1. Copy/Paste:
You can copy a table from one project and paste it into another project's Power Query.
If the table has connections or references to other tables, those dependencies will also be copied.
Example: If Table A references Table B and Table C, copying Table A will automatically bring along Table B and Table C.
2. Delete/Rename:
Straightforward functionality for removing or renaming tables.
3. Enable Load:
Some tables serve only as references for other tables and are not required in the Power BI Desktop data section.
Disabling the load prevents these tables from overcrowding your data model.
Best Practice: Ensure the table isn't directly used in measures or visuals before stopping its load. Indirect references through other tables are not an issue.
Example: A staging table used only for transformations can have its load disabled to keep the data model clean.
⭐ Level-Up: Mastering 'Enable Load' is a key step towards transitioning from a beginner to an intermediate analyst.
4. Include in Report Refresh:
Suitable for archived or static tables, such as historical data that won't change.
Disabling the refresh reduces unnecessary load time.
The table will remain in your dashboard but won't update during a data refresh.
Example: A table containing sales data from 2020 can be excluded from refresh since it won't change.
⭐ Level-Up: Understanding when and how to manage 'Include in Report Refresh' signifies an intermediate-level grasp of Power Query.
5. Duplicate vs Reference:
Duplicate: Creates an independent copy of the table.
Reference: Creates a linked copy that remains connected to the original table.
Advantage of Reference: When multiple tables originate from the same source, you can update the source table once, and all reference tables will inherit the changes.
With duplicates, changes must be applied individually.
Example: If you have a master sales table feeding into multiple queries, using a Reference ensures all queries remain updated when the master table changes.
A complete detail is provided on Understanding Duplicate vs Reference in Power BI.
⭐ Level-Up: Truly grasping the difference between Duplicate and Reference marks a transition from beginner to intermediate.
6. Move to Group:
Tables can be organized into groups, similar to files and folders.
Grouping helps keep related tables together, improving navigation and organization.
Example: Group all finance-related tables under a 'Finance' group and sales-related tables under a 'Sales' group.
7. Move Up/Down:
Tables can be reordered by dragging or using the 'Move Up' and 'Move Down' buttons.
Example: Move frequently used tables to the top for quick access.
8. Create Function:
Rarely used but allows creating reusable functions for repeated tasks.
Example: Create a reusable function for currency conversion applied across multiple tables.
9. Advanced Editor:
Displays all transformation steps in M-language.
Every click and transformation is recorded as code.
Best Practice: When modifying a table's structure or source, create a new table and make changes there first. Once satisfied, copy the code and paste it into the original table.
Why? Prevents errors in the primary table and protects dependent measures, calculated tables, and visuals from breaking.
Example: If Table A is the basis of your visuals, test changes in a temporary table before applying them to Table A.
⭐ Level-Up: Proficiency with 'Advanced Editor' reflects an intermediate-level understanding of Power Query.
10. Properties:
Not frequently used.
Allows changing the table name, adding a description, enabling/disabling load, and managing report refresh.
Most functionalities here are also available outside this section.
Example: Add a description to a table like 'This table contains raw sales data from 2020-2023 for quarterly analysis.'
⭐ Level-Up Milestone: If an analyst understands the nuances of Enable Load, Include in Report Refresh, Duplicate vs Reference, and Advanced Editor, they have successfully transitioned from a beginner to an intermediate-level Power Query analyst.
Using these features efficiently ensures better performance, cleaner models, and easier maintenance of your Power BI projects.
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