Transformations for CRM Analytics Dataflows

A transformation refers to the manipulation of data. You can add transformations to a dataflow to extract data from Salesforce objects or datasets, transform datasets that contain Salesforce or external data, and register datasets.

For example, you can use transformations to join data from two related dataset and then register the resulting dataset to make it available for queries.

• append Transformation
  The append transformation combines rows from multiple datasets into a single dataset.
• augment Transformation
  The augment transformation adds columns to a dataset from another related dataset. The resulting, augmented dataset enables queries across both related input dataset. For example, you can augment the Account dataset with the User dataset to enable a query to return account records and the full names of the account owners.
• computeExpression Transformation
  The computeExpression transformation enables you to add derived fields to a dataset. The values for derived fields aren’t extracted from the input data source. Instead, CRM Analytics generates the values using a SAQL expression, which can be based on one or more fields from the input data or other derived fields. For example, you can use an expression to assign a value to a field, concatenate text fields, or perform mathematical calculations on numeric fields.
• computeRelative Transformation
  You can use the computeRelative transformation to analyze trends in your data by adding derived fields to a dataset based on values in other rows. For example, to analyze sales pipeline trends, create derived fields that calculate the number of days an opportunity remains in each stage. You can also calculate the changes to the opportunity amount throughout the stages of the opportunity.
• delta Transformation
  The delta transformation calculates changes in the value of a measure (number) column in a dataset over time. The delta transformation generates an output column in the dataset to store the delta for each record. Create deltas to make it easier for business analysts to include them in queries.
• digest Transformation
  The digest transformation extracts synced connected data in a dataflow. Use it to extract data synced from an external Salesforce org, or data synced through an external connection. Use the sfdcDigest transformation to extract from your local Salesforce org.
• dim2mea Transformation
  The dim2mea Transformation creates a new measure based on a dimension. The transformation adds the new measure column to the dataset. The transformation also preserves the dimension to ensure that lenses and dashboards don’t break if they use the dimension.
• edgemart Transformation
  The edgemart Transformation gives the dataflow access to an existing, registered dataset, which can contain Salesforce data, external data, or a combination of the two. Use this transformation to reference a dataset so that its data can be used in subsequent transformations in the dataflow. You can use this transformation and the augment transformation together to join an existing dataset with a new dataset.
• export Transformation
  The export transformation creates a data file and a schema file from data in a specified source node in your dataflow. After the dataflow runs, Einstein Discovery users can access these files through the public API.
• filter Transformation
  The filter transformation removes records from an existing dataset. You define a filter condition that specifies which records to retain in the dataset.
• flatten Transformation
  The flatten transformation flattens hierarchical data. For example, you can flatten the Salesforce role hierarchy to implement row-level security on a dataset based on the role hierarchy.
• prediction Transformation
  The prediction transformation produces an Einstein Discovery prediction for a dataset. Einstein Discovery uses predictive analytics, which analyzes historical data (based on data mining, machine learning, and predictive statistical modeling) to identify patterns and predict future outcomes.
• sfdcDigest Transformation
  phThe sfdcDigest transformation generates a dataset based on data that it extracts from a Salesforce object. You specify the Salesforce object and fields from which to extract data. You can choose to exclude particular fields that contain sensitive information or that aren’t relevant for analysis.
• sfdcRegister Transformation
  The sfdcRegister transformation registers a dataset to make it available for queries. Users can’t view or run queries against an unregistered dataset.
• sliceDataset Transformation
  The sliceDataset transformation removes fields from a dataset in your dataflow, leaving you with a subset of fields for use in a new dataset or in other transformations. This allows you to create multiple datasets, each with different sets of fields from a single dataset.
• update Transformation
  The update Transformation updates the specified field values in an existing dataset based on data from another dataset, which we call the lookup dataset. The transformation looks up the new values from corresponding fields in the lookup dataset. The transformation stores the results in a new dataset.
• Overriding Metadata Generated by a Transformation
  Optionally, you can override the metadata that is generated by a transformation. You can override object and field attributes. For example, you can change a field name that is extracted from a Salesforce object so that it appears differently in the dataset. To override the metadata, add the overrides to the Schema section of the transformation in the dataflow definition file.