Data Blending

In the current times, everything runs on the data. All the organizations utilize the data for the betterment and development to run the organization efficiently. Every single organization holds a vast amount of data. Sometimes it is difficult for members of the organization to understand, analyze, and meet the business requirements at the right time. We need to combine the data to perform the operations related to data. Hence, we need to use some features that would help combine the data using some transformation methods to achieve efficiency. Tableau has come up with a data blending feature that has become popular these days. In this blog, you will gain an understanding of the data blending in Tableau, working on the Tableau, steps to be followed for blending the data along with its limitations.

What is data blending in Tableau?

Data blending is a process or a feature in Tableau, which helps bring the data from different resources together in a single view or a single tableau worksheet. An organization maintains a large amount of data, combining the data under two sources is one of the standard procedures that they follow to perform any operation related to the data. Tableau data blending is every feature that is in high demand among the different organizations today as it provides the organizations an opportunity to work and analyze the data in a single place.

Data blending in Tableau allows users with different options to combine and join different data sources. Data blending allows the combining of the data resources only after aggregation on the specific resources. Data blending allowed the users to work on a single worksheet or maintain a single worksheet and attached it to the standard dimensions. It does not allow the creation of new level joins and also does not allow you to add any dimensions or rows to your data. Data blending in Tableau is used when there exists some related data from various sources that you would like to combine in a single view.

Let's take an example of some data in an organization. As we all know that the organization maintains a vast amount of data, the data would be represented based on their departments. Let us consider the sales data of an organization present in the relational database and the sales target data available in the Excel spreadsheet. This is used to compare the actual sales and target sales. Here you can blend the data based on the common dimensions to get access to the sales target measure. These two sources that are involved in blending are called primary and secondary data sources. We will learn a little more about the data process in the later part.

How is data blending different from Data joining?

Data blending stimulates the traditional left join. The most crucial difference between the data blending and data join is when a join is performed concerning the aggregation. This process requires two different data sources as the primary data source and secondary data source. When the primary data source is designated, it is responsible for only performing the functioning as the primary data source. Any other subsequent data sources present in a sheet or used in the sheet apart from the primary data sources are considered as secondary data sources, if secondary data sources have their matches primary data source in the view.

After designating the primary and secondary data sources, the standard dimension must be defined between the two different data sources. This process of defining is called a linking field. The date field in the primary and the secondary data sources has the same name, and then Tableau will go ahead and create an association between them. This relationship is a link icon () next to the date field. Suppose the two dimensions do not have the same name. In that case, it defines a relationship that creates an exact mapping between the data fields among both primary and secondary data sources.

Working of data blending in Tableau:

I think you have got an idea of data blending. We also need to know how data blending works in Tableau. Let us take the same example of the sales data and the target sales data open organization. The sales data and the target sales data are the primary and secondary data sources. When you are blending or combining the two different data sources, there might be a high chance that they have a common data field. The Data field is used on the sheet. When you actually switch to the second data window, Tableau can link the fields which process the same name automatically. Both of them have the same name; a custom relationship can be defined to create the accurate mapping between the different fields.

Concerning the data sources represented on the sheet, the queries will be sent to the database, and the results will be processed, left join the common dimensions. This join is performed on the alias names of the standard sizes.

The data blending in Tableau also allows you to perform a regular test to check whether data is integrated smoothly to drag the dimensions from the primary source into the text table on the sheet. You need to drag the same fields from the secondary sources into the text table on the other sheet. If both the tables are matched, it means that the data is blended accurately in the Tableau as expected.

Types of blending:

Tableau allows two different types of blending. They are represented below: 

1. Automatically defined relationship
2. Manual tableau data blending
Let us have a quick review of these types of blending.

Automatically defined relationship:

The automatically defined relationship in Tableau is one of the types of data blending, which would be best if the field we are working on consists of the same field name for both the sources of data. If not, we have the alias name that can match.

Manual tableau data blending:

The manual Data blending process is used in most complex situations. It is specifically used when there exists a scenario that requires the most complex blend of data. It could be the budget comparison data from the spreadsheet with the data from the database.

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Steps to follow for data blending in Tableau:

Data blending is the practice or a method to combine the data from different sources that you would be interested in analysing together on a single sheet. Tableau consists of two inbuilt data sources named has sample coffee chain.mdb and sample superstore. Let us gain an understanding of the steps to be followed to blend the data in Tableau.

Step 1: Connect to your data and setup data resources.

1. You need to connect a set of data and set up the data source page. The inbuilt data source Sample coffee chain.mdb is a Ms Access database file used to illustrate the data blending.
2. It would be best to put the data option, followed by a new data source, and connect to the second set of data. The above example uses a sample superstore as the data source. You need to set up the data source.
3. Click on the sheet tab to start building the view.

Step 2: Designate a primary data source.

1. You need to drag at least one field from the primary data source Into the view to designate the primary data source.
2. In the data pane, you need to click on the data source that you would like to designate that you would like to visit as the primary data source. In the above example, sample coffee change will be selected as the primary data source.

Step 3: Designate the secondary data source.

The field used in the view from the data sources that are not used as primary data sources will be actively linked automatically to designate the subsequent data sources as a secondary data source. In the above example, the sample superstore is designated as a secondary data source.

Step 4: Blending of the data:

1. You will need to integrate or combine the data from both the resources based on the common dimension. In our example, the common dimension would be the state. You also need to know that a small link image will appear next to the dimension-State. This image will give you an understanding of the common dimension between the two data sources. 

2. Let us consider that you have prepared or created a bar chart with the profit ratio in the Column shelf and state in the row shelf. The chart will give you an understanding by presenting the variation between the profit ratio for each state in both the coffee chain shops and Super Stores.

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Limitations of data blending in Tableau:

Every tool, feature, or platform will have its limitations, which would be the future enhancements. In the same way, data blending features in Tableau also have some limitations. Let us have a quick review of the limitations of data blending in the tableau platform.

1. The Data blending feature compromises with the speed of the query in high granularity.
2. It also allows the cube data sources to be used as a primary data source for blending Tableau's data. They are not allowed to be used as secondary data sources.
3. When you try to sort out a calculated field or filter using some calculated field that uses blended data, the calculated field will not appear in the field drop-down list of the sort dialogue box.
4. The data blending in Tableau also has limitations around non-additive aggregate such as RAWSQLAGG, MEDIAN, etc.

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Conclusion:

Every business project on the business organization has its own choice of business intelligence solutions for business intelligence oriented projects. The business organization strives hard to make implementations and deliverables along with the maintenance and follow-ups. And it is essential to choose the right decision stream that will help in ensuring better business insights. We all know that there is a change in people's minds as there is a change in technology and advancement. All the data analysts are now switching their roles to do business analytics today for bringing outside flexible solutions for their business how to get trained and certified in Data Analytics that will help you in gaining a better understanding of the Data Analytics in the Purchase processes and also to hold the best career.

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Mudassir
Mudassir
DevOps ERP and IAM tools
Mudaasir is a programming developer for hkr trainings. He has a well knowledge of today’s technology and I’ve loved technology my entire life. And also been lucky enough to work for the programmer including science and technology. Big thanks to everyone who has followed me on LinkedIn and twitter.

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