Modernizing ETL with Azure Data Lake: Hyperscale, multi-format, multi-platform, and intelligent
Increasingly, customers looking to modernize their analytics needs are exploring the data lake approach. They are challenged by poorly-integrated technologies, a variety of data formats, and inconvenient data types. We explore a modern ETL pipeline through the lens of Azure Data Lake. This approach allows pipelines to scale to thousands of nodes instantly and lets pipelines integrate code written in .NET, Python, and R. This degree of extensibility allows pipelines to handle formats such as CSV, XML, JSON, Images, etc. Finally, we explore how the next generation of ETL scenarios are enabled by integrating intelligence in the data layer in the form of built-in cognitive capabilities.