Handling huge data sets requires optimum optimization of Power Query for effective transformation and efficient data refreshes. Below are actual simple techniques you can apply:
1. The Optimization of Query Folding
Use query folding, which pushes transformation steps back to the data source. All transformations, like filtering, grouping, and joining, should occur at the database level and not in Power Query. Right-click a step within Power Query and select "View Native Query" to verify query folding. However, if query folding is broken at some point, rearrange or simplify the transformations so that they can carry on for as long as possible.
2. Early Data Reduction
During the transformation process, appropriate filters are applied as soon as possible to reduce the loaded data in Power Query. For instance, unnecessary rows, columns, or date ranges can be filtered right at the source or just be one of the first few steps in Power Query. This hugely de-clutters the data and speeds up the other operations.
3. Optimize Steps Applied
Combining transformations reduces the number of applied steps. For example, column renaming does not require several different steps; it has to be done in one step. Avoid unnecessary intermediate steps that consolidated formulas or transformations can wipe out.
4. Efficient Management of Large Lookup.
When working with an irregular merge or lookup of large amounts of data, ensure that both tables are stripped down to only the necessary columns before the merge. Also, where advisable, the use of sorted joins or pre-summarized data greatly reduces the work required to perform the joining process.
5. Load Required Data Only
To accommodate the load, more tables or points of data from the source should not be pulled. Use SQL queries or source filtering options in the load to bring into Power Query only what is needed. Within Excel-based data source names, limit the scope using these names to reduce the queries loaded.
6. Make Use of Buffering For Redundant Transformations
If a dataset is going to be repeatedly used within the same query, apply the Table. Buffer function to cache the dataset in memory. This will avoid a redundancy of transformations and speed up the calculations.
7. Track and Optimize Dependencies Between Queries
Query dependencies are viewed in Power Query to visualize relations within queries. Dispose of matching dependencies. Or shrink the query dependencies string to stop cascading performance penalties.
8. Splitting The Data Process
Reduce to modules that are more manageable and implement these into queries recognized later, intermediate queries that link the actual reference query according to a part of the transformation process. Refreshing also simplifies the process of maximizing the manageability of the queries.
9. Complexity Avoidance in Custom Columns
Put on limitation for usage of complex custom columns in Power Query, especially in nested logic. If they require advanced calculations, import them from DAX or source level.
10. Incremente Refresh
Incremental refresh allows the user to refresh only the updated data or, for huge datasets, typically new data. This greatly reduces refresh time and, therefore, improves performance for older history records.
By following the previous strategies, you'll speed up the processing of Power Query with multi-step M queries and performance with large data sets.
