Beinex Wins 2021 AWS Rising Star Consulting Partner of the Year – MENA Award
Related Articles
Top Differences between Dynamic Set and Fixed Set
Dynamic Set- Set members change when the underlying data changes.
- It has a single dimension.
- Set members do not change.
- It can be single-dimensional or multidimensional.
Steps to Create a Dynamic Set
The process to create a dynamic set is as follows: In the Data pane, right-click on the sub-category dimension and choose Create > Set. (Figure 1)
Figure 1: Creating a Set
• In the Create Set dialog box, set up your set. You can configure it using the following tabs:
1. General: Use the General tab to choose one or multiple values to be considered when computing the set. Alternatively, you can choose the Use All option to consistently consider all members, even when new members are added or removed.
If you know the top-selling products beforehand, you can manually select the products as shown in Figure 2 below.
Figure 2: Creating a Set using General Tab
2. Condition: Utilize the Condition tab to establish criteria that decide which members should be incorporated into the set.
You can specify this condition and create the set if you need products with sales greater than $50,000. (Figure 3)
Figure 3: Creating a Set using Condition Tab
3. Top: Employ the Top tab to set restrictions on which members should be included in the set.
For instance, you can establish a limit based on total sales, where only the top 5 products with the highest sales are included. (Figure 4)
Figure 4: Creating a Set using Top Tab
- Once you have completed the configuration, click the "OK" button.
- The newly created set will appear at the bottom of the Data pane within the Sets section. You can identify it by the set icon, which denotes a set field.
Steps to Create a Fixed Set
The process to create a fixed set is as follows:
- In the developed visualisation, select one or more marks from the view (Figure 5).
Figure 5: Selecting the marks.
- Right-click on the selected mark and select “Create Set” (Figure 6).
Figure 6: Creating the set.
- Type a name for the developed set (Figure 7).
Figure 7: Typing the Set Name.
- When finished, select “OK”. This newly created set can be accessed from the data pane. When this set is placed in the filter, the view will be filtered to show only the relevant set values.
Top Benefits of Sets in Tableau
• Top N or Bottom N Analysis: Sets can filter the data to display only the top or bottom N values based on a specific condition. For example, you could create a set to show the top 10 products by profit or even combine sets and display the top N and bottom N products by profit in a single chart. (Figure 8)
Figure 8: Top 3 and Bottom 3 Products by Profit
• Segmentation Analysis: Sets can also segment data into groups based on a specific condition. This can be useful for analysing performance differences between different groups. For example, you could create a set to segment customers based on their geographic location.
• Excluding Data: Sets can be used to exclude specific data points from a visualisation. For example, you could create a set to exclude customers who have not purchased in the last six months.
What is Set Actions in Tableau
Set actions allow users to modify the values within a set, on selection of marks within a view. This enables your audience to engage directly with a visualisation or dashboard and control various aspects of their analysis.
To utilise set actions:
- Create sets associated with your data source.
- Build set actions using the created sets.
- Optionally, create calculated fields that incorporate the sets.
- Construct visualisations referencing the sets.
- Test and adjust the set actions for desired behaviour.
To create a set action that helps in drilling down category:
1. Create a set that selects a particular category (Figure 9 shows creating a set using the general tab selecting only furniture)
Figure 9: Creating Category Set using General Tab
2. If you are in a worksheet, go to Worksheet > Actions.
If you are in a dashboard, go to Dashboard > Actions.
3. In the Actions dialog box, click “Add Action” and choose "Change Set Values."
4. In the Add/Edit Set Action dialog box:
• Provide a descriptive name for the action.
• Choose a source sheet or data source. By default, the current sheet is selected. If you opt for a data source or dashboard, you can select specific sheets within it.
• Choose the desired method for users to execute the action:
- Hover: The action will trigger when a user hovers the mouse cursor over a mark in the view.
- Select: The action will activate when a user clicks a mark in the view.
- Menu: The action will initiate when a user right-clicks (or control-click on Mac) a selected mark in the view and then selects an option from the context menu.
• To specify the target set:
- First, choose the data source from the available options.
- Then, select the desired set from the Target Setlist.
Figure 10: Setting up set action
• Specify what happens when the action is run in the view:
- Assign values to set - Replaces all values in the set with selected values.
- Add values to set - Adds individually selected values to the set.
- Remove values from the set - Removes individually selected values from the set
• When the selection is cleared in the view:
- "Keep set values" will retain the current values in the set without any changes.
- "Add all values to set" will include all possible values in the set.
- "Remove all values from set" will remove all previously selected values from the set.
5. After configuring the desired behaviour, click "OK" to save the changes and return to the view.
6. To ensure the set action functions as intended, interact with the visualisation, and test its behaviour.
Benefits of Set Actions
- Filtering: Set actions can filter data based on user selections. For example, you could create a set step that filters the data to show only the top 10 customers in a particular region.
- Highlighting: Set actions can also highlight data based on user selections. For example, you could create a set action highlighting all the customers who have purchased in a particular month.
- Drill-downs: Set actions can create drill-downs that allow users to explore the data in greater detail. For example, you could create a set action enabling users to drill down from a high-level view of category by sales to a more detailed view of sales by sub-category. (Figure 11)


Many teams can build AI agents as experiments, but turning them into something that works reliably, safely, and at scale for real business use is much harder. To solve this, the Vertex AI Agent Builder from Google Cloud helps organizations build, run, monitor, and secure AI agents from start to finish. In short, Google is making it easier to go from "we built a demo agent" to "we run trusted AI agents in production."

1. Adjust the data sample size
- a. Boost the size of your data sample: Adjust the sample's row count by returning to the input stage. You can add more rows or include all the data but remember that doing so might make the performance slower. Another word of caution is that utilising a specified number of rows will only return the fastest method the underlying database can find to replace the given rows.
- b. Take random sampling: Tableau Prep automatically chooses the optimal number of rows to return based on the total number of fields in the collection and the data types of those columns. The database level random sampling occurs and returns the specified number of rows. The database returns a sample after inspecting each entry. Not all data sources provide this option, which could also affect performance.
- c. Add a step filter at the input stage: You may ensure that the information pulled into your data set is pertinent to your research by including a filter at the input stage. This improves performance while providing you with a more representative sample.
2.Evaluate the data
You'll probably want to start by counting the number of distinct values in each field. A simple check at the column header at the top reveals how many states are represented in the data set. You'll also want to understand how various values connect to identify data outliers or problems. You can utilise highlighting in Tableau Prep to find correlations between different fields. The data grid view is condensed to only display the records with the selected value in the chosen field when you click on a value in the profile pane. Tableau Prep highlights the corresponding values in blue, and the values span areas.
3.Filter the data
Limit the fields you import into Tableau Prep to those you'll need for your analysis to maximise the overall effectiveness of your data preparation process. By filtering your data, you can verify that you're performing the proper analysis while saving time. For instance, if you need to look at sales data from the previous two years, you may use the range or relative date filters to limit the date field to that period. You might want to eliminate any incorrect or irrelevant data. A value in the data pane can be excluded with a single click. You can do this at any time during your flow.
4.Assess and tidy up the data
Tableau's data types will have an impact on your analysis. Therefore, it's critical to correctly identify each field before beginning. Even though Tableau allows you to update aliases, alter data types, split lots, and create calculations, it is far simpler to carry out these tasks beforehand, particularly when preparing the data set for someone else. Tableau Prep includes built-in capabilities to aggregate and replace recurring characters or pronunciation, saving you from having to edit each one individually so that you don't have to; these solutions use algorithms to make cleaning easier. Or, if you foresee a missing value, you may manually add it so that it will be included when the flow processes the complete data set. You can apply a computation if you know that a field must be cleaned or filtered, but it takes more than the user interface offers.
5.Understand the data results
Deciding about the final data set's appearance while you begin to prepare your data can be difficult. For Tableau to effectively analyse your data, you might need to merge numerous data sources or pivot your data from columns to rows.
One technique to get beyond this obstacle is visualising the data pane in Tableau Desktop as to how it should appear. Do you have columns with the same value in several places? Should each product be in a single field with the sales transactions stated below, or should each product have its column with the sales transactions listed underneath? The latter is more likely, and a pivot is necessary for this situation.
You will be joining the data if you need to combine two tables. By using a join, you can increase the number of fields in your data source that you can investigate. Although a join can be added at any point during the data preparation process, the sooner you use it, the sooner you will comprehend the data set and identify areas that require immediate attention.
Like appending two data sets together, a union enables you to do so. For instance, you might have an Excel file where each sheet displays transactions from different years. You may maintain the same structure with extra rows by using a union rather than joining the tables.
After your data has been organised, processed, and filtered, it's time to interpret what it is trying to tell you. Tableau Prep connects with your entire business intelligence platform like many other data preparation products. To allow others to begin their analysis, publish the extract to Tableau Server or Tableau Cloud. Bring it into Tableau Desktop to start posing and investigating more in-depth queries. The hardest part of the data analysis process is now complete. It's time to share the breakthroughs that resulted from your hard work.
AI governance in the UAE now means three concrete things for any business operating here: mapping AI use cases against the UAE AI Charter's 12 principles, building an AI-enabled data governance framework that satisfies the Federal Personal Data Protection Law, and putting board-level oversight in place before a regulator asks for it. Enterprises that treat these as one connected program, not three separate compliance checklists, move faster and face less rework later.

- Virtual Connections
- Centralized Row-Level Security
- Edit Published Data Source
- Parameters in Tableau Prep
Centralized Row-Level Security - Bring precision and agility to data protection
In general, Row-Level Security (RLS) refers to filtering out rows of data at query time based on the current user's identity. Due to this, different users can view the same table, viz, or report and see only data they are authorized to see. RLS allows scenarios like having a regional manager only see sales created within their team, but not sales from another manager's team.
Rather than implementing RLS individually on every Tableau workbook or data source accessing a sensitive table, virtual connections can be used to centrally define and manage data policies. These policies will be consistently applied across all connected Tableau Flows, Data Sources, or Workbooks that depend on that data. This gives granular control over data security while bringing flexibility to reuse data sources.
Edit Published Data Source
This feature makes managing data sources very effortless. Now we can create, edit and rename data sources directly in Tableau Server and Tableau Online, test the changes, and publish—all without leaving the browser. This streamlines the governance and metadata management.
Parameters in Tableau Prep
Parameters in Tableau Prep speed up and simplify tasks by enabling users to easily change data inputs, data outputs, and values used throughout a flow which makes reusing them much easier.
Supported step types in 2021.4:
- Inputs /Outputs File path/File name
- Published data source name
- Calculated fields
- Filters
Tableau 2021.4 Highlights
- Data Management
- Virtual Connections
- Centralized Row-Level Security
- Tableau Catalog
- Inherited column descriptions
- Create flows from Catalog pages
- Tableau Prep Builder
- Tableau Prep Parameters
- Create Tiles
- Flow output Subscriptions
- Tableau Online
- Connected Apps
- Tableau Bridge Multi-Pool
- Analytics
- Metric Updates
- Show Metric status with colors
- Customize comparison period and data window
- Embed metrics
- Replay Animations
- Web authoring improvements
- Edit published data sources on Server and Online
- Copy/Paste Dashboard Zones
- Metric Updates