Merits and Demerits of Handling Large Data set from Azure Data bricks in Integration with Power BI

Question

Hi,

I would like to know about what are the various factors to consider when handling Larger Data set from Azure Data bricks in Integration with Power BI.

The complete list of Merits and Demerits of Azure Data Bricks with Power BI.

Thanks

Answer 1

HI Pradeep1 S,

Thanks for reaching out to Microsoft Q&A.

Please find below the details:

MERITS

1. It provides a unified analytics platform that integrates seamlessly with Power BI, enabling data scientists to collaborate effectively
2. Supports security patterns, including native integration with AAD, Databricks table ACL's and masking and column level encryption
3. It can provide high performance for data processing and analytics
4. Supports real time data processing and analytics, which allows users to make decisions based on up-to-date data

DEMERITS

Complexity: Setting up and managing the integration between Azure Databricks and Power BI can be complex, requiring expertise in both platforms.

Cost: While the platform is cost-efficient for large-scale operations, smaller organizations might find the costs associated with Azure Databricks and Power BI integration to be high

Learning Curve: There is a steep learning curve for users who are not familiar with Apache Spark or the specific features of Azure Databricks

Performance Issues: In some cases, performance issues may arise due to the complexity of the data models or the volume of data being processed

1. Dependency on Azure Services: The integration heavily relies on other Azure services, which might lead to challenges if there are issues or changes in those services

Limitations

• The Azure Databricks connector supports web proxy. However, automatic proxy settings defined in .pac files aren’t supported.
• In the Azure Databricks connector, the Databricks.Query data source is not supported in combination with DirectQuery mode.
• The data that the Delta Sharing connector loads must fit into the memory of your machine. To ensure this, the connector limits the number of imported rows to the Row Limit that was set earlier.

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