Fast and scalable azure data lake analytics!
July 26, 2022

Fast and scalable azure data lake analytics!

Anonymous | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User

Overall Satisfaction with Azure Data Lake Analytics

My primary use case in using and investigating Azure Data Lake Analytics was in comparing how it fulfilled aggregate build in our data lake environment compared to how Databricks solved for our initial use cases. At the time, in building out a raw, refined, and curated zone before landing data in a warehouse multiple bidirectional transformation processes run between the Refined to Curated and then ultimately Warehouse layer. Key was scale, cost, and performance as compared to what can be done in processing aggregates via Databricks and opposite that ELT to a warehouse like Snowflake instead of load from lake to Microsoft Synapse.


  • Process large data transformation jobs using pretty much any language needed.
  • Native integration with Azure storage.
  • Top notch security that fulfills all audit needs.
  • Easy to consolidate enterprise data under one location - Single source of truth.


  • Learning curve and professional services were the only reason why we got up and running quickly - Not a downside but a need to know.
  • Uniqueness to run on a per job basis
  • Security and support services (professional services) are the best in the industry.
  • Has allowed us to reduce compute expenses by enabling better synchronization of workloads and user usage.
  • Ease of data virtualization or rather connection of data sources from multiple locations.
Compared to Databricks which we have fully implemented and all teams use, Azure Data Lake Analytics was first pushed on our engineering team from the Data Science group pretty much from familiarity. Once we did a proof of technology, we found it to natively have the better scale and performant access for users needing access to data and building data aggregations from many sources. The bonus as well as how well it plays with very large data sets, and the service integration with other Azure products makes life easy for engineers and security professionals. From a cost perspective, we found and I'm sure you will as well that our enterprise pricing made it very competitive compared to competitors.

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Databricks Lakehouse Platform (Unified Analytics Platform), Confluent Platform, Azure Bot Service (Microsoft Bot Framework), Azure Blob Storage, Pypestream,
For us we have an enterprise of SQL users at all skill levels, and this product is very SQL friendly and extremely fast in creation of data aggregates and analysis. If you are an Azure storage user, considering using Lake Analytics over top of your blob or any other storage just adds complementary services and functions native to your existing architecture.


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