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Supported data sources

The DQC Platform supports both structured and semi-structured data across a wide range of source types. You can connect your data easily — without writing code — by using the built-in Integrations feature.

Data warehouses

  • Snowflake

  • Azure Synapse

  • Google BigQuery

  • Databricks

  • Amazon Redshift

  • MotherDuck

  • (and more)

These are ideal for large-scale, analytics-optimized data environments.

Databases

  • PostgreSQL

  • MySQL

  • MariaDB

  • SAP HANA

  • Azure SQL

  • OData (SAP, ...)

  • Clickhouse

The DQC Platform supports real-time and batch validation against common OLAP and OLTP database systems.

Static files

  • CSV

  • Parquet

  • Excel

  • JSON

  • PDF

These formats can be uploaded manually or synchronized via storage connectors. Useful for ad-hoc checks or external data feeds.

Cloud storage

  • AWS S3

  • Azure Blob

Use these connectors to import files from cloud buckets or share results externally.

Collaboration & document management

  • SharePoint

Connect Microsoft 365 collaboration platforms and read structured content (such as SharePoint lists) directly into DQC.

ERP, CRM, ...

  • SAP

  • Salesforce

  • Dynamics

  • ...

Use these connectors to import files from cloud buckets or share results externally.

Least privilege: one technical user per system

Every connector signs in to the source system with credentials you provide — ideally a dedicated technical user (service account) created specifically for the DQC Platform, one per connected system. Grant this user read-only access to exactly the schemas, tables, or views that should be quality-checked — and nothing else.

This is the most reliable way to technically enforce the principle of least privilege: the platform can never access more data than the technical user is allowed to see, because the boundary is guaranteed by your source system itself.

Access can then be narrowed further, layer by layer:

  • At the source: the technical user's permissions define the outer boundary of what DQC can read.

  • Within the platform: User management and access rights controls which users and groups may work with which connectors, tables, and rulesets.

  • Per ruleset: the ruleset check scope narrows a check even further — restrict rows via filters and show or hide individual columns.

Missing a connector?

If your desired data source is not listed:

  • Reach out to info@dqc.ai

    We’re continuously expanding our integrations based on demand.

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Supported data sources | DQC