Transforming Data with Salesforce Data Cloud: A Complete Guide to Batch & Stream Data Transforms

In today’s digital era, data is the anchor of every successful business strategy. Organizations rely heavily on rich, timely, and reliable data to improve customer engagement, optimise operations, and fuel smarter decision-making. As customer interactions become more dynamic, businesses need systems that not only collect data but transform it into actionable intelligence.

Salesforce Data Cloud solves this challenge with powerful batch and stream data transforms, enabling organizations to prepare, unify, and activate data with precision. This blog explores how these transforms work, when to use them, and how they power modern applications such as Marketing Cloud Next—all based on verified Salesforce capabilities.

Data Transforms with Marketing Cloud Next

What Are Data Transforms in Salesforce Data Cloud?

Data transforms allow businesses to convert raw, ingested data into structured, usable, and enriched datasets. With Data Cloud, transforms can be executed in two modes:

• Batch Transforms for scheduled, large-scale data preparation

• Stream Transforms for near real-time enrichment

Both methods help organizations bring data closer to analysis, activation, and customer experiences.

Batch Data Transforms: Scalable, Repeatable Data Preparation

Batch data transforms are ideal for processing large volumes of data on a scheduled cadence. They help teams apply business logic and merge, calculate, or reshape datasets for downstream use.

Key Characteristics

• Full Data Refresh: Each run replaces the output with a fresh dataset.

• Multiple Sources: Works with Data Lake Objects (DLOs) and Data Model Objects (DMOs).

• Visual Builder: Provides a user-friendly editor for joining data, adding calculated fields, and modelling outputs.

• Defined Targets: Results are written back to one or more target Data Lake Objects.

Example Use Case

Imagine you are preparing a unified dataset that merges heart disease data with older patient records. Using a batch transform, you can combine both sources and generate a clean output, such as “TGT Health Data”, ready for clinical analytics or segmentation.

Once the transform completes, you can verify the results using Data Explorer—ensuring your DMO or DLO reflects the expected fields and records.

When to Use Batch Transforms

• Preparing historical datasets for warehousing

• Daily or weekly data refreshes

• Normalising CRM or clinical data for analytics

• Financial or operational reporting workflows

Batch transforms are the backbone of structured data modelling.

Stream Data Transforms: Real-Time Data Intelligence

Stream data transforms operate in near real-time, processing data as it arrives. This is suited for high-velocity use cases where timeliness is mission-critical.

Key Characteristics

• Continuous Data Processing: Runs as events flow into Data Cloud.

• Real-Time Enrichment: Ideal for cleaning, formatting, or enriching data instantly.

• Supports SQL Expressions: Allows use of operators such as UNION to normalise data.

• Live Monitoring: Track processed, failed, and removed records via the monitoring panel.

Example Use Case

A classic use case is real-time fraud detection, where credit-card transactions from multiple systems are normalised instantly. As irregular patterns emerge, systems can act immediately—showing the power of streaming transforms.

When to Use Stream Transforms

• IoT sensor data

• Real-time customer behaviour

• Live event tracking

• Immediate marketing actions (e.g., click triggers, page views)

Stream transforms help businesses maintain responsiveness at digital speed.

Batch vs Stream: Choosing the Right Method

Aspect	Batch Transforms	Stream Transforms
Processing Mode	Scheduled	Continuous
Use Cases	Reporting, warehousing, periodic updates	Real-time insights, behavioural triggers
Ideal For	Large datasets	High-velocity, event-based data
Output	Rebuilt dataset each run	Incremental processing

Most organizations end up using both—batch for structured modelling and stream for responsive insights.

Monitoring & Governance

Data Cloud provides monitoring for both transform types:

• Track run history, processing metrics, success/failure counts, and data lineage

• View refresh status directly in the Data Transforms workspace

• Apply governance and access controls that are consistent with Data Cloud’s security model

This helps admins maintain trust, transparency, and control over enterprise data pipelines.

Where Transformed Data Goes Next: Aligning to Customer 360 Data Model

After transforming the data, the next step is to ensure it aligns to Salesforce’s Customer 360 canonical data model. This includes mapping attributes such as:

• Customer identifiers

• Engagement data

• Preferences and consent

• Transaction or behavioural signals

This mapping ensures the transformed datasets fit seamlessly into downstream activation, especially in marketing and personalization use cases.

How Transformed Data Powers Marketing Cloud Next

Because Marketing Cloud Next is built natively on Data Cloud, transformed data becomes immediately usable across marketing functions without duplication or syncing.

How it works (Validated):

• Transformed data appears directly inside Audience Builder

• Marketers can segment on fields from Data Kits and transformed DMOs

• Real-time updates from stream transforms instantly reflect in journeys

• Email/SMS/WhatsApp personalization uses fields from Data Cloud

• Identity stitching in Data Cloud ensures consistent customer profiles

• Governance and permissions applied in Data Cloud carry into MC Next

This creates a unified, modern marketing architecture where clean, transformed, real-time data flows directly into activation—without separate data models or sync jobs.

Conclusion: A Balanced Data Strategy for Modern Enterprises

Batch and stream transforms form the foundation of modern data processing in Salesforce Data Cloud. Batch transforms excel at large-scale modelling, while stream transforms drive immediate insights and actions. Together, they help businesses ensure that data is not only collected but prepared, enriched, unified, and activated.

With the rise of Marketing Cloud Next, these capabilities become even more powerful—fueling intelligent segmentation, real-time personalization, and unified customer engagement across channels.

A thoughtful combination of both transform types enables businesses to deliver truly connected, data-driven experiences at scale.