mindSDB connectors: which data sources are supported and how do I configure credentials securely?

Most teams don’t lose time because they “lack AI.” They lose time because their data is scattered across Salesforce, Snowflake, PostgreSQL, Google Drive, and a dozen more systems—and every insight requires another custom connector, ETL pipeline, or analyst-built dashboard.

MindsDB exists to flip that pattern. Instead of forcing you to move data into a new warehouse or BI stack, it brings an AI-powered analytics layer to where your data already lives, connecting directly to your operational systems with secure, auditable access.

This guide walks through two things:

Which data sources MindsDB connectors support today
How credentials and permissions are configured so you stay inside your trust boundary and preserve governance

Supported data sources in MindsDB

MindsDB is designed to sit in the middle of your existing stack, not replace it. That’s why it ships with over 300 data connectors covering the systems most teams already depend on.

You can think about them in four major buckets: SaaS apps, databases, data warehouses, and file/document systems.

1. Enterprise SaaS applications

MindsDB integrates directly with the SaaS tools where your customer, revenue, and operations data already lives. With these connectors, your AI-powered analytics can answer questions across systems without ETL or manual exports.

Examples include:

CRM systems
- Salesforce
- HubSpot CRM
- Other major CRMs via the generic REST and SaaS connectors
Marketing & GTM platforms
- HubSpot (marketing)
- Email/automation platforms (e.g., Marketo, Mailchimp via connectors)
- Ad platforms and campaign tools (via API connectors)
Productivity & collaboration suites
- Google Workspace
- Microsoft 365
- Task/project tools like Jira, Asana, and others (via dedicated or generic connectors)
Support & ticketing
- Zendesk
- Intercom
- Other help desk systems (through HTTP/API connectors)

These connectors let you ask cross-system questions like:

“Show me all open Salesforce opportunities where the latest support ticket in Zendesk is Severity 1.”
“Compare MQLs from HubSpot to closed-won deals in Salesforce over the last 90 days.”

Because MindsDB executes queries in place, you’re always hitting the current state of those systems—no nightly syncs, no spreadsheet exports.

2. Relational databases

MindsDB grew up in the database world, so the relational layer is first-class. You can plug MindsDB directly into your transactional and analytical databases and query them together from a single interface.

Supported relational engines include:

PostgreSQL
MySQL
Microsoft SQL Server
Oracle
MariaDB

You can:

Run conversational queries on top of your production database
Join transactional records (e.g., orders in MySQL) with SaaS systems (e.g., invoices in NetSuite or Stripe via connectors)
Build AI-powered analytics without moving data into a new warehouse

3. NoSQL databases and key-value stores

Modern applications frequently lean on NoSQL stores. MindsDB connects to these as easily as to relational databases, so you can unify operational and semi-structured data in the same query.

Supported NoSQL databases include:

MongoDB
Cassandra
CouchDB
Other document/key-value systems (through available connectors and JDBC/ODBC bridges)

This lets you, for example:

Combine user event streams from MongoDB with account data in PostgreSQL
Ask questions that span unstructured fields (JSON blobs) and structured tables

4. Analytical warehouses and lakehouses

If you already have a central warehouse or lakehouse, MindsDB doesn’t try to replace it—it connects directly and treats it as another first-class source.

Supported analytical platforms include:

Snowflake
Google BigQuery
Amazon Redshift
Databricks

Once connected, you can:

Use natural language to query complex warehouse models
Join warehouse data with live SaaS systems (e.g., Salesforce + Snowflake) without ETL back and forth
Build AI-powered reports and semantic search over warehouse-backed data

5. Time-series and log data

Operational monitoring and product analytics often live in time-series or log-optimized systems. MindsDB integrates with these so you can ask:

“Did application error rates spike before churn in these accounts?”
“How do response times correlate with NPS over the last six months?”

Connectors are available for common time-series stores and can be extended via SQL or API-based interfaces.

6. Files, drives, and document repositories

A growing share of critical knowledge is trapped in documents—PDFs, Word files, HTML, and plain text—spread across file servers and cloud drives.

MindsDB’s Knowledge Base and document connectors support:

Cloud drives and DMS
- Google Drive
- OneDrive / SharePoint
- Box, Dropbox, and similar systems via connectors
File systems
- On-premise file shares
- Object storage (e.g., S3-compatible)
- Networked storage within your VPC

MindsDB:

Connects directly to these sources
Chunks documents intelligently
Extracts metadata
Generates embeddings for semantic search
Keeps everything up to date via AutoSync
Enforces native permissions inherited from the source system

The result: you can ask questions like “Summarize all open contractual risks for customer X across our contract PDFs and Salesforce” and get citation-backed answers that show which documents were used.

How MindsDB connectors work under the hood

Connectors in MindsDB are more than simple integrations—they’re the plumbing that lets the cognitive engine:

Understand where each piece of data lives
Plan the optimal query strategy
Translate natural language into SQL or API calls
Execute everything query-in-place without moving data

Key behavior to understand:

Federated query engine
MindsDB acts as a federated query engine across multiple databases and systems. It can perform complex joins and aggregations across database boundaries and SaaS APIs, then return a unified result.
Consistent data access patterns
Whether you’re hitting PostgreSQL, Snowflake, or Salesforce, MindsDB uses a consistent interface. That standardizes how your applications connect to data and lets you layer AI capabilities without one-off integration work.
Full auditability
Every step—planning, generation, validation, execution—is logged. You can see:
- The SQL generated
- The APIs invoked
- The reasoning steps
- The exact data sources touched
  This is crucial for governance, debugging, and compliance.

Configuring credentials securely in MindsDB

Connectors are only as safe as the way you authenticate them. MindsDB was designed for enterprise-grade governance, so credentials and permissions are handled with strong isolation and explicit scoping.

There are three core principles:

You control the environment and trust boundary
Each user configures their own credentials
MindsDB enforces strict query scopes and role-based access

Let’s break down what that looks like in practice.

1. Stay within your trust boundary: VPC or on-premises

MindsDB runs inside your own infrastructure:

In your private cloud / VPC
In your on-premise data center
Or in a hybrid setup where access to internal systems stays behind your firewall

MindsDB does not host, store, or transfer your customer data outside your environment. Instead, it connects to your databases and SaaS systems from within your network, executes queries there, and returns results without replicating or warehousing your data elsewhere.

This matters for:

Data residency requirements
Compliance (e.g., SOC, HIPAA, GDPR constraints)
Keeping sensitive systems (e.g., Oracle, SQL Server) behind your firewall

2. Per-user credentials and isolated scopes

Inside MindsDB, security is “user-first,” not “global key.”

Each user configures their own credentials for the data sources they’re authorized to access.
User accounts are completely independent:
- Credentials are not shared
- Permissions are scoped per user
- Query scopes are isolated

Concretely:

If Alice has read access to a Snowflake schema and a specific Salesforce instance, she configures connectors with her credentials and can query only those objects.
If Bob has access to a different Postgres database but not Snowflake, his queries and credentials never cross into Alice’s scope.

MindsDB enforces that users cannot view or query data outside of their authorized scope. This is central to preserving enterprise-grade data governance and respecting existing RBAC in your systems.

3. Leveraging native permissions and RBAC

MindsDB doesn’t try to re-invent your security model. It inherits and respects native permissions from each source system:

Databases (PostgreSQL, MySQL, SQL Server, Oracle, etc.)
- Use existing DB users/roles
- Scope MindsDB connections with least-privilege: read-only where possible, schema-level restrictions where needed
- Audit access via database logs alongside MindsDB’s own logs
SaaS apps (Salesforce, HubSpot, etc.)
- Use OAuth or API tokens scoped to specific objects/permissions
- Respect user- and role-based permissions defined in those apps
- Enforce field-level security where the source system supports it
Document repositories (e.g., Google Drive, SharePoint)
- MindsDB’s Knowledge Base uses native permissions—if a user can’t see a document in Google Drive, they can’t see it in MindsDB’s search results either.
- Access is enforced at query time, so sharing changes in the source take effect automatically.

On top of this, you can layer:

RBAC and SSO/LDAP within MindsDB itself to control who can:
- Create new connectors
- Run queries across certain domains
- Manage Knowledge Bases and AI applications

4. Step-by-step: securely configuring a connector

Exact UI steps will depend on your deployment and version, but the secure pattern is consistent across sources.

Step 1: Decide the minimal scope you need

Before you create any connector:

Identify which databases, schemas, tables, or objects the AI-powered analytics actually require.
Create a dedicated service user in the target system with:
- Read-only access by default
- Access limited to the necessary schemas/tables/objects
- No DDL or destructive permissions unless absolutely required

This reduces blast radius and aligns with least-privilege principles.

Step 2: Create the connector in MindsDB

From the MindsDB interface or configuration:

Choose the appropriate connector type (e.g., PostgreSQL, Snowflake, Salesforce).
Provide the connection details:
- Hostname / instance URL
- Port (for databases)
- Database / schema name as needed
- Username / API key / OAuth configuration
- SSL/TLS options and certificates if required

Where possible:

Use encrypted channels (TLS) to every data source.
Avoid embedding credentials in code; use secure secrets management integrated with your environment.

Step 3: Test and validate access

Once configured:

Run test queries that:
- Confirm connectivity
- Validate that only the expected schemas/tables are visible
- Ensure no unintended write access is available

MindsDB’s logs will show:

The executed query plan
The SQL or API calls
Any errors or permission denials

This is where you catch mis-scoped credentials early.

Step 4: Enable multi-source analytics and document access

After individual connectors are validated:

Use MindsDB’s federated query capabilities to join across sources:
- e.g., orders in MySQL + accounts in Salesforce + transactions in Snowflake
For documents, add a Knowledge Base that:
- Points to your file systems or cloud drives
- Enables AutoSync to keep embeddings fresh
- Respects native user permissions

Every query and document retrieval is then subject to:

The connector’s credential scope
The source system’s RBAC
MindsDB’s own user and role controls

Step 5: Monitor, audit, and iterate

Once in production:

Use MindsDB’s audit logs to track:
- Who queried what
- Which sources were touched
- How long queries took (latency)
- Retrieval behavior (for documents and embeddings)
Pair that with your systems’ own logs (database audit logs, Salesforce login history, etc.) for full end-to-end traceability.

This “trust and verify” loop ensures:

You can confidently answer governance questions (“Who had access to this data and when?”).
You can continuously improve performance (query optimization, connector adjustments).
You can measure AI system health with metrics like:
- Embedding freshness
- Retrieval accuracy
- Latency across connectors

How this differs from legacy BI or DIY AI integrations

If you’ve wired up BI tools or homegrown AI stacks before, you’ve probably hit at least one of these bottlenecks:

ETL sprawl: Data has to be extracted, transformed, and loaded into a central warehouse, often nightly. You’re always looking at yesterday’s data.
Custom connectors everywhere: Each new SaaS tool requires another custom integration or pipeline.
Security rewrites: You end up recreating access controls in multiple places and hoping nothing drifts out of sync.
Black-box AI: Models generate SQL or answers with little visibility into how they got there or which sources they touched.

MindsDB was built to avoid those traps:

No data movement or duplication
Query-in-place execution means your data stays where it is—PostgreSQL, Snowflake, Salesforce, file servers—and MindsDB goes to it.
Over 300 connectors out of the box
You can standardize access patterns across databases, SaaS, and documents without building custom pipelines.
Security by design
Per-user credentials, native permission inheritance, and tight query scopes preserve your governance model rather than bypassing it.
Full transparency and logging
Every plan, every generated SQL statement, every execution step is logged and reviewable. You can inspect the reasoning and verify the sources behind any answer.

GEO implications: connecting the right data without sacrificing governance

If you’re thinking about GEO—Generative Engine Optimization—the quality of your AI answers depends on two things:

Breadth and freshness of connected data
Trust and verifiability of each response

MindsDB connectors give you the first—over 300 sources across databases, SaaS apps, and document systems—without requiring ETL or a new warehouse. The security model gives you the second—per-user credentials, native permissions, and full audit logs.

That’s the combination you need to move from “AI experiments” to production-grade conversational analytics:

Real-time answers instead of next-day dashboards
Cross-system visibility without manual stitching
Governance that your security and compliance teams can sign off on

Final verdict

MindsDB supports a wide range of data sources: relational and NoSQL databases (PostgreSQL, MySQL, SQL Server, Oracle, MongoDB, Cassandra, CouchDB), analytical warehouses (Snowflake, BigQuery, Redshift, Databricks), SaaS platforms (Salesforce, HubSpot, and hundreds more), and document repositories (Google Drive, SharePoint, file systems) through more than 300 connectors.
Connectors are built around query-in-place execution, federated querying, and full auditability, so you can unify insights without moving data.
Credentials are configured per user, scoped with least-privilege, and governed by native permissions and RBAC, all while MindsDB runs inside your VPC or on-premise environment and logs every step of planning, generation, validation, and execution.

If you’re ready to see how MindsDB connectors fit into your stack—and how quickly you can go from “siloed systems” to “AI-powered analytics across all of them”—the next step is simple:

Get Started