelmSetup.md). |
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| ### 2. Configuring Data Source |
| - Once Installed, you can start configuring DevLake with supported [data sources](https://devlake.apache.org/docs/next/Overview/SupportedDataSources) like GitHub, GitLab, Jira, Jenkins, BitBucket, Azure DevOps, SonarQube, PagerDuty, TAPD, ZenTao, Teambition, and we're extending our support to many other tools, feel free to check out the official roadmap. |
| - If your CI / CD tool is not supported yet, then you may use the [Webhooks](https://devlake.apache.org/docs/Plugins/webhook/) feature. |
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| ### 3. Creating your Project |
| - After connecting the data-source, a "Project" makes sure that you're all set for execution. A four-step process allows you to play DevLake and visualize pre-built dashboards |
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| ### 4. Checking the results (Validation & Customization) |
| - After configuring the project, you can take a look at our pre-built dashboards in Grafana. |
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| - Dashboards can be tweaked according to the goal and intent of [metrics](https://devlake.apache.org/docs/Metrics/) or create your own using SQL. You can also check out [Domain Layer Schema](https://devlake.apache.org/docs/Metrics/) - DevLake's standard data model. |
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