Connecting Collate to Amazon Redshift: A Guide to Metadata Management

Introduction

Collate is an AI-enabled platform designed to help data teams organize, govern, and optimize their data assets. It focuses on automating tasks like data discovery, quality assurance, observability, and compliance to boost productivity and reduce costs. By using Collate, you can quickly find and collaborate on key data assets across various sources, with over 90 connectors. Generate secure and permission-aware insights from a unified knowledge graph, ensure regulatory compliance, such as GDPR, and build a self-service data culture that accelerates development and problem resolution.

Amazon Redshift remains one of the most popular data warehousing solutions in the AWS ecosystem, and for good reason. When paired with a robust metadata management platform like Collate, organizations can unlock powerful insights about their data assets while maintaining governance and quality standards.

This resource will walk you through the straightforward process of connecting Collate to Redshift and exploring its comprehensive agent capabilities. A companion video to this blog is available on YouTube.

Setting Up the Connection: Surprisingly Simple

The initial setup process for connecting Collate to Redshift is straightforward. Starting from Collate's homepage, the process begins in Settings > Services > Databases > Add New Service, where Redshift can be easily located among the extensive list of supported connectors. The process looks like this

Initial Connection Setup

Starting from Collate:

1. Navigate to Settings: Begin by accessing the services section in Collate's settings.

2. Add New Database Service: Select “Services”, then “Databases”, then "Add New Service" and search for Redshift in the service list.

3. Configure Connection Details:

• Enter a descriptive name for your database service
• Provide database credentials (username, password)
• Specify the host endpoint and port

• Ingest All Databases: If selected, all databases in the cluster will be ingested; otherwise, only tables from the named database will be ingested.

• RedshiftConnection Advanced Config: If expanded, options to utilize various SSL Modes or AWS S3 Storage Configurations become available.

Press the Test Connection button first to ensure you have connectivity. Note, however, that if the service isn’t running and the test connection is what wakes it up, the first try might fail if it doesn’t start fast enough. If that’s the case, wait a couple of minutes and try the test again; it should work. It is always best practice to test your connection. Once done, click Next.

Collate uses filters to control what data is ingested, databases, schemas, or tables, via names or regular expressions (regex). Out of the box, it excludes system schemas, such as "information_schema" or "performance_schema", to focus on user data.

Accept the defaults for a full ingest, or customize: for instance, include only schemas matching "^prod_.*" to target production data. Use the filtering options to control which databases, schemas, or tables are imported, thereby reducing unnecessary bloat.

The Power of Collate's Agent Architecture

Once the initial connection is established, Collate automatically launches its Metadata Agent that begins ingesting data immediately. But this is just the beginning. Collate has a comprehensive agent ecosystem, each designed to extract different types of value from your Redshift data.

Understanding Agents

The above screenshot illustrates how the Agents dashboard provides quick insights as to the status of each agent. Their run status, schedule, logs for additional details, which are especially useful if a failure has occurred, and additional options in the three-dot menu. Let’s review the various agents and their respective roles.

The Usage Agent provides insights into data popularity and query patterns. By analyzing how frequently tables and schemas are accessed, this agent informs Collate's auto-tiering feature, enabling organizations to understand which data assets are most critical to their operations.

The Lineage Agent connects to query logs and automatically curates data lineage relationships. This automated approach saves countless hours that would otherwise be spent manually documenting data flows and dependencies.

For teams focused on data quality, the Profiling Agent generates metrics including row counts, unique value percentages, and column-level profiling statistics. It also supports custom SQL queries for organization-specific metrics, making it adaptable to unique business requirements.

AI-Powered Intelligence with Collate AI

What sets Collate apart is its AI-powered agent suite, available as an optional add-on within Collate. These agents bring artificial intelligence directly to metadata management challenges.

The AI Documentation Agent addresses one of the most common pain points in data management: missing or inadequate documentation. This agent automatically suggests descriptions for tables and columns that lack documentation, allowing teams to accept, reject, or process these suggestions in bulk. Teams can even configure it to enforce descriptions, ensuring comprehensive documentation coverage.

The AI Quality Agent offers a smart starting point for data quality initiatives. By analyzing metadata patterns, it suggests appropriate data quality tests from Collate's extensive test library, giving organizations a solid foundation for their data quality programs without requiring extensive manual configuration.

Finally, there is the AI Tiering Agent, which automatically classifies data assets from Tier 1 to Tier 5 based on their importance. This classification considers multiple factors, including query frequency, lineage position, upstream and downstream dependencies, and the overall impact on the data ecosystem.

Best Practices for Implementation

For organizations with extensive Redshift implementations, leveraging the filtering capabilities during initial setup can help focus efforts on the most critical data assets. Regular expression support is convenient for organizations with consistent naming conventions.

Teams looking to accelerate their metadata management maturity should strongly consider the AI agent suite. The documentation and quality agents can significantly reduce the manual effort required to establish comprehensive data governance practices.

Conclusion

The real-time ingestion capability enables teams to see their metadata appear in Collate as the agent runs, providing immediate visibility into table structures, column definitions, and data types. Each table view includes comprehensive column information with data types and descriptions, creating a centralized catalog of data assets.

Redshift's position within the AWS ecosystem creates additional opportunities for metadata enrichment. The platform naturally integrates with services like AWS Glue and S3, enabling organizations to build comprehensive manual lineage maps when needed. This ecosystem approach means that Redshift connections in Collate can serve as anchor points for broader AWS data architecture documentation.

Connecting Collate to Amazon Redshift demonstrates how modern metadata management platforms can transform data warehouse operations from reactive to proactive. The combination of automated ingestion, intelligent agent capabilities, and AI-powered insights creates a foundation for effective data governance at scale.

Whether you're managing a single database or complex multi-database environments, Collate offers a scalable solution for understanding and governing your data infrastructure. Ready to get started? Sign up for the Collate Free Tier of our managed OpenMetadata Service, or visit the Product Sandbox to try out Collate with demo data.