# Amazon SageMaker and when to use Amazon SageMaker vs Amazon DataZone
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[Amazon SageMaker Catalog](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/working-with-business-catalog.html), built on Amazon DataZone, allows users to centrally manage their data assets. You can catalog your data assets, search and discover the data, use built-in generative AI capabilities to create metadata, or you could just ask Amazon Q Developer natural language questions to find your data. Users can consistently define and enforce access policies using a single permission model with [fine-grained access controls](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/fine-grained-access-control.html) centrally in the Amazon SageMaker Unified Studio. You can create business glossary, extend your metadata and build [data products](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/data-products.html) that can be shared with large teams with fine grained access control. You can also view [data quality scores](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/data-quality.html) and discover [data lineage](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/datazone-data-lineage.html) of your data assets.

You can access Amazon SageMaker Catalog from [Amazon SageMaker Unified Studio](https://docs.aws.amazon.com/sagemaker-unified-studio/latest/userguide/what-is-sagemaker-unified-studio.html). Unified Studio is a development experience within Amazon SageMaker that brings together AWS data, analytics, artificial intelligence (AI), and machine learning (ML) services. It provides a place to build, deploy, execute, and monitor workflows from a single interface. This helps drive collaboration across teams and facilitate agile development.