CoComply · Module 10

Data
Onboarding

Where the platform meets the estate — metadata-first, without moving underlying data.

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Capability Overview

Sources in, governance at scale

Sources are registered, connectors configured, and assets flow into governance through guided registration or bulk import. Connecting a source begins harvesting structure and context immediately — no data movement required.

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Pages in This Section

Three onboarding surfaces

PAGE 1

Source Registration

Guided registration of databases, warehouses, lakes, pipelines & BI platforms — with ownership and domain captured at entry.

PAGE 2

Connectors & Integrations

Configuration and health monitoring for every connector, covering auth models, harvest scheduling, and scope controls.

PAGE 3

Bulk Import

Template-driven mass registration of assets, CDEs, and mappings for programs migrating from spreadsheets or legacy tools.

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Integrations Reference

One connector fabric, three interaction patterns

PATTERN 1

Read-Only Metadata APIs

Catalog, schema, and config harvesting through native metadata services. Incremental — never touches row-level data.

PATTERN 2

Query & Job Log Ingestion

Runtime execution traces from query history, audit logs, and pipeline runs — the richest as-executed lineage.

PATTERN 3

In-Environment Pushdown

Quality & profiling jobs run inside the client's own cloud. Only scores, outcomes, and metadata return.

04

Service Integration Summary

GCP, AWS & Azure — one fabric

Function	Google Cloud	AWS	Azure
Warehouse & query	BigQuery	Redshift, Athena	Synapse, Fabric
Catalog harvesting	Dataplex, Data Catalog	Glue Data Catalog	Microsoft Purview
Storage & lake	Cloud Storage	S3, Lake Formation	ADLS Gen2
Pipeline metadata	Dataflow, Composer	Glue ETL, Step Functions	Data Factory
Log ingestion	Cloud Audit Logs	CloudTrail, query logs	Azure Monitor
In-tenant AI (optional)	Vertex AI	Bedrock, SageMaker	Azure OpenAI, Azure ML

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Data Residency by Design

The estate stays where it is

The same fabric extends to Snowflake, Databricks, on-premises databases, and mainframe sources.
Harvesting is incremental and read-only — it never touches row-level data.
Pushdown execution keeps computation inside the client's own tenancy.
In-tenant AI is optional for institutions requiring inference inside their own cloud.

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Key Takeaway

Onboarding starts the whole engine

Because connection begins metadata harvesting immediately, Data Lineage and Data Quality start working from the moment a source is registered — governance at scale, without moving the data.

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