Data Joining in Tableau

Tableau makes analyzing the data very fastly and easily. It offers a connectivity to any database and easy to learn, those are all the best reasons to use Tableau. It provides many options to use for a different kind of the organization vary. Tableau Desktop fulfills a high need for business analysis work, even users do not have a computer degree, development skills can access a large volume of data. It can be used for publishing data source, creating a data visualization. and now in this blog explains the What is Data Joining in Tableau?

Tableau Server helps you to set live interactive and give high security to your data. It is a secure way to diffuse the worksheets. We can take data from anywhere and shared that data through the mobiles or Desktops within the organization. This is a small overview of Tableau, in previous blogs I gave complete information of Tableau Server, Tableau Desktop, the difference between Tableau Server and Desktop and what is data blending?

Learn more about this technology Tableau Online Training in this overview

What is Data Joining in Tableau?

Joining is a method for mixing the related data to those common fields means if the data is analyzed in Tableau is after made up of collections of tables those are related to common fields. Finally the result of mixing the data by using a join virtual table adding columns of data it is typically extended horizontally.

For Specimen, If you are analyzing the to publishers and they have two tables. The first table has patient ID numbers, patient name. And the same way the second table has patient ID numbers and foreign key. In this two tables, the common field is Patient ID number.

You can analyze the above two tables together, you can join the ID number to answer a question like. After combining the data using join we can view and use the data from different tables in your analysis. For more information of what is data joining in tableau discussed below.

Overview of join types

In Tableau, we have four types of joins that we can use combine the data. Those are left, right, inner, and full outer. Depends upon the database we can decide which type of join will be used. By checking the join dialog we can tell which type of join supports our data.

Left: If you use a left join to combine tables in Tableau, it shows the result by the following way: the total data present in the left tables and corresponding data matches from the right table. Otherwise, it shows null value in the grid.

Right: If you use a right join to combine tables in Tableau, it shows the result by the following way:  the total data present in the right tables and corresponding data matches from the left table. Otherwise, it shows null value in the grid.

Inner: If you use an inner join to combine tables in Tableau, it shows the result by the following way: the table that contains data that will match in both tables.

 Full outer: If you use a full outer join to combine tables in Tableau, it shows the result by the following way:  that contains all values from both tables. Otherwise, it shows null value in the grid.

Union: It is not included in above types of join, Union is another type of method for combining two or more tables attaches rows of data from one table to another.

Working with the multi-connector data source

When you are working with a multi-connector data source is nothing but working with any data source, with some warnings discussed below.

Make extract files the first connection

Make sure that the connection to the extract (.hyper) file is the first connection when connecting the extract files in a multi-connection data source. Suppose, if you need to connect to several extract files in your multi-connection data source, the only thing you do is make changes to your corresponding task in the extract in the first connection preserved. This is about Data Joining in Tableau, I hope you like this blog and get some idea of What is Data Joining in Tableau? For more updates of Tableau get touch with us.Read More At Tableau Online Training Hyderabad