Remove Data Models Remove Data Preparation Remove Definition
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Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

Flipboard

By combining the capabilities of LLM function calling and Pydantic data models, you can dynamically extract metadata from user queries. Knowledge base – You need a knowledge base created in Amazon Bedrock with ingested data and metadata. In this section, we discuss some ways you can address them.

AWS 160
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Introduction to Power BI Datamarts

ODSC - Open Data Science

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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How to: Focus on three areas for a holistic data governance approach for self-service analytics

Tableau

No single source of truth: There may be multiple versions or variations of similar data sets, but which is the trustworthy data set users should default to? Missing data definitions and formulas: People need to understand exactly what the data represents, in the context of the business, to use it effectively.

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What is a data fabric?

Tableau

Shine a light on who or what is using specific data to speed up collaboration or reduce disruption when changes happen. Data modeling. Leverage semantic layers and physical layers to give you more options for combining data using schemas to fit your analysis. Data preparation.

Tableau 101
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How to: Focus on three areas for a holistic data governance approach for self-service analytics

Tableau

No single source of truth: There may be multiple versions or variations of similar data sets, but which is the trustworthy data set users should default to? Missing data definitions and formulas: People need to understand exactly what the data represents, in the context of the business, to use it effectively.

article thumbnail

What is a data fabric?

Tableau

Shine a light on who or what is using specific data to speed up collaboration or reduce disruption when changes happen. Data modeling. Leverage semantic layers and physical layers to give you more options for combining data using schemas to fit your analysis. Data preparation.

Tableau 98
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Apply fine-grained data access controls with AWS Lake Formation in Amazon SageMaker Data Wrangler

AWS Machine Learning Blog

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. compute.internal.

AWS 98