If you’re exploring Microsoft’s Big Data stack, you’ll run into Azure Data Lake Analytics (ADLA) in older tutorials. Here’s the quick, search-friendly update:

ADLA was Microsoft’s on-demand analytics service that let teams run U-SQL scripts at cloud scale and pay per job, without managing clusters. Microsoft retired ADLA on February 29, 2024; therefore, new implementations should target current platforms, such as Azure Synapse or Spark engines on Azure.

What ADLA was (and why it mattered)?

ADLA abstracted the heavy lifting of distributed computing. You wrote U-SQL, a language blending SQL with C#, submitted a job, and ADLA scaled resources behind the scenes. Pricing was consumption-based (per-second billing), which made bursty workloads cost-efficient. For storage, ADLA commonly worked with Azure Data Lake Storage (ADLS), a cloud data lake designed for massive, multi-format data with hierarchical namespaces and analytics-friendly semantics.

The current state- what to use instead?

Since ADLA is retired, Microsoft recommends using Azure Synapse Analytics (serverless SQL, dedicated SQL, or Spark) or Spark engines on Azure for scalable transformations and analytics on ADLS Gen2.

Learn more: What is Data Analytics?