Month: April 2020

In my previous blog, I talked about creating a dataset from a csv file (Click Here). Now we would be talking about another approach on creating a dataset using dataset builder and dataflow.

A big question – what is a dataflow ?

A Dataflow in simple terms is just a way in which you can start with a particular set of data and then change it, build on it, combine with other data, derive new columns based on some values in other columns, etc. to come up with your final data. So simply it a set of steps or instructions to be performed on a data to use it for further analytics and insights.

Here we would be creating a dataset for Accounts and its owner information (Full Name and Photo)

STEP 1 : Click on Create Button & Select Dataset

There are multiple ways to start creating a dataset. After entering Analytics Studio, you can either click on the “Create” button (on the top right corner) or you can go into your Einstein Analytics app and click the button, etc. Select Dataset.

STEP 2 : Choose Data Source

Choose Salesforce Data as the datasource.

STEP 3 : Add New Dataset Details

Add the new dataset name and whether you want to create a new dataflow or add it to an existing dataflow. Lets name the dataset as Accounts and choose a new dataflow – Account with Owner Details.

STEP 4 : Select the objects and Fields

Select the root or base object to start building the dataflow. This is the object from which we will start building our dataflow. Ideally you should choose the base or the main object from which you want to create further associations.

We will choose Account as the Base object.

Click on the plus symbol which will allow you to add fields for the Account object that you want to be included in your dataset.

We wanted Account with Owner Details. Click on the Relationship Tab to Associate with Owner fields.

Click on the User (Owner ID) node (plus symbol) and select the fields. In case the list is long, you can even search for the field. We will search for Full Name field. When finished, click on the Next Button.

STEP 5 : Select the App

Select the App where you want to save your dataset.

Einstein now does the magic of creating the dataflow and also running the dataflow in the background.

You can now see the dataflow that has been created. In case you choose not to perform the above steps, you can directly create the dataflow as well. I will cover the dataflow in the upcoming blogs.

However to give an idea, the dataflow created the following :-

• 2 SFDCDigest Node – These nodes represent that data is coming from Salesforce Objects
• 1 Augment Node – This node is to join the Account and the user object i.e. based on the User Id in the Owner field, join the Account data with other user information.
• 1 SFDCRegister – This is the node for creating the dataset.

You can check the “Monitor” tab and see your dataflow running with stats of every step.

You can edit the dataflow directly, if required. I will cover that in the upcoming blogs.

Lets start creating the first dataset (you can read more about dataset in my blog on Datasets – Click Here) using a CSV file.

STEP 1 : Click on Create Button & Select Dataset

There are multiple ways to start creating a dataset. After entering Analytics Studio, you can either click on the “Create” button (on the top right corner) or you can go into your Einstein Analytics app and click the button, etc. Select Dataset.

STEP 2 : Choose Data Source

Choose CSV File as we are creating dataset from csv.

STEP 3 : Upload the CSV File

Upload / Drag and Drop the csv file to upload. Here we are uploading Opportunity Data. I am using the data available in my org (built as part of one of the trailhead modules)

STEP 4 : Define the properties of the Dataset

This is the place to give the name and the App where you want to save the dataset. You can also check the file properties (Auto detected) in case it needs a change. Data Schema file will be covered in later blogs, however the file (also known as Extended Metadata or XMD file) is used for some formatting options of the dataset fields and their values. Click Next button to move to the next step.

STEP 5 : Preview and Edit the Field Attributes

This step now allows you to preview the data being loaded and to change any settings. This is the step where you define the type of column (Dimension, Measure and Date) and set its attribute. By default Einstein Analytics automatically detects the data type but you can change it as well.

In this step, you can check the fields that would be part of the dataset. As we can see we have Account.Name, Amount, CloseDate, Id, etc. as the columns (or fields) of the dataset.

You can preview the data getting loaded. Here I have a screen of 3 columns, where we will now make some changes.

We have a column as Account.Name. This is because in Opportunity Data, we also extracted Parent Account’s Name field and in the csv it got extracted as Account.Name. Further to note Analytics Studio automatically picked the field as dimension (qualitative value).

You can choose to fix column name in CSV file before loading or you can change the field label in this Step. So now we will change Account.Name to Account.

The Next field to check is the Amount field. This field is auto-detected as a Measure (value) field as the data in this column is numeric.

Since its a currency, we will change the number format to 0.00. Furthermore we will add a grouping symbol and a Decimal Symbol to our data.

We will now check the Close Date field. This field is of type Date which has been auto-picked by Einstein Analytics.

After doing checks / changes to the attributes of the fields, we move to the next step.

STEP 6 : Einstein builds Dataset

This is where Einstein Analytics Engine builds the dataset. It can take a few seconds for the while to load. Though I like the page as we can see Mr. Einstein doing the magic.

STEP 7 : Review the prepared Dataset

Once the dataset is loaded, you will automatically get transferred to the dataset page. There you can check your dataset properties and also add Security predicates and XMD file. A good check here is to check the number of rows loaded.

You can even the view the dataset data by clicking “Explore” button. This opens a Lens where you can add your filters, bars (or the dimension) , bar length (measure or calculation on measure)metc. and choose a chart to display.

I added a filter saying Account contains “Einstein”. Bar Length has been kept as Sum of Amount. Bars have been kept as Closed Date (Quarter) and Account (and not Account.Name). Just to add, we only loaded Closed Date, however Einstein Analytics provided us with various groupings like Year, Quarter, Month, Year-Month, Year-Month-Day, etc.

We will soon use this dataset in our dashboard.