Up next, you will learn the benefits of using Data Blending in Tableauįollowing are a list of benefits of using Data Blending in Tableau Joins can only perform the join operations at row level.
Joins can execute all four varieties of Joins.ĭata Blending offers data availability at different levels of granularity.ĭata has to be maintained at one single level granularity throughout the process while using Joins.ĭata Blending in the tableau can execute queries to the separate datasets, aggregate data, and then perform data blending. Joins can combine data from the same sources only.ĭata Blending can execute Left-Join only. Joins combine the data and then aggregates it.ĭata Blending can combine data from different sources. The following table describes the differences between joins and data blending in Tableau.ĭata Blending Aggregates the data and then combines it. The answer to this question is in the next subheading, where you will explore the major differences between Joins and Data Blending in Tableau. Now that you have executed an example based on Data Blending, you might have a question in mind, “We already have Table Joins what makes Data Blending in Tableau Stand out?” Drag that into the columns section, and you can instantly see the newly created visualization with combined sales.
Now, you can see the newly created calculated field in the measures section. Moving on, write in the formula below and select the OK button in the right bottom corner. To eliminate these null spaces, the agenda is to create a new calculated field and write the formula below. Now, as it is evident there are null values in the second graph. The combined result will look as shown below. Next, you will need to find out the annual sales for bikes and cars together. The visualization for the Fuel Efficiency of bikes is shown below. In short, Tableau will automatically convert the currently active dataset as a Primary Data Source. Tableau will change Bikes Data Set to Primary Source and convert the Car Data Set to Secondary Data Source in the current situation. Now, you must use the data obtained from Bikes Data Set. Next, you need to find out the fuel efficiency of the different bikes available. The visualizations look as follows.Īccording to the results, Dodge Viper is the one with the highest engine Horsepower. In the next visualization, you should calculate the highest horsepower amongst the cars available in the dataset. The visualization looks like this, as shown below. Here in this visualization, the Car Data Set acts as the Primary Data Source. The next step is to create some visualizations using the data available.įirst, create a visualization that explains cars’ annual sales in different regions of a country. Next, you need to select the common columns and apply the Custom Blend as shown below. This new window will provide you with columns from both datasets. Selecting the Custom option will open a new window. So, here you must perform the Custom Data Blending between the two columns as shown below. The two datasets have similarities between the Zone and Region columns. Next, Tableau automatically generates the blend between the data, as shown below.Īlso, the option of choosing the custom blend is available, as well. Now, both the datasets are visible on the tableau window. Next, you have to choose the data option from the toolbar and import the second dataset. Since the file type here is Excel, choose Microsoft Excel. So, the first step is to import the first dataset as shown below. Here, the objective is to try to import these two datasets and combine them to implement Data Blending in Tableau. In the following example, consider two different datasets, namely.
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