MLREL02-BP01 Use a data catalog - Machine Learning Lens
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MLREL02-BP01 Use a data catalog

Process data across multiple data stores using data catalog technology. An advanced data catalog service can enable ETL process integration. This approach improves reliability and efficiency.

Desired outcome: You establish a centralized system that inventories your ML data assets across the organization. You can track, discover, and manage data transformations, and your stakeholders can find and use appropriate datasets. With a complete data catalog in place, you gain better data governance, reduce duplication of effort, increase data quality, and accelerate ML model development through streamlined data preparation workflows.

Common anti-patterns:

  • Maintaining separate, isolated data silos without centralized metadata.

  • Manual tracking of data assets using spreadsheets or wiki pages.

  • Failing to document data transformations and lineage.

  • Rebuilding ETL processes for each new ML project.

  • Relying on tribal knowledge for understanding data characteristics.

Benefits of establishing this best practice:

  • Provides centralized visibility of available data assets.

  • Improves data governance.

  • Reduces time spent searching for and understanding data.

  • Enhances data quality through consistent transformation processes.

  • Strengthens collaboration between data engineers and data scientists.

  • Accelerates ML model development with faster data preparation.

Level of risk exposed if this best practice is not established: Medium

Implementation guidance

A data catalog is critical infrastructure for machine learning workloads to succeed. Without proper cataloging, data scientists spend excessive time searching for, understanding, and preparing data rather than analyzing it or building models. A comprehensive data catalog provides a centralized inventory of your data assets, complete with metadata, transformation history, and usage information.

When implementing a data catalog for ML workloads, focus on creating a single source of truth for data discovery. The catalog should document where data resides, what transformations have been applied, and how it can be accessed. This reduces the time spent on data preparation, which typically consumes 60-80% of a data scientist's time.

For AWS environments, the AWS AWS Glue Data Catalog offers a powerful solution as it integrates seamlessly with other AWS analytics and machine learning services. By implementing a data catalog as part of your ML infrastructure, you create a foundation for consistent, reliable data processing that supports both current and future ML initiatives.

Implementation steps

  1. Set up AWS AWS Glue Data Catalog. Establish the AWSAWS Glue Data Catalog as your central metadata repository. This fully managed service provides a unified view of your data across multiple data stores, making it accessible to various AWS services like Amazon SageMaker AI, Amazon EMR, Amazon Athena, and Amazon Redshift.

  2. Define your metadata strategy. Determine what metadata to capture about each dataset, including business definitions, data lineage, quality metrics, and usage patterns. Well-documented metadata assists data scientists in quickly understanding if a dataset is appropriate for their modeling needs.

  3. Populate the data catalog. Use AWS Glue crawlers to automatically discover schemas, data types, and statistics from your data sources. Crawlers can scan various data stores including Amazon S3, Amazon RDS, and other database systems. Systematically add your data sources to build comprehensive coverage.

  4. Implement ETL processes with AWS Glue. Create ETL jobs that transform raw data into formats optimized for machine learning. AWS Glue can automatically generate Python or Scala code for these transformations, reducing development time and improving consistency across different data processing pipelines.

  5. Establish data lineage tracking. Configure your data catalog to track transformations and maintain clear lineage information. With data lineage tracking, data scientists can understand data provenance, which builds trust in the datasets used for model training.

  6. Integrate with ML workflows. Connect your AWS AWS Glue Data Catalog with Amazon SageMaker AI to streamline data access for model training. For enterprise environments, implement Amazon SageMaker AI Catalog as a central metadata hub for ML and data assets, enabling secure sharing and governed access via Amazon DataZone integration. This integration allows data scientists to discover and use properly prepared datasets directly from their modeling environments.

  7. Implement access controls and governance. Configure appropriate permissions for your data catalog using AWS IAM roles. Verify that data scientists have access to the metadata and datasets they need while maintaining security and regulatory adherence.

  8. Automate catalog maintenance. Set up automated processes to keep your data catalog current as new data arrives and transformations occur. Regular updates verify that data scientists have access to the latest information about available datasets.

  9. Monitor and measure impact. Track key metrics like time saved in data discovery, reduction in redundant data preparation work, and improvements in model development cycles to quantify the benefits of your data catalog implementation.

Resources

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