Remove Data Processing Remove Metadata Remove Publishing
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Implement a custom subscription workflow for unmanaged Amazon S3 assets published with Amazon DataZone

AWS Big Data

We want to publish this data to Amazon DataZone as discoverable S3 data. Custom subscription workflow architecture diagram To implement the solution, we complete the following steps: As a data producer, publish an unstructured S3 based data asset as S3ObjectCollectionType to Amazon DataZone.

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

To achieve this, EUROGATE designed an architecture that uses Amazon DataZone to publish specific digital twin data sets, enabling access to them with SageMaker in a separate AWS account. From here, the metadata is published to Amazon DataZone by using AWS Glue Data Catalog. This process is shown in the following figure.

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Demystify data sharing and collaboration patterns on AWS: Choosing the right tool for the job

AWS Big Data

Next, we focus on building the enterprise data platform where the accumulated data will be hosted. Business analysts enhance the data with business metadata/glossaries and publish the same as data assets or data products. The enterprise data platform is used to host and analyze the sales data and identify the customer demand.

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How ANZ Institutional Division built a federated data platform to enable their domain teams to build data products to support business outcomes

AWS Big Data

For instance, Domain A will have the flexibility to create data products that can be published to the divisional catalog, while also maintaining the autonomy to develop data products that are exclusively accessible to teams within the domain. A data portal for consumers to discover data products and access associated metadata.

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Integrate custom applications with AWS Lake Formation – Part 2

AWS Big Data

Solution overview AWS AppSync creates serverless GraphQL and pub/sub APIs that simplify application development through a single endpoint to securely query, update, or publish data. For simplicity, we use the Hosting with Amplify Console and Manual Deployment options. All the resources are now deployed on AWS and ready for use.

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Reduce your compute costs for stream processing applications with Kinesis Client Library 3.0

AWS Big Data

Kinesis Data Streams not only offers the flexibility to use many out-of-box integrations to process the data published to the streams, but also provides the capability to build custom stream processing applications that can be deployed on your compute fleet. KCL uses DynamoDB to store metadata such as shard-worker mapping and checkpoints.

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How BMW streamlined data access using AWS Lake Formation fine-grained access control

AWS Big Data

The CDH is used to create, discover, and consume data products through a central metadata catalog, while enforcing permission policies and tightly integrating data engineering, analytics, and machine learning services to streamline the user journey from data to insight.