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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. As part of the transformation, the objects need to be treated to ensure data privacy (for example, PII redaction).

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Exploring real-time streaming for generative AI Applications

AWS Big Data

A RAG-based generative AI application can only produce generic responses based on its training data and the relevant documents in the knowledge base. Streaming jobs constantly ingest new data to synchronize across systems and can perform enrichment, transformations, joins, and aggregations across windows of time more efficiently.

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Migrate Hive data from CDH to CDP public cloud

Cloudera

Using easy-to-define policies, Replication Manager solves one of the biggest barriers for the customers in their cloud adoption journey by allowing them to move both tables/structured data and files/unstructured data to the CDP cloud of their choice easily. The Replication Manager support matrix is documented in our public docs.

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Advancing AI: The emergence of a modern information lifecycle

CIO Business Intelligence

A modern information lifecycle management approach Today’s ILM approach recognizes the enterprise value of all digitized and enriched assets , avoiding the habituated, narrow reliance ontraditional structured data. Beyond “records,” organizations can digitally capture anything and apply metadata for context and searchability.

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

AWS Big Data

A data hub contains data at multiple levels of granularity and is often not integrated. It differs from a data lake by offering data that is pre-validated and standardized, allowing for simpler consumption by users. Data hubs and data lakes can coexist in an organization, complementing each other.

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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.

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Five actionable steps to GDPR compliance (Right to be forgotten) with Amazon Redshift

AWS Big Data

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It is designed for analyzing large volumes of data and performing complex queries on structured and semi-structured data. Data mapping involves identifying and documenting the flow of personal data in an organization.