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

AWS Big Data

While traditional extract, transform, and load (ETL) processes have long been a staple of data integration due to its flexibility, for common use cases such as replication and ingestion, they often prove time-consuming, complex, and less adaptable to the fast-changing demands of modern data architectures.

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Beyond Data Fabrics: Cloudera Modern Data Architectures

Cloudera

What used to be bespoke and complex enterprise data integration has evolved into a modern data architecture that orchestrates all the disparate data sources intelligently and securely, even in a self-service manner: a data fabric. Cloudera data fabric and analyst acclaim. Next steps.

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Laying the Foundation for Modern Data Architecture

Cloudera

It’s not enough for businesses to implement and maintain a data architecture. The unpredictability of market shifts and the evolving use of new technologies means businesses need more data they can trust than ever to stay agile and make the right decisions.

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

CIO Business Intelligence

We also examine how centralized, hybrid and decentralized data architectures support scalable, trustworthy ecosystems. As data-centric AI, automated metadata management and privacy-aware data sharing mature, the opportunity to embed data quality into the enterprises core has never been more significant.

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

Need for a data mesh architecture Because entities in the EUROGATE group generate vast amounts of data from various sourcesacross departments, locations, and technologiesthe traditional centralized data architecture struggles to keep up with the demands for real-time insights, agility, and scalability.

IoT 101
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The Race For Data Quality in a Medallion Architecture

DataKitchen

This architecture is valuable for organizations dealing with large volumes of diverse data sources, where maintaining accuracy and accessibility at every stage is a priority. It sounds great, but how do you prove the data is correct at each layer? How do you ensure data quality in every layer ?

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Data integrity vs. data quality: Is there a difference?

IBM Big Data Hub

When we talk about data integrity, we’re referring to the overarching completeness, accuracy, consistency, accessibility, and security of an organization’s data. Together, these factors determine the reliability of the organization’s data. In short, yes.