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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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6 Case Studies on The Benefits of Business Intelligence And Analytics

datapine

Using business intelligence and analytics effectively is the crucial difference between companies that succeed and companies that fail in the modern environment. Experience the power of Business Intelligence with our 14-days free trial! Why Is Business Intelligence So Important? The power of knowledge.

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How Remote Data Teams Are Winning in Times of COVID-19

Octopai

Businesses around the globe have been forced to reassess the way they conduct their operations, even if their methods have been in place for decades. Some business intelligence professionals are not only contributing to their organization’s survival during these difficult times; they are actually helping it thrive.

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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

We explore why Orca chose to build a transactional data lake and examine the key considerations that guided the selection of Apache Iceberg as the preferred table format. Lastly, we discuss the challenges encountered throughout the project, present the solutions used to address them, and share valuable lessons learned.

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Top 10 Data Lineage Podcasts, Blogs, and Magazines

Octopai

The particular episode we recommend looks at how WeWork struggled with understanding their data lineage so they created a metadata repository to increase visibility. Agile Data. Another podcast we think is worth a listen is Agile Data. Currently, he is in charge of the Technical Operations team at MIT Open Learning.

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A Stitch in Time: How Jet Analytics Boosts Microsoft Fabric Time-to-Value

Jet Global

The solution offers data movement, data science, real-time analytics, and business intelligence within a single platform. This allows you to fully utilize your Fabric-based systems and overcome typical obstacles related to complex data environments.

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Power enterprise-grade Data Vaults with Amazon Redshift – Part 2

AWS Big Data

In the following sections, we discuss the most common areas of consideration that are critical for Data Vault implementations at scale: data protection, performance and elasticity, analytical functionality, cost and resource management, availability, and scalability. Automatic WLM manages the resources required to run queries.