Over 2.5 quintillion bytes of data is generated every single day, and the estimation is that, by 2020, over 1.7 MB of data is expected to attain optimum efficiency. Businesses are bound to look for new transformation methods to combine data to achieve optimum efficiency. One such process of combining data is Tableau Data Blending.
Tableau Data Blending is in high demand among enterprises today, and to capitalize further on this demand, Tableau is expanding its product offerings such as Tableau Creator, Viewer, and Explorer.
Data Blending in Tableau
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Tableau Data Blending is a powerful Tableau feature that is used to analyze the related data into a single view among multiple data sources.
Data Blending is aimed at making Tableau’s offerings easy to customize and scale, thus receiving positive feedback from the company’s clients. Now, because data blending plays such a crucial role in any organization’s data cycle, it makes for an essential module in the Tableau training curriculum. Data Blending performs the following activities.
Data blending can be chosen under the following conditions:
You want to combine data from multiple databases but not supported by cross-database joins: When cross-database joins are not supporting connections to cubes (like Oracle Essbase) or some exact-only connections (such as Google Analytics), in such cases, you have to set up individual data sources to analyze and use data blending to combine the data sources on a single sheet.
Data is at different levels of detail:
Data set captures data sometimes using various levels of detail, i.e., greater and lesser granularity than the other data set.
Data blending in Tableau provides the user with options to combine and join various data sources. However, mixing and joining are different in Tableau. Unlike other joining cases, Data Blending enables combining data sources after aggregation on the specific sources.
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Data blending stimulates the traditional left join. The main difference between these two is when the join is performed concerning aggregation. It requires at least two data sources termed primary and secondary sources. When designated a primary data source, it performs the functions as the primary data source or main table. Any subsequent data sources used on the sheet are treated as secondary data sources. The columns of secondary data sources have corresponding matches in the primary data source appear in the view.
Data Blending in Tableau won’t create any row-level joins or even not a way to add new rows or dimensions to your data. Data blending best fits when you have related data in distinct sources to analyze together in a single view. If you're going to integrate data, first add one of the standard dimensions from the primary data source to the view.
For instance, let’s consider a classic example of Sales and Target of business. The operation is to perform Tableau data blend on the two data sources. When blending primary and target sales data, the two data sources may have a common data field. The date field is used on the sheet. When you switch the secondary source in the data window, Tableau links the fields having the same name automatically. If both are under the same name, a custom relationship can be defined to create an accurate mapping between fields.
For data sources used on the sheet, queries are sent to the database and process the results, left joined the common dimensions. The join is performed on the member’s alias names of the standard sizes. Therefore, the values aren’t an exact match and can fix in Tableau.
A regular test should be performed to observe whether the data is integrated smoothly to drag dimensions from the primary source into the text table on the sheet. On the other sheet, drag the same fields from secondary sources into the text table. If these two tables are matched, the data is almost going to blend accurately in Tableau.
|Related Article: Tableau Join Tables|
Implementing Tableau Data Blending with an Example:
Step1: Connect to your data and set up the data sources and designate a primary data source
Step2: Select Data > Connect to Data and connect to the Sales Plan spreadsheet. Drag the Sales Plan measure to the Level of Detail shelf.
Step3: Designate a secondary data source. Right-click the Sales axis and select Add Reference Line. In the Reference Line dialogue box, add reference lines, which shows Sales Plan per cell. Click OK when finished.
Step4: Now the worksheet pulls data from the Sales plan ( secondary source) to show how sales are compared to the sales forecast.
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As we already discussed earlier, Tableau Data Blending is the operation performed to combine multiple data sources into the same view through common fields between them to join. Unlike the ordinary join that combines data sources at the lowest granularity before aggregation is done, a Tableau data blend can combine data sources after aggregation on the individual sources, and ultimately limit the quantity (number) of records joined thus maximizing computational efficiency. The post-aggregate join performs better than joining at row-level before developing the view and then conducts aggregate calculations.
There are two types of Tableau Data Blending as discussed below:
Automatic Data Blending is defined as a relationship in Tableau that works best only if the field in which we are working on consists of the field name same for both sources of data. If not, we have alias names so that they can match.
The data blending process is used when there exists a scenario that requires a more complex blend that would be the budget comparison data from spreadsheets with data from a database.
There are few limitations of Tableau data blending around non-additive aggregates like RAWSQLAGG and MEDIAN. They are:
The success of BI projects depends on an organization's choice of BI solutions and the vendor’s ability to deliver relatively rapid and successful implementations, along with supportive maintenance and follow-up.
However, choosing the right BI tool for your business is the primary condition to ensure better business insights. With a quick list of BI tools, you may shortlist the best suitable one for your business, be it big or small.
Most data analysts are switching to business analytics tools today for more flexible solutions. Try Mindmajix Data Analytics training for a better understanding of Data Analytics in the business process.
Prasanthi is an expert writer in MongoDB, and has written for various reputable online and print publications. At present, she is working for Mindmajix, and writes content not only on MongoDB, but also on Sharepoint, Uipath, and AWS.