In this Tableau tutorial, we learned about various Data Blending in Tableau, Tableau Data blending with examples.

What is Data Blending in Tableau?

Tableau’s Data Blending is a method for combining data from a variety of resources and displaying it as a complete on a single screen.

Tableau Data Blending function is extremely useful. It’s useful when we have relevant data from numerous sources that we wish to evaluate in such a single view. It’s a process of combining data that combines columns of information from some other source of data to a database table through one data source.

Normally, joins are being used to conduct such type of data merging, although there are occasions where data blending is desirable, dependent on criteria such as the type of data and its resolution.

Need Data Blending in Tableau

Consider you’re a Tableau Developer who works with Salesforce transactional data and Access quota data. Because the data you want to merge is contained in two databases and the quality of the data gathered in each table differs between the two, data blending seems to be the great way to approach it together.

Data blending is important in the following situations:

  1. You want to merge data from multiple databases which cross-database joins don’t enable.Connectivity to cubed (for example, Oracle Essbase) and some retrieve connections are not supported by cross-database joins (for example, Google Analytics). Established separate data sources for the data we wish to examine in this scenario, and then merge the data sources on a single sheet using data blending.
  2. There are several levels of information in the data.Sometimes one data collection collects data at different levels of detail, i.e. with more or less refinement than the other.

Benefits of Data Blending in Tableau

The advantages of using Data Blending in Tableau are listed below.

  • Tableau’s Data Blending feature offers the best-in-class solutions for a wide range of data complexity concerns.
  • Data Blending in Tableau is used to resolve the Data Collocation issues.
  • Tableau Data Blending is capable of responding to and meeting the needs of investigative visualization tools.

Limitations of Data Blending in Tableau

  1. Non-additive aggregation, such as MEDIAN and RAWSQLAGG, have some data blending constraints.
  2. Data Blending slows down Query performance at high accuracy.
  3. Whenever you try and sort by a calculated area which contains blended data, the calculated field isn’t available in the Sort dialogue box’s Field drop-down list.
  4. When mixing data in Tableau, tabular data sources could only be deployed as that of the main source of information. They can’t be used to supplement primary data.

Steps for Blending Data

When similar data is available in many data sources, then data blending is commonly used. We’ll go over the multiple processes included in Tableau data blending in the sections below.

Step 1: Blending data preparation.

The first step is to ensure that the spreadsheet contains several data sources. Go to Menu—>Data—>NewData source afterwards when. Drag a field to the Display On-Screen area, and observe that data source from which you retrieved the element will be the primary data source. As a result, we’ve finished integrating the primary data source.

Step 2: To add the supplementary data source.

It is necessary to confirm that the secondary data source has a balanced connection with the primary data source before adding it. Repeating the previous steps, going to Menu—>Data—>New Data Source. Examine for the orange connection field indication to see if the data sources are automatically linked (). Click the grey backlink symbol to connect the data sources ().

Step 3: Blending the Data.

Today, based on the common dimensions, users can quickly combine data from both primary and secondary data sources. Next to every common dimension, look for a small connection graphic.

What is Primary and Secondary Data sources?

Primary data sources and secondary data sources are the two main components of data blending. The primary data source is the one that is referenced first with display, whereas the secondary sources of data is the one that is utilized second. It’s possible that perhaps the values will be limited to secondary data simply. It is because the display simply shows the data that corresponds to the primary source.

Difference between data joins and data blending.

Both of these techniques allow the user to mix data from various sources in a technical sense. They are, however, slightly distinct from one another. Many of the distinctions are described below:

  • Data blending allows data from many sources to be combined and connected. Data joins, on the other hand, can now only function with data from the very same sources.
  • Due to the large number of data sets available, data blending is the only option for joining tables. Data joins, on the other hand, are not really an appropriate alternative in this scenario since they can result in duplicate content, which can exacerbate the practical difficulties.
  • The length of functioning is also another significant difference between the two approaches. Data blending takes place within Tableau, but data joins can take place anywhere.

FAQs

Q. Define data blending in Tableau?

Data blending is a technique for merging information from several sources. Data blending gathers in data from a secondary source of data and shows it alongside data from the primary data collection method in the same screen. There are various methods for combining data, each with its own set of advantages and disadvantages.

Q. Difference between data blending and data joining in Tableau.

Data Blending enables the linking of data from many sources. Data Joining, on the other hand, only interacts with data from the same source. For example, if the data comes from an Excel spreadsheet and a SQL database, the only way to integrate the two sources of data is to use Data Blending.

Q. What are the limitations of data blending in Tableau?

Limitations of Data Blending in Tableau

  • Data blending difficulties exist with non-additive aggregation like as MEDIAN, COUNT, and RAWSQLAGG.
  • It’s difficult to publish the blended data source.
  • Secondary data sources are determined and summarized at all times.
  • Cube data sets should always be used as the primary data collection method.

Q. When should we use data blending to combine data?

  1. It should be performed when you need to evaluate data from many sources.
  2. When executing Data Blending, make sure there is a similar variable named “Linking field.”
  3. Data Blending is a left join procedure that accepts and conducts no other types of joins.

Q. What are the constraints that apply to data blending?

When it comes to non-additive aggregates like COUNTD, MEDIAN, and RAWSQLAGG, data blending has several limits. Non-additive aggregation are aggregation functions that give outputs that are not able to be aggregated along a dimension. However, each value must be calculated separately.

 

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