Salesforce Data 360 Connector

Use this guide to configure the Salesforce Data 360 connector. Salesforce Data 360 (formerly Data Cloud) organizes your data into data spaces. Collate brings each data space in as a database and groups its objects into three schemas:

• Data Lake Objects: Raw ingested data.
• Data Model Objects: Harmonized, modeled data.
• Calculated Insights: Metrics and measures.

Each object becomes a table, with its fields mapped to columns. Calculated Insights are ingested as views. Configure and schedule Salesforce Data 360 metadata workflows from the Collate UI:

• Requirements
• Metadata Ingestion
• Troubleshooting

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Requirements

Collate connects to Salesforce Data 360 through the Salesforce REST API using a connected app that authenticates with the OAuth 2.0 client credentials flow. You’ll need:

• A Salesforce connected app configured for the OAuth 2.0 client credentials flow. This gives you the Consumer Key and Consumer Secret that Collate uses to authenticate. See the Salesforce documentation on creating a connected app for the setup steps.
• The Data 360 (Data Cloud) API scopes, cdp_query_api and cdp_profile_api, granted to that connected app, so it can list data spaces and read object metadata. Collate only reads metadata. It does not query the underlying data.
• Permission for the integration user the connected app runs as to view the data spaces and the Data Lake Objects, Data Model Objects, and Calculated Insights you want to ingest. The connection test verifies this by listing the data spaces and the objects in the first one.

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Metadata Ingestion

To ingest metadata from Salesforce Data 360, you need to create a service connection. The service connects Salesforce Data 360 with Collate. Once you create a service, Collate automatically starts ingesting metadata.

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Step 1: Add New Service

Create the service as follows:

1. In the left navigation, click Connections.
2. On the Connections page, click Add New Service.

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Step 2: Select a Service and Connector

From the service type dropdown, select Database Services, then click the Salesforce Data 360 connector tile.

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Step 3: Add Service Name and Description

• Enter a unique, descriptive Service Name. Collate identifies services by their service name. Enter a name that distinguishes this deployment from other Salesforce Data 360 services you are ingesting metadata from.
• Optional: Enter a Description for the service.

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Step 4: Configure Connection Options

Specify where ingestion runs, provide your source credentials, and verify the connection.

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Select Ingestion Runner

Select an Ingestion Runner: the runner where the ingestion pipeline will execute.

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Enter Connection Details

Enter the connection details for Salesforce Data 360. The right-hand panel in the UI displays inline help for each field.

• Salesforce API Version: The Salesforce REST API version to call. Defaults to 63.0. Change this only if your Salesforce org requires a different version.
• Salesforce Domain: The domain of your Salesforce instance, used to build the login URL. Defaults to login. Use test for a sandbox, or your My Domain subdomain (for example, mycompany) for a custom domain.
• Pagination Limit: How many Data 360 objects to request per page when listing data spaces and objects. Defaults to 10. Valid values are 1–100. Raise it to reduce the number of API calls on large orgs.
• Consumer Key: The consumer key from your Salesforce connected app. This is the client identifier used for OAuth authentication.
• Consumer Secret: The consumer secret from your Salesforce connected app. This is the client secret used for OAuth authentication.

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Test Connection

1. Click Test Connection to verify the credentials.
2. After the test succeeds, click Save.

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Step 5: Configure Ingestion Options

In the What to Ingest step, use filter rules to control which data spaces, schemas, and tables Collate ingests from the source.

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How Filter Rules Work

Each rule matches asset names using one of five match types:

• contains: matches any name containing the value. For example, sales matches my_sales_data and sales_2024.
• starts with: matches names beginning with the value. For example, prod_ matches prod_db and prod_schema.
• ends with: matches names ending with the value. For example, _raw matches events_raw and logs_raw.
• is exactly: matches the exact name only. For example, analytics matches only analytics.
• matches regex: matches names using a regular expression. For example, ^prod_.*_v\d+$ matches prod_events_v1.

When include and exclusion rules both match an asset, the exclusion rule takes priority.

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Filter Options

The Database, Schema, and Table sections control ingestion for the corresponding asset type:

• Database: Controls which data spaces Collate ingests from the source.
• Schema: Controls which of the three object categories (Data Lake Objects, Data Model Objects, Calculated Insights) are included.
• Table: Controls which objects within the ingested schemas are included.

Each section provides the following controls:

• Scan Mode: Choose between the following scan modes:
  • Scan all: Ingests every asset of that type the connector can access. This is the default.
  • Only specific: Enables include rules so only assets matching at least one rule are ingested.
• Exclude system toggle: Use this toggle to filter out system-reserved names defined by the connector for that asset type.
• Always exclude: Add permanent exclusion rules (shown in red). Assets matching these rules are never ingested, regardless of include rules.
• Preview: Shows a real-time summary of what will be in scope based on your current rules.
• Include rules: In Only specific mode, click + Add to define a rule. Added rules appear as chips. An asset is included if it matches any rule.

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Step 6: Create & Deploy

Click Create & Deploy to deploy the agent and start the first metadata ingestion run. Collate saves the service configuration and immediately begins pulling metadata from the source. To monitor ingestion progress or view the service you just added, go to Connections in the left navigation and select your service.

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Configure Metadata Agent and Schedule Ingestion

The Metadata Agent extracts schemas, tables, columns, and other structural metadata from your source and keeps your Collate catalog in sync. It powers discovery, lineage, and governance across your data assets. When you click Create & Deploy, Collate automatically deploys a Metadata Agent for this service and triggers the first ingestion run. View its status and run history from the Agents tab on the service detail page. To configure the additional Metadata Agent and schedule ingestion, follow these steps:

1. In the left navigation, click Connections and select your service.
2. Click the Agents tab.
3. Click Add Agent and select Metadata from the dropdown. For some services, the dropdown is not available and clicking Add Agent takes you directly to the agent configuration page.
4. On the Configure Ingestion page, do the following and click Next.
   • Name this Ingestion: Enter a unique, recognizable name for this ingestion pipeline.
   • Agent Setup: Configure core parameters for metadata extraction. The following fields are available:

     Field	Default	Description
     Default Owner	—	Owner applied to any entity that has no owner from a more specific field below. Accepts a user or a team, by name or email.
     Service Owner	—	Not applied during ingestion. This field doesn’t affect the owners of databases, schemas, or tables.
     Database Owner	—	Owner assigned to every database this agent ingests. Accepts either one owner for all of them, or a mapping of specific owners per database.
     Database Schema Owner	—	Owner assigned to every schema this agent ingests. Accepts either one owner for all of them, or a mapping of specific owners per schema.
     Table Owner	—	Owner assigned to every table this agent ingests. Accepts either one owner for all of them, or a mapping of specific owners per table.
     Enable Inheritance	On	When on, a schema or table with no match of its own inherits the owner resolved for its parent. Default Owner still applies when nothing else resolves an owner.
     Query Log Duration	1	How many days of query logs to look back through when processing stored procedure results.
     Query Parsing Timeout Limit	300	How many seconds to spend parsing a single query before giving up on it and moving to the next. Raise this if large or complex queries are being skipped.
     Number of Threads	1	How many tables to ingest in parallel. Raising this can speed up ingestion on databases with many tables, at the cost of more load on the source.
     Incremental Extraction	Off	When on, a run extracts only entities that changed since the last successful run, instead of re-scanning everything every time. Faster on large, mostly-unchanged databases.
     Successful Pipeline Run Lookback Days	7	Only used when Incremental Extraction is on. How many days back to search for a prior successful run to use as the starting point for “what changed since then.”
     Safety Margin Days	1	Only used when Incremental Extraction is on. Extra days added before that starting point, as a buffer so changes that were still in flight when the last run finished aren’t missed.
     Extract JSON Schema	Off	Sample values in JSON columns to infer and ingest their schema, so the individual fields inside a JSON column are visible in Collate rather than just “JSON”. Requires SELECT permission on the sampled tables; if that permission is missing, the JSON column is still ingested, just without a detailed schema.
     JSON Schema Sample Size	10	Only used when Extract JSON Schema is on. Number of rows sampled per JSON column to infer its schema. A larger sample gives more accurate results but takes longer to run.

     For more information, see Hierarchical Owner Configuration.
   • Filter Patterns: Apply include or exclude rules to scope which databases, schemas, tables, and stored procedures this agent ingests. These follow the same filter options described in Step 5.
   • Scope & Behaviour: Control how the agent handles metadata during ingestion. Toggle each option on or off based on your needs:

     Toggle	Default	Description
     Include Tables	On	Ingest tables and their columns. Turn off to skip tables entirely, for example if this agent exists only to pull tags or stored procedures.
     Include Tags	On	Ingest tags already defined on tables/columns in the source system (for example, a classification tag applied in Snowflake) and bring them into Collate as tags on the same assets.
     Include Stored Procedures	On	Ingest stored procedures as their own assets, so they show up in the catalog and can be linked into lineage.
     Include DDL Statements	Off	Also ingest the raw CREATE TABLE / CREATE VIEW statement for each asset, so it’s viewable in Collate alongside the asset’s metadata.
     Include Owners	Off	When an ingested asset’s owner in the source system is an email that matches an existing Collate user, assign that user as the asset’s owner. Never overwrites an owner an asset already has in Collate.
     Include Custom Properties	Off	Ingest source-specific metadata that doesn’t map to a standard Collate field (for example, a database-specific attribute) into custom properties on the asset, if custom properties have been defined for that entity type.
     Mark Deleted Tables	On	If a table this agent previously ingested no longer exists in the source, soft-delete it in Collate too. Only affects tables within schemas this agent actually scans.
     Mark Deleted Stored Procedures	On	Same as Mark Deleted Tables, but for stored procedures.
     Mark Deleted Schemas	Off	If an entire schema this agent previously ingested no longer exists in the source, soft-delete the schema and everything in it.
     Mark Deleted Databases	Off	If an entire database this agent previously ingested no longer exists in the source, soft-delete the database and everything in it.
     Override Metadata	Off	On: values from the source (descriptions, tags, owners, display names) overwrite what’s currently in Collate, even if someone edited it there. A description or display name that the source doesn’t send never replaces an existing value, and existing table constraints are kept when the source sends none. Off: Collate only fills in fields that are still empty, so manual edits in Collate are preserved.
     Enable Debug Log	Off	Run this ingestion at DEBUG log verbosity instead of the normal level. Turn on when troubleshooting a failing or unexpected run; leave off otherwise, since debug logs are much larger.

     Note: Available toggles vary by connector. Stored procedure options only appear for connectors that support stored procedures.
     
   • Advanced Config: Driver-level options (connection arguments, scheme, and timeouts). Most connections never need these, and they vary by connector. This section also has one ingestion-scope toggle:

     Toggle	Default	Description
     Include Views	On	Ingest views in addition to tables. Turn off to skip views entirely. Because view lineage is derived from parsing each view’s SQL definition, turning this off also stops view-based lineage from being generated for this service.

     
5. On the Schedule Interval page, set when the agent runs:
   • Schedule: Choose a preset interval (Hourly, Daily, Weekly, Monthly) or enter a custom cron expression.
   • On-Demand: No automatic schedule. Trigger the agent manually when needed.
   
6. Click Add to deploy the agent.

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Troubleshooting