Supercharge Your Data Workflows: A Beginner's Guide to Snowflake Dynamic Tables

[@silky-ke6ue]

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[@KSRDatavizon]

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[@aashikkumar7066]

Nicely explained! If we want to load incrementally then what will be the change here? Should we alter dynamic table everytime for incremental data refresh?

[@KSRDatavizon]

Incremental Loading in Power Query: A Step-by-Step Guide Understanding Incremental Loading: Incremental loading in Power Query involves loading only the new or updated data from a source, rather than reloading the entire dataset each time. This is particularly useful for large datasets or when you need to minimize data refresh times. Key Steps: Identify the Change Column: Choose a column that uniquely identifies each row in your data source and that is likely to change over time (e.g., a timestamp, ID, or version number). Store the Last Loaded Value: Create a variable or parameter to store the last loaded value of the change column. This can be stored in a table, a query parameter, or a variable within your Power Query script. Filter Based on Change: When refreshing the data, filter the source data based on the change column. Load only the rows where the value of the change column is greater than the last loaded value. Update the Last Loaded Value: After loading the incremental data, update the last loaded value variable to the maximum value of the change column from the newly loaded data. Example: Assuming your change column is named "Timestamp" and you're storing the last loaded timestamp in a variable called LastLoadedTimestamp, your query might look like this: Code snippet let Source = Csv.Document(File.Contents("YourFile.csv"), [Delimiter=","]), FilteredData = Table.SelectRows(Source, each [Timestamp] > LastLoadedTimestamp), // ... rest of your query in // Update the LastLoadedTimestamp variable LastLoadedTimestamp := List.Max(FilteredData[Timestamp]) Use code with caution. Additional Considerations: Performance Optimization: For large datasets, consider using techniques like partitioning or indexing to improve query performance. Error Handling: Implement error handling mechanisms to gracefully handle scenarios where the data source might be unavailable or if there are data inconsistencies. Scheduling: If you're refreshing the data on a schedule, ensure that the scheduling mechanism is set up to update the last loaded value variable correctly. Dynamic Tables: While dynamic tables can be used to store the last loaded value, they might not be the most efficient approach for large datasets or frequent refreshes. Consider using variables or parameters instead for better performance and flexibility. By following these steps and considering the additional factors, you can effectively implement incremental loading in Power Query to improve data refresh efficiency and reduce processing time.

[@InsaniyathWala]

Nice explanation

[@KSRDatavizon]

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[@roberbonox]

Hi, i create a dynamic table and have task that validates data on tables (standard tables) that are used in the dynamic table. The thing is when i finish the validations i need, i put an ALTER DYNAMIC TABLE IF EXISTS xxx REFRESH; inside the task, and when the task is executed the refresh is not starting. If i execute manually the command it works but is like it doesn't when is inside the task, is this a limitation right now??

[@KSRDatavizon]

Snowflake currently handles dynamic table refreshes within tasks. We can check the logs to understand the dependence. Also check snowflake task schedule frequency. I assume something is missed at task level