Power BI Embedded using Databricks sql warehouse or data lake?

Hi, Don-Bot 

The following are the characteristics and applicable scenarios of these two solutions:

Azure Databricks SQL Warehouse supports serverless architecture with the following features:

1.Fast startup time (usually between 2 and 6 seconds).

2.Rapidly expand to obtain more computing resources to maintain low latency.

3.Query capacity close to hardware limits, not virtual machines.

4.Rapid scaling to minimize costs and provide optimized performance and resources when demand is low.

5.Photon engine: The native vectorized query engine on Databricks accelerates existing SQL and DataFrame API calls and reduces overall workload costs.

6.Predictive IO: A set of features used to accelerate selective scan operations in SQL queries.

7.Intelligent workload management (IWM): By using AI prediction and dynamic management technology, ensure that workloads get the correct number of resources quickly.

Applicable scene:

Ideal for large data workloads, 1TB or more.

For query needs that require flexibility, fast startup, intelligent resource management, and low cost, choose serverless SQL Warehouse.

The features of Azure Data Lake are as follows:

1.Azure Data Lake Storage Gen2 is a large-scale data lake storage solution suitable for storing and analyzing large amounts of data.

2.Direct connection: You can directly connect Power BI to Azure Data Lake to query and analyze data.

3.Unlimited scalability: Azure Data Lake Storage Gen2 can easily expand to TB and PB levels of data, suitable for large-scale data storage needs.

4.Cost-effectiveness: It provides cost-effective storage, and you only pay based on the storage resources and computing resources actually used12.

5.Raw data storage: Data Lake stores data in its original, unconverted state without losing any information. This is useful for data exploration in big data environments, as you may not know in advance what insights exist in the data.

6.Flexibility: Data Lake can store unstructured and semi-structured data and is more flexible than traditional data warehouses.

7.Self-service queries: Users can explore data and create their own queries without being restricted by predefined schemas.

Applicable scene:

Suitable for scenarios where large amounts of unstructured data need to be stored and analyzed. If you already have Azure Data Lake storage and want to avoid data imports and query the data directly, Azure Data Lake is a good choice.

If you want to learn more about these two solutions, you can click on the relevant links below for more detailed information:

SQL WareHouses: https://learn.microsoft.com/en-us/azure/databricks/compute/sql-warehouse

Microsoft Power BI and Lakehouse: https://techcommunity.microsoft.com/t5/analytics-on-azure-blog/power-up-your-bi-with-microsoft-power-bi-and-lakehouse-in-azure/ba-p/3816771

Data Lake Storage: https://www.trustradius.com/products/azure-data-lake-storage/reviews?qs=pros-and-cons#comparisons

Data Lake: https://learn.microsoft.com/en-us/azure/architecture/data-guide/scenarios/data-lake

Data Lake benefits: https://blog.nextpathway.com/azure-data-lake-benefits

Remember that each solution has its trade-offs, and the best choice depends on your specific requirements, existing skills, and budget. Consider factors like data volume, performance, ease of use, and integration capabilities. 

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Best Regards

Jianpeng Li

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