Lesson 4: Tableau CRM Data Integration Basics 📊 📈 📉
Kick Off the Data Journey
How Do You Create a Dataset?
Plan the Data Journey
Step 1: Identify Your Data Requirements
When you identify data requirements, think about what data you need, where it’s located, and if it needs to be combined with other data.
Step 2: Map the Data Journey
Extract External Data into Tableau CRM
We can upload external data in a .csv format through the user interface. When we upload a .csv file, Tableau CRM infers the metadata about each column in the .csv file. Metadata describes the structure of the data in the file, like the data type, precision, and scale. If we upload a .csv from the user interface, Tableau CRM automatically generates the metadata, which we can preview and change.
Note: You can also use the External Data API to programmatically upload .csv files. Use the API to take advantage of more features, like performing incremental extracts and performing append, delete, and upsert operations.
Extract Salesforce Data into Tableau CRM
What is a dataflow
You use the dataflow to extract data from Salesforce objects. The dataflow is a set of instructions in JavaScript Object Notation (JSON) that runs to extract data and create datasets. These instructions specify which objects and fields you want to extract data from and the names of the datasets you want to create. The dataflow also has other uses, such as joining data together.
Since there’s a chance the dataflow is already in use, it’s a good idea to make a backup before you add new instructions. The dataflow in the Developer Edition org isn’t already in use, but let’s back it up anyway to see how it’s done.
Gear icon >> Data Manager >> Dataflows & Recipes >> On the right of the Default Salesforce Dataflow, click Drop Down >> Run Now >> Monitor >> After the dataflow run completes, click the Dataflows & Recipes tab >> On the right of the Default Salesforce Dataflow, click Drop Down >> Download >> Save the JSON file locally and keep it as a backup of your existing dataflow.
Create a Dataset with the Dataset Builder
Data geeks refer to the root as the grain of a dataset — the unit of data in each row. In our dataset, each row is an opportunity, so the opportunity record is the grain. It’s an important concept when you’re joining data together from different sources, as you discover later.
Monitor the Dataflow and Verify the New Dataset
Schedule the Dataflow
Prepare Data in a Dataflow
In addition to extracting, the dataflow is also a great data preparation tool. We can use it to filter data, add and remove fields, add or update rows from another dataset, and add calculations to your data.
Prepare Data in a Dataset Recipe
Use Data Prep, a user interface tool, to create dataset recipes that take data from existing datasets, prepare it, and output the results to a new dataset. Use a recipe to combine data from multiple datasets, bucket the data, add formula fields, and cleanse the data by transforming field values. We can remove fields and filter rows that we don’t need before you create the target dataset.
Create a Dataset Recipe
Happy Learning! ✍️
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