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

AWS Big Data

Data lakes are more focused around storing and maintaining all the data in an organization in one place. And unlike data warehouses, which are primarily analytical stores, a data hub is a combination of all types of repositories—analytical, transactional, operational, reference, and data I/O services, along with governance processes.

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How to get powerful and actionable insights from any and all of your data, without delay

Cloudera

By enabling their event analysts to monitor and analyze events in real time, as well as directly in their data visualization tool, and also rate and give feedback to the system interactively, they increased their data to insight productivity by a factor of 10. . Our solution: Cloudera Data Visualization.

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Exploring real-time streaming for generative AI Applications

AWS Big Data

Streaming jobs constantly ingest new data to synchronize across systems and can perform enrichment, transformations, joins, and aggregations across windows of time more efficiently. AWS Glue can interact with streaming data services such as Kinesis Data Streams and Amazon MSK for processing and transforming CDC data.

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Top Graph Use Cases and Enterprise Applications (with Real World Examples)

Ontotext

Specifically, the increasing amount of data being generated and collected, and the need to make sense of it, and its use in artificial intelligence and machine learning, which can benefit from the structured data and context provided by knowledge graphs. We get this question regularly. million users.

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Data platform trinity: Competitive or complementary?

IBM Big Data Hub

In another decade, the internet and mobile started the generate data of unforeseen volume, variety and velocity. It required a different data platform solution. Hence, Data Lake emerged, which handles unstructured and structured data with huge volume. Data lakehouse was created to solve these problems.

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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

Customer 360 (C360) provides a complete and unified view of a customer’s interactions and behavior across all touchpoints and channels. This view is used to identify patterns and trends in customer behavior, which can inform data-driven decisions to improve business outcomes. Then, you transform this data into a concise format.