A well-crafted Data Lakes is essential within an organization as the central data repository. In this session, we will discuss methods for creating and maintaining a data lake in Azure which will meet the needs of many different users. Whether you want to use the data for analysis, reporting or machine learning, we will discuss how to accommodate these different use cases and build a data lake that can meet them. Learn how to provide database functionality on the data, create machine learning environments, provide raw storage, and archive functionality. The session demos will show the best practices for creating and maintaining the data lake to provide organization's the structure they need to maintain and utilize the data with Azure Synapse Analytics. We will also discuss data ingestion and organization strategies to develop a data lake which can support use cases from Power BI reporting to data science structures while minimizing storage costs and providing a framework for scalability and long term data storage. This session will provide methods for creating a maintainable structure and use cases for different scenarios to access the data.

You will learn:

• An understanding of how to create a well-organized data lake using Azure Data Lake Gen2 which will provide the ability to provide data for a number of different types of users.
• Different methods of ingestion and long term maintenance processes to ensure that data is optimally stored in the appropriately sized Azure resources with the goal to minimize Azure spend while providing the appropriate level of functionality.
• Understand how to use a data lake for Reporting with Power BI as well as machine learning functionality using Azure Synapse.