Go through the table and study its fields. Step 13: When the above steps are correctly followed in order, we get the updated table as shown below. The table shall be updated based on the new SQL query.
#TABLEAU PREP 2020.4 UPDATE#
Step 12: When the SQL query is updated, we get options to update the table viz. Click on the OK button once done with the writing of the desired query. So we removed emp_no, from_date and to_date from the salaries table. Here, we removed those fields which were earlier repeated. Go through it once, and observe carefully the changes we did. Step 11: As we can see in the below screenshot, we edited the SQL query. We will edit this SQL to obtain overcome redundancy. Also, hire_date and from_date are two same fields but as they are present in the two tables with two different names so they have been considered as separate fields. We can see that the emp_no field has been considered for both the tables as highlighted. Step 10: Following the above step navigates us to “Convert to Custom SQL” section as shown by the screenshot below. Under the Data tab Click on the “Convert to custom SQL” option as shown by the following screenshot.
When two tables are joined, Tableau writes its own SQL query. Step 9: We can overcome the redundancy witnessed in the above step by using Custom SQL functionality in Tableau.
The two fields have been highlighted by the green boxes. However, the date has been referred to as Hire Date in employees table and From Date in Salaries table. Both the fields essentially represent the date from which the employee started working for the organization.
Similarly, the Hire Date and From Date fields are nothing but one and the same. This is because, Tableau, by default, considers fields from all the tables after the join is performed. field has been repeated as highlighted by the red boxes. Step 8: Just have a look at the data obtained after Tableau joined the employee’s table with the salary table.Ĭarefully study each of the fields in the above table. Note, if Tableau identifies that common field exists between them then it creates the join. Step 7: We brought tables employees and salaries into “Drag tables here” section, and automatically Tableau created a join between the two tables. We can directly drag the requisite tables into “Drag tables here” section. Step 6: When the database is loaded, the layout in Data Source looks like as the one shown by the following screenshot. We will use the required table to serve our purpose. As we can see, there are multiple tables present in this database. Step 5: We selected the “test” database from the drop-down menu. Now, in the Database section, we can select the desired database from the drop-down. We have got a “Connected to MySQL” message. Step 4: If correct credentials are provided then a successful connection with MySQL happens as shown by the following screenshot. Step 3: As can be seen below, we supplied requisite credentials in the MySQL Connection dialogue box in order to connect with the database.
In this dialogue box, we need to provide requisite credentials to get access to the data. Step 2: When we click on MySQL, the MySQL Connection dialogue box pops up. So click on MySQL as highlighted in the below screenshot. Step 1: Let’s first connect to the data source. Hadoop, Data Science, Statistics & others Using this database will work on and demonstrate the concept of Custom SQL in Tableau. Various tables present in the database include employees, departments, salaries, dept_manager, etc. It contains various details about employees in an organization. We will connect the “Employees” database in MySQL. Using the Custom SQL option in Tableau, we can streamline data extraction operations. However, in complex situations, these SQL queries may not be in an optimized form. Automatically, through SQL queries, the joins are performed by Tableau. When Tableau is connected with the SQL server, requisite data gets loaded into Tableau’s environment. Tableau supports connectivity with various types of data sources the most common SQL server. Any visualization tool working on a large database employs JOINs in order to get access to data from the database. In a database, multiple tables exist, and often to get desired data, multiple tables have to be joined.
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