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The Race For Data Quality in a Medallion Architecture

DataKitchen

The Race For Data Quality In A Medallion Architecture The Medallion architecture pattern is gaining traction among data teams. It is a layered approach to managing and transforming data. 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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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.

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Unlocking Data Team Success: Are You Process-Centric or Data-Centric?

DataKitchen

We’ve identified two distinct types of data teams: process-centric and data-centric. Understanding this framework offers valuable insights into team efficiency, operational excellence, and data quality. Process-centric data teams focus their energies predominantly on orchestrating and automating workflows.

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12 Cloud Computing Risks & Challenges Businesses Are Facing In These Days

datapine

Like many other branches of technology, security is a pressing concern in the world of cloud-based computing, as you are unable to see the exact location where your data is stored or being processed. This increases the risks that can arise during the implementation or management process. Cost management and containment. Compliance.

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Top 10 Analytics And Business Intelligence Trends For 2020

datapine

Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2020 will be the year of data quality management and data discovery: clean and secure data combined with a simple and powerful presentation. 1) Data Quality Management (DQM).

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

AWS Big Data

Data teams struggle to find a unified approach that enables effortless discovery, understanding, and assurance of data quality and security across various sources. SageMaker simplifies the discovery, governance, and collaboration for data and AI across your lakehouse, AI models, and applications.

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An AI Chat Bot Wrote This Blog Post …

DataKitchen

In addition to speeding up the development and deployment of data-driven solutions, DataOps automation also helps organizations to improve the quality and reliability of their data-related workflows. Query> An AI, Chat GPT wrote this blog post, why should I read it? . By using DataOps, organizations can improve.