Transformations for Batch Data Transforms

Use transformations to perform calculations on and manipulate your data. For example, you can use a transformation to create a calculated field based on a formula. You add transformations inside a Transform node. You can string together multiple transformations to manipulate data sequentially.

• Bucket Transformations: Categorize Field Values
  Bucket your number, text, and boolean fields to group and organize data.
• Data Type Conversion Transformations: Convert Column Types
  The data type of a column determines how you can query that column’s data. For example, you can filter and group by a text or date column, or perform math calculations on a number column. Sometimes, fields are tagged incorrectly. If needed, use the column-type conversion transformations to convert columns to the correct types.
• Date and Time Transformations: Calculate on Date Fields
  Date fields are key to understanding trends over time or keeping teams aware of upcoming milestones and deadlines. To calculate based on dates or make them more helpful to your analysis, use date and time transformations. Insert a column with the current date and time in a specified format with the Now option. Calculate the duration between two selected date columns as days, months, or years using Date Difference. And add or subtract days or months from a date column with the Add or Subtract Days or Months function.
• Drop Columns Transformation
  Drop unwanted columns from a batch data transform. For example, you can add a Drop Columns transformation after a Formula transformation to drop input columns used for a calculated column.
• Extract Transformation: Get a Date Component
  Use the Extract transformation to pull a selected component from a date field into a new field. For example, extract the hour component from the Case Created Date field to analyze case creation by hour of the day.
• Edit Attributes Transformation: Change the Column Names and Value Formats
  Use the Edit Attributes transformation to make column names more descriptive and apply consistent formats to column values. You can set the labels and API names of all columns. You can also set the precision and scale for numeric columns, character length for text columns, and date formats for date columns.
• Flatten JSON Data
  Turn data ingested as a JSON string into tabular data in a batch transform. To use data in downstream features, such as identity resolution and segmentation, the data must be stored as rows and columns in a data lake object or data model object. You can flatten JSON data that’s stored as a single object, an array of objects, or an array of literals (scalar values).
• Flatten Hierarchical Data
  Use the flatten transformation to simplify nested, hierarchical data by converting it to a list. The list tracks where each node and the ones preceding it (referred to as its ancestors) appear within the hierarchy. To flatten data, specify the field that contains every node in the hierarchy and the field that contains their corresponding parent. After you run the transform, each record contains the list of ancestors.
• Format Dates Transformation: Standardize the Date Format in a Column
  Standardize the format for all dates in a text column with the Format Dates transformation. With a consistent format, you can correctly filter and group records by date, including filtering by date component, such as month. A consistent format also ensures that you can successfully convert the column type from Text to Date.
• Formula Transformation: Create a Calculated Column Based on an Expression
  Create a column that displays values based on a formula calculation. The calculation can include input from other columns in the same row or across rows. For example, you can create a Profit column based on input from Revenue and Cost columns. Enter formulas in SQL. SQL in Data 360 is a collection of standard and custom functions for numeric, string, and date data.
• Split Transformation: Break Up Column Values
  Split a string in a text column into two values by specifying a delimiter. To split column values into more than two parts, add multiple instances of the split transformation. For instance, you can use three split transformations to split an address into street address, city, state, and ZIP code.