I have a requirement like this:
- Source data will come from Azure SQL (master data) and Azure Cosmos Db (transactional data)
- Create a data model in Azure Databricks using Delta Lake
- Create visualizations in Power BI Embedded
I am very new to Delta Lake. So, pardon my noob questions.
My questions are:
- How should we create the data model (DWH)
- Should we use Direct Query or Import Data in Power BI to visualize the data
CodePudding user response：
How should we create the data model (DWH) To create the data model in Azure Databricks using Delta Lake, you can follow these steps:
Connect to your Azure SQL and Azure Cosmos DB databases using the appropriate connectors in Databricks. Use the connectors to read the data from the databases into DataFrames in Databricks. Use the DataFrame API in Databricks to clean, transform, and reshape the data as needed. Write the transformed data to Delta Lake tables in Databricks. Use the Delta Lake API in Databricks to perform operations such as merging, upserting, and deleting data as needed. Use the Delta Lake tables as the source for your Power BI visualizations. Should we use Direct Query or Import Data in Power BI to visualize the data It depends on your specific requirements and the nature of the data.
If you have a large amount of data and need to have real-time updates, you should use Direct Query. This way, the data will be queried directly from the Delta Lake tables in Databricks each time the report is loaded or a user interacts with it.
If you have a smaller amount of data and the data changes infrequently, then you can import the data into Power BI. This way, the data will be imported into the Power BI model and will be stored in the report's data model, which will make the report load faster and make it more responsive.
It's also important to note that with Direct Query, you may need to be mindful of the performance of your queries, since they will be executed each time a user interacts with the report, and that can affect the performance of the report.
In the end, it's a trade-off between performance, real-time updates, and ease of use. You'll have to evaluate the pros and cons of each method, and choose the one that works best for your needs.
When creating a data model in Delta Lake, there are a few guiding principles to consider:
Data Governance: Delta Lake is designed to provide data governance features such as data versioning, data lineage, and data auditing. It is important to ensure that these features are leveraged to maintain data quality, security, and compliance.
Data Integrity: When modeling the data in Delta Lake, it is important to consider the data integrity constraints and ensure that the data is stored in a way that ensures consistency and accuracy.
Data Normalization: Normalizing the data in Delta Lake can help to eliminate data redundancy and improve data quality. This can be achieved through the use of primary and foreign keys, and by breaking down complex data structures into simpler ones.
Data Denormalization: In some cases, it may be necessary to denormalize the data in order to improve performance. This can be done by creating a copy of the data in a different format, such as a star schema or snowflake schema.
Performance: The data model in Delta lake should be optimized for performance and to minimize data movement. This can be achieved by partitioning the data and by leveraging the columnar storage format of Delta Lake.
scalability: Delta Lake is built to handle large amounts of data, so it is important to ensure that the data model can scale with the data volume.
In general, whether to use a star schema or a snowflake schema is a design decision that should be based on the specific requirements of the data model. A star schema is often used for data warehousing and business intelligence use cases, while a snowflake schema may be more appropriate for analytical use cases.