Refresh your Microsoft Fabric warehouse without specifying a date, using incremental updates for an enhanced user experience.

Incremental refresh without a date? No problem! Just create a meta table to track max IDs, use power query functions to incrementally load data, and execute store procedures in your pipeline. Voila! Your warehouse is refreshed incrementally without a date 🔄💻 #DataWarehousingWin

Setting Up the Meta Table and Store Procedure

In this video, we’ve been having conversations about incrementally loading warehouses and lake houses in Microsoft Fabric. Alex Powers wrote a blog post on using data flows and pipelines effectively for incremental loading. To begin, we need to create a table to store the metadata for the incremental loading process. The table should store the table name and the maximum ID for incremental loading.

Table NameMax ID

In addition to creating the meta table, a stored procedure should be created to accept the table name as a parameter and return the max ID for that specific table. This allows for a dynamic approach to managing incremental loading.

Using Data Flows and Pipelines

Once the meta table and stored procedure are set up in the database, the next step is to utilize data flows and pipelines in Microsoft Fabric. The data flow is connected to the source system and filters the data based on the maximum ID stored in the meta table. This ensures that only new and updated data is loaded incrementally.

The pipeline is then created to call the data flow, moving the data into staging tables in the data warehouse. Another stored procedure is executed within the pipeline to update the max IDs for the respective tables, ensuring that subsequent runs of the pipeline only pull in new data.

Completing the Process

Finally, a full pipeline is created to clear out the staging tables, execute the previous pipeline, and then load the facts and dimensions into the data warehouse. This completes the incremental refresh process, allowing for efficient and automated data loading.

Key Takeaways
– Utilize a meta table and stored procedure for managing incremental loading
– Use data flows and pipelines in Microsoft Fabric for data transformation
– Create a full pipeline to automate the incremental refresh process

If you have any questions or comments, feel free to reach out. Thank you for watching!

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