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AI recommendations for descriptions in Amazon DataZone for enhanced business data cataloging and discovery is now generally available

AWS Big Data

Data consumers need detailed descriptions of the business context of a data asset and documentation about its recommended use cases to quickly identify the relevant data for their intended use case. Getting started with generative AI-powered data descriptions To get started, log in to the Amazon DataZone data portal.

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Data governance in the age of generative AI

AWS Big Data

First, many LLM use cases rely on enterprise knowledge that needs to be drawn from unstructured data such as documents, transcripts, and images, in addition to structured data from data warehouses. Data enrichment In addition, additional metadata may need to be extracted from the objects.

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Three Emerging Analytics Products Derived from Value-driven Data Innovation and Insights Discovery in the Enterprise

Rocket-Powered Data Science

The results showed that (among those surveyed) approximately 90% of enterprise analytics applications are being built on tabular data. The ease with which such structured data can be stored, understood, indexed, searched, accessed, and incorporated into business models could explain this high percentage.

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What is a data scientist? A key data analytics role and a lucrative career

CIO Business Intelligence

The data that data scientists analyze draws from many sources, including structured, unstructured, or semi-structured data. The more high-quality data available to data scientists, the more parameters they can include in a given model, and the more data they will have on hand for training their models.

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How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

Sources Data can be loaded from multiple sources, such as systems of record, data generated from applications, operational data stores, enterprise-wide reference data and metadata, data from vendors and partners, machine-generated data, social sources, and web sources.

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Advancing AI: The emergence of a modern information lifecycle

CIO Business Intelligence

Once focused solely on reducing search and retrieval times, information lifecycle management (ILM) is now critical to workflow automation, identifying and tracking performance metrics, and harnessing the burgeoning potential of AI. Operationalizing data to drive revenue CIOs report that their roles are rising in importance and impact.

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Non-JSON ingestion using Amazon Kinesis Data Streams, Amazon MSK, and Amazon Redshift Streaming Ingestion

AWS Big Data

JSON data in Amazon Redshift Amazon Redshift enables storage, processing, and analytics on JSON data through the SUPER data type, PartiQL language, materialized views, and data lake queries. The function JSON_PARSE allows you to extract the binary data in the stream and convert it into the SUPER data type.