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Integrate Amazon Bedrock with Amazon Redshift ML for generative AI applications

AWS Big Data

The steps to build and run the solution are the following: Load sample patients’ data Prepare the prompt Enable LLM access Create a model that references the LLM model on Amazon Bedrock Send the prompt and generate a personalized patient diet plan Pre-requisites An AWS account. See Amazon Bedrock model IDs for how to find the model ID.

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Combine transactional, streaming, and third-party data on Amazon Redshift for financial services

AWS Big Data

This stack creates the following resources and necessary permissions to integrate the services: Data stream – With Amazon Kinesis Data Streams , you can send data from your streaming source to a data stream to ingest the data into a Redshift data warehouse. version cluster. version cluster.

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Configure monitoring, limits, and alarms in Amazon Redshift Serverless to keep costs predictable

AWS Big Data

It automatically provisions and intelligently scales data warehouse compute capacity to deliver fast performance, and you pay only for what you use. Just load your data and start querying right away in the Amazon Redshift Query Editor or in your favorite business intelligence (BI) tool. Open the workgroup you want to monitor.

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Optimize your workloads with Amazon Redshift Serverless AI-driven scaling and optimization

AWS Big Data

The current scaling approach of Amazon Redshift Serverless increases your compute capacity based on the query queue time and scales down when the queuing reduces on the data warehouse. This post also includes example SQLs, which you can run on your own Redshift Serverless data warehouse to experience the benefits of this feature.

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Interview with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity

Corinium

Ahead of the Chief Data Analytics Officers & Influencers, Insurance event we caught up with Dominic Sartorio, Senior Vice President for Products & Development, Protegrity to discuss how the industry is evolving. Are you seeing any specific issues around the insurance industry at the moment that should concern CDAOs?

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AI agents will transform business processes — and magnify risks

CIO Business Intelligence

Our goal is to analyze logs and metrics, connecting them with the source code to gain insights into code fixes, vulnerabilities, performance issues, and security concerns,” he says. Insurance company Aflac is one company making sure this is the case to maintain human oversight over the AI, instead of letting it act completely autonomously.

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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.