Once you accumulate all of the required information, you’ll understand that the info is various and combined up. Everything is tracked otherwise throughout totally different information. There are even separate information which can be from a particular class that is all the info for only a single 12 months. Sorting by way of all of that may appear extraordinarily complicated and tough however Tableau Prep might help care for this. You will find out about every thing there is to find out about Tableau Prep on this tutorial.
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What is Tableau Prep?
Tableau Prep helps with the cleansing, shaping, and group of knowledge earlier than it may be analyzed. It can collect a substantial quantity of knowledge from diverse sources and remodel them. Its easy drag-and-drop options shortly streamline the difficult duties of pivots, unions, joins, mixture, and so forth. Once the info is clear, it may be subsequently used within the Tableau Prep output as the info supply for Tableau Desktop for evaluation.
There are pattern datasets included in Tableau Prep just like the Sample Superstore that you should use to create a circulate for Please observe that the most recent model of Tableau Prep Builder shall be used right here. Results might differ for earlier variations.
Tableau Prep Builder Download and Installation
To obtain and set up Tableau Prep Builder, you will want a Creator product key and the installer. Go to the Customer Portal for probably the most newest and up to date model of Tableau Prep Builder.
The installer may be downloaded from the Product Downloads part in accordance with your working system. You will get the installers from the Product Downloads and Release Notes web page. There is additionally a free trial model out there.
Please observe that Tableau Prep Builder is particularly constructed to work with Tableau Desktop. So, make certain to put in it on the identical system that runs Tableau Desktop and never the one which is operating Tableau Server. Tableau Server Resource Manager (SRM) can not differentiate between the Tableau Prep protocol server course of and the Tableau Server protocol server course of. SRM might finish the protocol server strategy of Tableau Prep Builder in case the pc sources are exhausted, and this doesn’t have a restoration mechanism.
Tableau Prep Builder mechanically creates a My Tableau
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Tableau Prep Tutorial
For this Tableau Prep Builder tutorial, you’ll use the Superstore information, which may be downloaded from right here.
When you open Tableau Prep, you will note the next display with the Connections pane:
First, you’ll have to join to an information supply. This will create an enter step. You can select to connect with over 25 information sources with Tableau Data Prep. Go to Connections, choose Microsoft Excel, and choose the downloaded Superstore information file. Once the file is imported, you will note the info as proven under:
There are numerous sheets on the left-hand aspect within the supply file (or Excel file, on this case). You have to pull Orders from the left into the white space on the best, and you will note a listing as proven under:
You can choose any area/fields you need from right here, however for the aim of this tutorial, you’ll choose Category, Order Date, Customer ID, Product ID, and Sales.
Once your required fields are chosen, you’ll intention to discover a buyer’s first buy date, the gross sales, classes, and the overall distinct Product IDs. You will see totally different operations to select from if you increase Order on the prime window. Then, you’ll choose Aggregate as highlighted within the picture under:
Now, you’ll drag and drop the Customer ID to Grouped Fields on the best and the Order Date to Aggregated Fields.
Now, you have to select the Aggregation degree for the Order Date as Minimum (see under).
Similarly, you’ll add a brand new Aggregate (Aggregate 2) from the supply orders. This time, Order Date and Customer ID needs to be introduced into the Grouped Fields and Product ID, Sales, and Category into the Aggregated Fields.
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You want to ensure to decide on the Aggregation degree for Sales as Sum and for Product ID and Category as Count Distinct.
Now, let’s discover ways to apply the Join operation. In this case, becoming a member of each the aggregated outcomes gives you the primary Purchase Date, Count of Products, Sales, and Categories. So, you’ll choose Join from Aggregate 2 as proven under:
Now, you have to deliver Aggregate 1 to Join 1, and you will note the next modifications:
The subsequent step shall be so as to add the Join situation on Customer ID and Order Date.
Now, you could have all of the fields required in addition to the duplicate ones for Customer ID and Order Date. You can dispose of the duplicates within the cleansing step.
For readability, it is higher to rearrange the info and rename the fields—Order Date as 1st Purchase Date, Product ID as 1st Purchase Products, Sales as 1st Purchase Sales, and Category as 1st Purchase Categories.
This provides you Customer IDs with their first date of buy, gross sales, and the overall distinct merchandise and classes.
Next, you’ll apply one other Join operation on Aggregate 1 and Aggregate 2,however this time, with a barely totally different standards. It could have an identical Customer ID, however the Order Dates is not going to be the identical.
Again, you’ll add Clean Step and take away the duplicates, Customer ID-1 and Order Date-1. Your display will look one thing like this now:
Next, you’ll apply a brand new Aggregate (Aggregate 3) to Clean 2 to search out the minimal Order Date as the primary Order Date was eliminated when Join 2 was utilized. You want to pull Customer ID to Grouped Fields and Order Date to Aggregated Fields and alter the Order Date degree to minimal.
You will create Join 3 after Aggregate 3 and be a part of Aggregate 2 with it on Order Date and Customer ID as given within the picture under:
Now, you could have the output in addition to the duplicate fields, Customer ID-1 and Order Date-1. You will take away these fields after making use of the Clean Step operation (Clean 3).
You will now rearrange and rename the fields, like earlier than, for readability—Order Date as 2nd Purchase Date, Product ID as 2nd Purchase Products, Sales as 2nd Purchase Sales, and Category as 2nd Purchase Categories.
Now, you could have two datasets (outputs from Clean 1 and Clean 3) prepared at hand. Now, you’ll mix them utilizing Join on Customer ID as proven within the under pictures:
You will see a reproduction Customer ID within the Join Results panel. You will take away the duplicate entry by making use of Clean Step after which rearrange the info for correct association.
Your closing display will appear to be this:
You can export the info into both a .csv, .hyper, or .tde file utilizing the Output operation.
Then, you have to click on on Run Flow by which you’re going to get a affirmation like under:
Now, this saved file is prepared for use in Tableau for information visualisation and additional evaluation.
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