Data Platform Best Practices

John Miner

You now have an idea of what architectural pattern to aim for. Now what? You understand how to ingest data into your environment as well as transforming, enriching, and serving that data to the end consumer. How do you move your new solution from inception to development, and finally to production with ease?

In this last session of the Azure Data Engineering learning pathway, we will cover best practices that the data engineer should consider during the project life cycle. The following topics will be covered so that your project will be a complete success: project planning, deployment frameworks, object tagging, coding standards, data dictionary, data segregation, system alerting, capturing performance, growth planning and scaling compute.