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Data governance in the age of generative AI

AWS Big Data

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. Data governance is a critical building block across all these approaches, and we see two emerging areas of focus.

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What you don’t know about data management could kill your business

CIO Business Intelligence

To avoid the inevitable, CIOs must get serious about data management. Data, of course, has been all the rage the past decade, having been declared the “new oil” of the digital economy. Still, to truly create lasting value with data, organizations must develop data management mastery.

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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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Doing Cloud Migration and Data Governance Right the First Time

erwin

So if you’re going to move from your data from on-premise legacy data stores and warehouse systems to the cloud, you should do it right the first time. That means your cloud data assets must be available for use by the right people for the right purposes to maximize their security, quality and value.

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Don’t Fear Artificial Intelligence; Embrace it Through Data Governance

CIO Business Intelligence

This would be straightforward task were it not for the fact that, during the digital-era, there has been an explosion of data – collected and stored everywhere – much of it poorly governed, ill-understood, and irrelevant. Further, data management activities don’t end once the AI model has been developed.

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5 Ways Data Engineers Can Support Data Governance

Alation

Data is a key asset for businesses in the modern world. Used correctly, it can improve internal operations, power marketing strategies, and much more. That’s why many organizations invest in technology to improve data processes, such as a machine learning data pipeline. Let’s look at five different ways.

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Build a transactional data lake using Apache Iceberg, AWS Glue, and cross-account data shares using AWS Lake Formation and Amazon Athena

AWS Big Data

Building a data lake on Amazon Simple Storage Service (Amazon S3) provides numerous benefits for an organization. However, many use cases, like performing change data capture (CDC) from an upstream relational database to an Amazon S3-based data lake, require handling data at a record level.