Remove Data Lake Remove Data Quality Remove Machine Learning
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Data Quality Power Moves: Scorecards & Data Checks for Organizational Impact

DataKitchen

A DataOps Approach to Data Quality The Growing Complexity of Data Quality Data quality issues are widespread, affecting organizations across industries, from manufacturing to healthcare and financial services. 73% of data practitioners do not trust their data (IDC).

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The next generation of Amazon SageMaker: The center for all your data, analytics, and AI

AWS Big Data

Our customers are telling us that they are seeing their analytics and AI workloads increasingly converge around a lot of the same data, and this is changing how they are using analytics tools with their data. This innovation drives an important change: you’ll no longer have to copy or move data between data lake and data warehouses.

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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

As technology and business leaders, your strategic initiatives, from AI-powered decision-making to predictive insights and personalized experiences, are all fueled by data. Yet, despite growing investments in advanced analytics and AI, organizations continue to grapple with a persistent and often underestimated challenge: poor data quality.

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

The following requirements were essential to decide for adopting a modern data mesh architecture: Domain-oriented ownership and data-as-a-product : EUROGATE aims to: Enable scalable and straightforward data sharing across organizational boundaries. Eliminate centralized bottlenecks and complex data pipelines.

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Perform data parity at scale for data modernization programs using AWS Glue Data Quality

AWS Big Data

Today, customers are embarking on data modernization programs by migrating on-premises data warehouses and data lakes to the AWS Cloud to take advantage of the scale and advanced analytical capabilities of the cloud. Some customers build custom in-house data parity frameworks to validate data during migration.

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Simplify data integration with AWS Glue and zero-ETL to Amazon SageMaker Lakehouse

AWS Big Data

With this new functionality, customers can create up-to-date replicas of their data from applications such as Salesforce, ServiceNow, and Zendesk in an Amazon SageMaker Lakehouse and Amazon Redshift. SageMaker Lakehouse gives you the flexibility to access and query your data in-place with all Apache Iceberg compatible tools and engines.

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Data Lakes on Cloud & it’s Usage in Healthcare

BizAcuity

Data lakes are centralized repositories that can store all structured and unstructured data at any desired scale. The power of the data lake lies in the fact that it often is a cost-effective way to store data. The power of the data lake lies in the fact that it often is a cost-effective way to store data.

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