ODSC - Open Data Science

JUNE 12, 2023

This article is an excerpt from the book Expert Data Modeling with Power BI, Third Edition by Soheil Bakhshi, a completely updated and revised edition of the bestselling guide to Power BI and data modeling. No-code/low-code experience using a diagram view in the data preparation layer similar to Dataflows.

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IBM Journey to AI blog

MARCH 29, 2024

Enterprise applications serve as repositories for extensive data models, encompassing historical and operational data in diverse databases. Generative AI foundational models train on massive amounts of unstructured and structured data, but the orchestration is critical to success.

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Pickl AI

JUNE 23, 2023

Data Collection The process begins with the collection of relevant and diverse data from various sources. This can include structured data (e.g., databases, spreadsheets) as well as unstructured data (e.g., Data Preparation Once collected, the data needs to be preprocessed and prepared for analysis.

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Tableau

SEPTEMBER 23, 2021

If we asked you, “What does your organization need to help more employees be data-driven?” where would “better data governance” land on your list? We’re all trying to use more data to make decisions, but constantly face roadblocks and trust issues related to data governance. . A data governance framework.

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Tableau

SEPTEMBER 23, 2021

If we asked you, “What does your organization need to help more employees be data-driven?” where would “better data governance” land on your list? We’re all trying to use more data to make decisions, but constantly face roadblocks and trust issues related to data governance. . A data governance framework.

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AWS Machine Learning Blog

AUGUST 21, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. We are happy to announce that SageMaker Data Wrangler now supports using Lake Formation with Amazon EMR to provide this fine-grained data access restriction.

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Pickl AI

JUNE 18, 2024

It now allows users to clean, transform, and integrate data from various sources, streamlining the Data Analysis process. This eliminates the need to rely on separate tools for data preparation, saving time and resources. Data governance and compliance are critical aspects of Data Analysis.

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