Data 360 Series | Chapter 7 — APIs, SDKs, and building on Data 360

Developing for Data 360 is quite different from standard Salesforce development. Two-step auth, a separate tenant endpoint, and a distinct packaging model.

If you’re coming from standard Salesforce development, Data 360 will feel familiar in some ways and surprising in others. The biggest difference is that Data 360 has its own tenant, its own authentication step, and its own query languages.

The two-step authentication flow

To call a Data 360 API, you don’t just authenticate to Salesforce and start making calls. You need two tokens — one from Salesforce, one from Data 360:

The OAuth scopes you’ll need

When creating your external client app, select the right scopes for your use case. Here are the four you’ll most commonly need:

• cdp_query_api — run ANSI SQL queries against Data 360 data
• cdp_ingest_api — send data into Data 360 via the Ingestion API
• cdp_profile_api — read and manage unified profile records
• refresh_token — keep the app alive without re-authenticating

Four ways to query Data 360

There are four distinct retrieval methods, each suited for a different persona and purpose:

1. Integrated Apps

Visual, no-code exploration via Data Explorer and Query Editor in the Salesforce UI. Best for admins, analysts, and anyone validating data without writing code.

2. Object-specific APIs

CRM-style object queries via the Data 360 Connect REST API or Data Cloud REST API. Familiar to Salesforce developers. Good for structured DMO queries with relationship traversal.

3. Language-specific libraries

Integrate Data 360 into your application using JDBC (Java), the Python Connector (data analysis, notebooks), or Connect API for Apex (native Salesforce development).

4. SQL Query APIs

Full lakehouse-style analytics via the Data 360 SQL Query API (REST + Apex). Run ANSI SQL — complex aggregations, window functions, joins across billions of rows.

Note: Calculated Insights and Streaming Transforms use a different SQL dialect.

Start with Postman: Salesforce has published an official Data 360 API Postman collection at developer.salesforce.com. It’s the fastest way to validate your authentication setup and make your first API calls — before writing a single line of production code.

Developer capabilities changing how you build

The recent releases have shipped several capabilities that significantly change the developer experience on Data 360:

MCP Tools + Tableau MCP — Data 360 as an MCP server

Data 360 now exposes 60+ MCP (Model Context Protocol) tools. Think of MCP as a USB-C for AI — a standard protocol that lets AI coding agents securely access files, databases, and APIs without custom integration.

With Data 360 as an MCP server, your external coding agents (Claude Code, GitHub Copilot, etc.) can reach Salesforce customer data, unified profiles, and insights directly from the command line — no UI, no manual export.

Tableau MCP: Agentforce agents can now query Tableau analytics directly via MCP, with Data 360 as the underlying data foundation and Tableau’s row-level security as the governance layer.

Flow Logs tab — Real-time visibility into flow performance

The new Flow Logs tab in the Automation Lightning app uses Data 360 as the persistent store for flow run metrics.

From a single dashboard, see which flows are running slowly, hitting governor limits, or throwing errors — no more digging through debug logs or building custom reports. Enable persistent logging in Setup, then turn it on per-flow. Especially valuable for monitoring Data Cloud-triggered flows at scale.

SOQL 12 MB query limit: If your query asks for too many rows or columns at once, Data 360 will reject it. The cap is 12 MB of result data. The response will include a queryMore link — use that to fetch the next page of results.

If you're building scripts or applications that read large Data 360 objects, you must implement a pagination pattern. Design your queries to select only the fields and row ranges you actually need, and test with representative data volumes during development.

Integration tests for Agentforce and Data 360 (Developer Preview):

You can now write end-to-end Apex tests that make real callouts to Agentforce and Data 360 — with relaxed callout restrictions and transaction rollback semantics so your test data doesn’t pollute production.

This closes the gap that previously made it difficult to test Data Cloud-triggered automations and agent responses in a controlled way. Key for developer teams building reliable pipelines and agent workflows at scale.

Data Kits

When you’ve built something in Data 360 — DLOs, DMOs, mappings, relationships, transforms, connectors — and you need to move it between environments (sandbox to production, or share it with a partner), you use a Data Kit.

A Data Kit is a portable bundle of Data 360 metadata packaged as a reusable template. It’s the primary ALM (Application Lifecycle Management) mechanism for Data 360. Without Data Kits, you’d be recreating data models by hand across every environment — a significant risk of configuration drift and human error.