Get Started With Batch Data Transforms in Data 360

Learning Objectives

After completing this unit, you’ll be able to:

Describe batch data transforms and when to use them.
Identify the different kinds of data transformation nodes.
Describe how to create a batch data transform.

As of October 14, 2025, Data Cloud has been rebranded to Data 360. During this transition, you may see references to Data Cloud in our application and documentation. While the name is new, the functionality and content remains unchanged.

Introducing Batch Data Transforms

In Data 360, data comes in through a data stream and resides in a data lake object (DLO). A DLO is the storage container for data ingested into Data 360. A data transform lets you access data in one or more DLOs and transform it to create your own set of data. A data transform can also be used to transform data that has been mapped to a data model object (DMO). A DMO is a structured representation of data entities and their relationships within Salesforce.

When to Use a Batch Data Transform

In contrast to a streaming data transform, which runs continually, a batch data transform runs on a scheduled basis. Batch data transforms also offer more functionality than streaming data transforms, which are based on a SQL statement. Batch data transforms contain a rich visual editor. You use this editor to combine data from multiple DLOs, create calculated fields with functions, and output data to multiple DLOs.

Use a batch data transform when you need to do complex data transformations or when you need the data updated on a scheduled basis. In a batch data transform, you can join, aggregate, and append data. You can also use formulas and filters.

Here are some scenarios when a batch data transform would be useful:

Calculate metrics for dashboards: Transform raw ingested data into usable KPIs like aggregates and computed formulas such as win rate or customer lifetime value.
Standardize values: Consolidate inconsistent ingested values such as “Salesforce,” “salesforce.com,” or “SFDC” into a single, uniform value.
Enrich with other data sources: Combine data from multiple sources like CRM, payroll, usage logs, and customer records for use in Data 360 services like identity resolution and calculated insights.

How Does a Batch Data Transform Work?

Using the visual editor, you drag and drop nodes to create the data you need. A node represents each step in the process. Nodes represent the source and target data and the various operations you perform on that data.

When you create a batch data transform, you can use the different node types to produce the exact data you need. Here are the different node types you can choose and what they do.

Node Type	What It Does
Aggregate	Rolls up data to a higher granularity using these functions: Average, Count, Maximum, Minimum, Stddevp, Stddev, Sum, Unique, Varp, and Var.
AI Functions	Uses AI models from Einstein Studio to make predictions.
Append	Combines rows from multiple sets of data.
Filter	Removes rows that aren’t needed in your target data.
Input	Contains source data in a DLO or DMO.
Join	Joins two input branches via a lookup or join. Each input branch must provide a key field. For example, the customer data input node and the ticket sales node each have a customer ID field.
Output	Contains the transformed data in a DLO or DMO.
Transform	Manipulates data by use of functions. With this node, you can calculate values, modify string values, format dates, bucket values, drop columns, process JSON values, and so on.
Update	Swaps column values with data from another data source when key pairs match.

Hands-On Challenge: Create a Batch Data Transform

Step 1: Create your Playground

To complete this module, you need a special limited-time custom playground that contains Data 360 and our sample data.

Click Create Playground.
Your new org is automatically attached to your Trailhead account!
Make note of your org's expiration date and complete this badge before then.

Step 2: Create your Data Streams

Before you can ingest account and opportunity data, you need to configure them as data sources in Data 360.

In Data Cloud, go to the Data Streams tab and click New.
Click Salesforce CRM under Connected Sources, and click Next.
Note the Salesforce Trial Org is preselected. From View Bundles, choose the Sales data bundle and click Next. The bundle has all the data streams you need to create your batch data transform.
Leave all standard fields selected and click Next.
On the next screen, click Deploy. If you get an error or data stream creation takes too long, cancel the action and try again.

Step 3: Create your Batch Data Transform

Now, let’s combine and filter the data to create our new DLO that shows us total large sales by industry.

Create a new batch data transform using Data Lake Objects.
1. Click the Data Transforms tab.
2. Click New Transform.
3. Select Batch Data Transforms and Next.
4. Select Data Lake Objects and Next. The Transform Builder appears.
Add the Opportunity object as input data.
1. Click Add Input Data.
2. When the Add Input Data table appears, select Opportunity_Home.
Uncheck the box next to Name. Select the following fields and click Next:
1. KQ_Id
2. KQ_AccountId
3. KQ_OwnerId
4. Account
5. Owner
6. Amount
7. Name
8. LeadSource
Opportunity_Home now appears as a node in the Transform Builder.
Add a filter to the Opportunity node called “Filter >30,000”.
1. Select the plus sign next to the Opportunity node.
2. From the list of options, select Filter.
3. Select the pencil icon next to the Filter 0 title.
4. Rename the node Filter >30,000.
5. Click Apply.
Configure the filter to only show deals over $30,000.
1. In the data preview table, click the rectangular plus sign under Filters.
2. Select Amount in the modal that appears.
3. For Operator, choose greater than.
4. For Numeric value, type 30000. Don’t include punctuation or currency.
5. Click Done and then Apply.
6. Filter >30,000 now appears as a node in the Transform Builder.
Join the Account object to the Filter >30,000 node.
1. Click the plus sign on the Filter >30,000 node.
2. From the list of options, select Join.
3. In the Select Input Data to Join table, select Account_Home.
Uncheck the box next to Name. Select the following fields and click Next.
1. KQ_Id
2. Id
3. Name
4. Industry
Use a Join node to pool your Account and Opportunity data.
1. In the Account_Home node, select Inner Join.
2. Under Join Keys, click the rectangle under Filter >30,000 and Account_Home.
3. In the Select Join Keys modal, for Filter >30,000, select Account.
4. For Account_Home, select ID.
5. Click Add.
6. Rename the Join node to Get Accounts Info
7. Click Apply.
Use an Aggregate node to combine large deal amounts from the same industry.
1. Click the plus sign on the Get Accounts Info node.
2. Select Aggregate.
3. In the data preview table, click the plus sign under Aggregates.
4. Select Sum and then Amount.
5. Click Done.
6. In the data preview table, click the plus sign under Group Rows.
7. Select Industry in the modal and click Apply.
8. Rename the Aggregate node to Total Sales by Industry.
Create a new DLO that uses this batch data transform to track your large sales by industry.
1. Click the plus sign on the Total Sales by Industry node.
2. From the list of options, select Output.
3. Keep Create New and Replace mode selected.
4. Under Object Name, type Large Sales by Industry
5. Under Object Category, select Other.
6. Under Primary Key, select Account_Home.Industry__c.
7. Click Apply.
8. Rename your Output node Large Sales by Industry.
9. In your data preview table, you can now see which industries drive your biggest deals.
In the Transform Builder, click Save.
Name your transform Large Sales by Industry and click Save.
Run the transform.
1. Click Back to Data Transforms.
2. In the table, find the transform you just created called “Large Sales by Industry.”
3. Click the down arrow at the end of the row, select Run Now.
4. Click Run in the modal that appears.
5. Wait for the transform run to complete. This will take a few minutes.

The Batch Data Transform Builder with nodes that manipulate opportunities, accounts, sales amounts, and industries.

Preview Results

After the transform successfully completes, go to Data Explorer to open the Large Sales by Industry DLO and inspect the data.

Check Challenge

It’s time for you to check your work. Make sure you followed all the previous steps, and click Check Challenge to Earn 500 Points to move on to the next unit.

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