> ## Documentation Index
> Fetch the complete documentation index at: https://docs.maia.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Connections and credentials overview

export const m_runner = "Maia runner";

export const maia = "Maia";

Before you can run pipelines in {maia}, you need a connection to your cloud data platform. This section covers platform-specific setup for Amazon Redshift and Databricks, and secure connectivity options for AWS-hosted resources.

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## Amazon Redshift

[Amazon Redshift](/docs/guides/amazon-redshift-connection) and [Amazon Redshift Serverless](/docs/guides/amazon-redshift-serverless-connection) are both supported as cloud data warehouses. Redshift Serverless is optimized for Full SaaS deployments, but works with Hybrid SaaS too.

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## Databricks

{maia} connects to Databricks on both AWS and Azure, in Full SaaS and Hybrid SaaS deployments. You can connect your workspace using [Databricks Partner Connect](/docs/guides/databricks-partner-connect), and configure a [Databricks Jobs Compute cluster](/docs/guides/databricks-jobs-compute) to run and schedule transformation pipelines cost-effectively. For general setup, read the [Databricks connection guide](/docs/guides/databricks-cdw-guide).

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## Secure connectivity with AWS PrivateLink

[AWS PrivateLink](/docs/guides/aws-privatelink) lets you connect to an AWS VPC without exposing traffic to the public internet. Use it to secure connections to Snowflake, Amazon Redshift, or other AWS-hosted data sources. If you're running a {m_runner} in your own AWS account under Hybrid SaaS, read [Connecting {m_runner}s via AWS PrivateLink](/docs/guides/connecting-via-privatelink) instead.