Best practices for designing and building data platforms¶

Building a data platform is a complex and challenging task that requires careful planning and execution. By following the best practices for designing and building data platforms, businesses can ensure that their data is easily accessible, properly governed, and efficiently operated. This, in turn, can lead to better decision-making, improved productivity, and increased revenue. Whether you're just starting to build a data platform or looking to optimize an existing one, following these best practices can help you achieve your goals and drive business success.

Making your data available to project teams¶

To empower project teams to build data products, you need to provide a data exploratory environment and facilitate data discovery and governance.

Provide a data exploratory environment¶

Set up individual workspaces for data users and engineers within a larger logical data lake. Provide them with appropriate access to tools and environments for experimentation. For more details, check out MLOps: Experimentation.

Provide a data catalog to enable data discovery and governance¶

Users need a way to discover and explore existing datasets available to them, along with relevant business metadata, lineage, and governance controls. Having a data catalog is imperative to make your data products easily discoverable and properly governed. For more information, see Data Governance: Data Catalog.

Operationalizing the data platform¶

Efficiently operating a data platform requires automating deployments and testing, centralizing configuration, and monitoring infrastructure, pipelines, and data.

Automate deployments and testing¶

Add all artifacts required to build the analytical system to a source control system, such as Git. Have a safe and repeatable process to deploy changes with automated tests through dev, test, and production stages. For more details, check out DevOps for Data: Automate Deployments and Testing.

Centralize configuration¶

Maintain a central, secure location for sensitive configuration such as database connection strings that can be accessed by the appropriate services within the specific environment. For example, you can secure secrets in Azure Key Vault per environment, then have the relevant services query Key Vault for the configuration. For more information, see Azure Key Vault and Continuous Delivery: Secrets Management.

Monitor infrastructure, pipelines, and data¶

A proper monitoring solution should be in place to ensure failures are identified, diagnosed, and addressed in a timely manner. This includes monitoring the base infrastructure, compute, and pipeline runs, as well as continuously monitoring data quality post-deployment to production. For more information, see Data Quality: Data Quality and Data Observability: Monitoring and Logging.

Conclusion¶

When designing and building analytical systems, it's important to provide a data exploratory environment, facilitate data discovery and governance, automate deployments and testing, centralize configuration, and monitor infrastructure, pipelines, and data. By following these key learnings, you can make your data platform available to project teams and efficiently operate a data platform.