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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes.

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How EUROGATE established a data mesh architecture using Amazon DataZone

AWS Big Data

For container terminal operators, data-driven decision-making and efficient data sharing are vital to optimizing operations and boosting supply chain efficiency. Together, these capabilities enable terminal operators to enhance efficiency and competitiveness in an industry that is increasingly data driven.

IoT 110
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Introducing a new unified data connection experience with Amazon SageMaker Lakehouse unified data connectivity

AWS Big Data

The need to integrate diverse data sources has grown exponentially, but there are several common challenges when integrating and analyzing data from multiple sources, services, and applications. First, you need to create and maintain independent connections to the same data source for different services.

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Build event-driven data pipelines using AWS Controllers for Kubernetes and Amazon EMR on EKS

AWS Big Data

An event-driven architecture is a software design pattern in which decoupled applications can asynchronously publish and subscribe to events via an event broker. Amazon Elastic Kubernetes Service (Amazon EKS) is becoming a popular choice among AWS customers to host long-running analytics and AI or machine learning (ML) workloads.

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Manage Amazon OpenSearch Service Visualizations, Alerts, and More with GitHub and Jenkins

AWS Big Data

It allows organizations to secure data, perform searches, analyze logs, monitor applications in real time, and explore interactive log analytics. With its scalability, reliability, and ease of use, Amazon OpenSearch Service helps businesses optimize data-driven decisions and improve operational efficiency.

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What you need to know about product management for AI

O'Reilly on Data

But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools. AI products are automated systems that collect and learn from data to make user-facing decisions. Why AI software development is different.

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Implement a custom subscription workflow for unmanaged Amazon S3 assets published with Amazon DataZone

AWS Big Data

Organizational data is often fragmented across multiple lines of business, leading to inconsistent and sometimes duplicate datasets. This fragmentation can delay decision-making and erode trust in available data. This solution enhances governance and simplifies access to unstructured data assets across the organization.