Remove 2012 Remove Data Governance Remove Metadata
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Enrich your AWS Glue Data Catalog with generative AI metadata using Amazon Bedrock

AWS Big Data

Metadata can play a very important role in using data assets to make data driven decisions. Generating metadata for your data assets is often a time-consuming and manual task. First, we explore the option of in-context learning, where the LLM generates the requested metadata without documentation.

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Design a data mesh pattern for Amazon EMR-based data lakes using AWS Lake Formation with Hive metastore federation

AWS Big Data

In this post, we delve into the key aspects of using Amazon EMR for modern data management, covering topics such as data governance, data mesh deployment, and streamlined data discovery. Organizations have multiple Hive data warehouses across EMR clusters, where the metadata gets generated.

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10 Years Later: Who’s the GOAT of Data Catalogs?

Alation

December 2012: Alation forms and goes to work creating the first enterprise data catalog. Later, in its inaugural report on data catalogs, Forrester Research recognizes that “Alation started the MLDC trend.”. August 2017: Alation debuts as a leader in the Gartner MQ for Metadata Management Solutions.

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How Volkswagen streamlined access to data across multiple data lakes using Amazon DataZone – Part 1

AWS Big Data

The current method is largely manual, relying on emails and general communication, which not only increases overhead but also varies from one use case to another in terms of data governance. Data domain producers publish data assets using datasource run to Amazon DataZone in the Central Governance account.

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How Novo Nordisk built distributed data governance and control at scale

AWS Big Data

The first post of this series describes the overall architecture and how Novo Nordisk built a decentralized data mesh architecture, including Amazon Athena as the data query engine. The third post will show how end-users can consume data from their tool of choice, without compromising data governance.

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Orchestrate an end-to-end ETL pipeline using Amazon S3, AWS Glue, and Amazon Redshift Serverless with Amazon MWAA

AWS Big Data

This approach allows the team to process the raw data extracted from Account A to Account B, which is dedicated for data handling tasks. This makes sure the raw and processed data can be maintained securely separated across multiple accounts, if required, for enhanced data governance and security.

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Seamless integration of data lake and data warehouse using Amazon Redshift Spectrum and Amazon DataZone

AWS Big Data

This streamlined architecture approach offers several advantages: Single source of truth – The Central IT team acts as the custodian of the combined and curated data from all business units, thereby providing a unified and consistent dataset. Similarly, individual business units produce their own domain-specific data.

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