i). Manage Direct Data Filters in a Separate Node
Direct data filters are now a separate node, making it easier to see whether a direct data input is filtered. Direct data filters speed up recipe runs by reducing the number of records pulled into the recipe. Previously, filters were part of the direct data input node.
How: Add a filter node after a direct data input node. The filter’s graph icon has a lightning bolt to denote that it’s a direct data filter.
ii) Set Up Transformations More Easily with Transform Panel Improvements
a.) Customize the API name of derived columns in Tranformation pane:-
I really loved this Features.Earlier we have to perform edit Attributes steps for each and every custom derived column created from a transformation in order to change the API name. it was time consuming as well as additional steps has to perform. but now easily we can able to define the API Name of custom derived column from a transformation on the transformation’s configuration panel without editing its attributes.
b) Resizing the transform panel :-
No more we would need to keep on eye widely open and scrctch our head to rebiew the long Formula. Now we can able to resizing the transform panel. For example, expand the columns pane to view long column names, or shrink it to have more space for a long formula.
c) Choose the Columns That You Want to Keep:-
phew!!!!! Refine your datasets by choosing the columns that you want to keep and dropping the rest with Drop transformation. Previously, you could only choose which columns you wanted to remove.
eg : after perfoming all the transformation you have a 44 coumns on the node but you want to keep only 15 columns out of it. in that case you have select remaining 29 columns from the drop down on the Drop transformation.
now we can able to Save time for hiding or dropping multiple columns with the option to select more than one at a time.
How :- To choose which columns that you want to keep and drop the rest, click Keep
iii) Build Big Recipes Faster
Node Clean up:-
Tidy up your graph using node cleanup to arrange nodes by type, organize the flow of Input nodes are moved to the left and output to the right, with intermediate steps placed in between, and straighten up relationships. You can monitor whether a node’s preview is ready to view with a build progress wheel.
Reating large recipes is easier with zoom options, node cleanup, visibility of build progress, and error message linking. For example, you can change your perspective of the data prep graph by zooming out to see all your nodes or zoom in for greater detail.
iv) Update Dataset Column Values with Data from Another Source
With the Update node, swap column values with data from another data source when key pairs match. For example, to update selected account names after a series of recent acquisitions and mergers, replace the name from an uploaded spreadsheet based on matching account IDs. Previously, without key pair matching, you’d include every account’s name in the spreadsheet and clean up with a Join node
How :- After you add the original data as an input node, add an Update node.Choose the dataset containing the update data. This example replaces the Account Name of certain rows based on whether their ID is included in a previously uploaded CSV file.Choose which key pair values must match between the original and update data and which columns to update . A column is updated if all key pairs match. If the account ID is included in the uploaded spreadsheet, its name is updated. If the ID isn’t included, the original name is left in place.
v) Configure Your Fiscal Year in Data Prep
Analyze your Tableau CRM data by a fiscal year without impacting your entire org or taking manual steps. Define your fiscal year in Data Prep and automatically apply it to your Data Prep data. Previously, the fiscal month offset attribute had to be modified in each dataset’s JSON. This feature is only available for orgs that don’t have an org-wide custom fiscal yer defined in the company profile.
vi) Add Accuracy to Your Insights with Date and Time Data in Your Local Time Zone
Want to break free from GMT and view time-sensitive dashboard data in another time zone? We’ve got you covered. Now you can view dashboard data in the time zone that matters most to your business. In case you didn’t know, dashboard data is converted to the GMT time zone. That means when your California-based support team creates a case late on Wednesday (in California time), the creation date could suddenly change to early Thursday in their customer support dashboard. With Single Custom Time Zone, you can ensure that time-sensitive data is more precise and relevant to your global audience. This feature is now generally available.
How: Turn on Single Custom Time Zone in Analytics Setup and select which time zone to use.
Keep these considerations in mind when using Single Custom Time Zone.
- A time zone is defined at the org level.
- All datasets must be refreshed after enabling Single Custom Time Zone and specifying your default org time zone.
- Existing dashboards remain in the GMT time zone.
- After enabling Single Custom Time Zone, new dashboards can use non-GMT time zones.
for the other release features kindly check out the below release notes
