Remove Books Remove Data Quality Remove Data Warehouse
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Go vs. Python for Modern Data Workflows: Need Help Deciding?

KDnuggets

This readability becomes valuable when collaborating with domain experts who need to understand and validate your data transformations. Real-world data projects often involve integrating multiple data sources, handling different formats, and dealing with inconsistent data quality.

Python 197
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Alation 2022.2: Open Data Quality Initiative and Enhanced Data Governance

Alation

generally available on May 24, Alation introduces the Open Data Quality Initiative for the modern data stack, giving customers the freedom to choose the data quality vendor that’s best for them with the added confidence that those tools will integrate seamlessly with Alation’s Data Catalog and Data Governance application.

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Becoming a Prized Data Warehouse and Data Integration Tester

Dataversity

Data warehouse (DW) testers with data integration QA skills are in demand. Data warehouse disciplines and architectures are well established and often discussed in the press, books, and conferences. Each business often uses one or more data […]. Click to learn more about author Wayne Yaddow.

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10 Best Data Engineering Books [Beginners to Advanced]

Pickl AI

Aspiring and experienced Data Engineers alike can benefit from a curated list of books covering essential concepts and practical techniques. These 10 Best Data Engineering Books for beginners encompass a range of topics, from foundational principles to advanced data processing methods. What is Data Engineering?

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Five benefits of a data catalog

IBM Journey to AI blog

You have a specific book in mind, but you have no idea where to find it. You enter the title of the book into the computer and the library’s digital inventory system tells you the exact section and aisle where the book is located. It uses metadata and data management tools to organize all data assets within your organization.

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Architect a mature generative AI foundation on AWS

Flipboard

For the preceding techniques, the foundation should provide scalable infrastructure for data storage and training, a mechanism to orchestrate tuning and training pipelines, a model registry to centrally register and govern the model, and infrastructure to host the model. She has presented her work at various learning conferences.

AWS 130
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Scale knowledge management use cases with generative AI

IBM Journey to AI blog

Data quality strongly impacts the quality and usefulness of content produced by an AI model, underscoring the significance of addressing data challenges. It provides the combination of data lake flexibility and data warehouse performance to help to scale AI.

AI 69