Azure Data Lake Analytics vs traditional Data Virtualization tool


  • Hello.

    Does anybody have any input on comparing Azure Data Lake Analytics with traditional data visualization tools, such as Denodo? 

    ADLA looks very attractive, but the number of connections appears limited. I'd like to be able to connect to all sorts of sources, such as Salesforce, Workday, various web services, etc..., to bring in and federate the data.

    Is that possible?



    Monday, March 4, 2019 8:35 PM

All replies

  • Hi Boris,

    Azure Data Lake Analytics is an on-demand analytics job service that simplifies big data. Instead of deploying, configuring, and tuning hardware, you write queries to transform your data and extract valuable insights. The analytics service can handle jobs of any scale instantly by setting the dial for how much power you need. You only pay for your job when it is running, making it cost-effective.

    Azure Data Lake Analytics works with Azure Data Lake Store for the highest performance, throughput, and parallelization and works with Azure Storage blobs, Azure SQL Database, Azure Warehouse.

    For more details, refer “What is Azure Data Lake Analytics?”.

    The Denodo Platform for Azure integrates all of your Azure data sources – SQL Data Warehouse, Cosmos DB, SQL Server databases, HDInsights, Azure Data Lake, and more – and your SaaS applications, such as Salesforce, and Microsoft Dynamics to deliver a standards-based data gateway making it quick and easy for users of all skill levels to access and use your cloud-hosted data. 

    For more details, refer “Denodo platform for Azure” and “Denodo – Data Sources

    A final verdict, “Denodo has many data sources compared to Azure Data Lake Analytics”.

    Hope this helps.

    Tuesday, March 5, 2019 11:11 AM
  • Hi Boris,
    Just checking in to see if the above answer helped. If this answers your query, do click “Mark as Answer” and Up-Vote for the same. And, if you have any further query do let us know.

    Friday, March 8, 2019 5:47 AM