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Logical Data Management and Data Mesh

Data Virtualization

But there’s a lot of confusion in the marketplace today between different types of architectures, specifically data mesh and data fabric, so I’ll. The post Logical Data Management and Data Mesh appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.

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The Modern Data Stack Explained: What The Future Holds

Alation

What if, experts asked, you could load raw data into a warehouse, and then empower people to transform it for their own unique needs? Today, data integration platforms like Rivery do just that. By pushing the T to the last step in the process, such products have revolutionized how data is understood and analyzed.

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Choosing A Graph Data Model to Best Serve Your Use Case

Ontotext

Poor data modeling capabilities of LPGs with vendor specific constructs to express semantic constraints hinders portability, expressibility, and semantic data integration. It accelerates data projects with data quality and lineage and contextualizes through ontologies , taxonomies, and vocabularies, making integrations easier.

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Adding AI to Products: A High-Level Guide for Product Managers

Sisense

As an AI product manager, here are some important data-related questions you should ask yourself: What is the problem you’re trying to solve? What data transformations are needed from your data scientists to prepare the data? What are the right KPIs and outputs for your product? What will it take to build your MVP?

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DataOps Observability: Taming the Chaos (Part 2)

DataKitchen

It’s because it’s a hard thing to accomplish when there are so many teams, locales, data sources, pipelines, dependencies, data transformations, models, visualizations, tests, internal customers, and external customers. You can’t quality-control your data integrations or reports with only some details. .

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Self-Serve Data Prep CAN Be Easy AND Sophisticated!

Smarten

If your team has easy-to-use tools and features, you are much more likely to experience the user adoption you want and to improve data literacy and data democratization across the organization. Machine learning capability determines the best techniques, and the best fit transformations for data so that the outcome is clear and concise.