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Architectural Patterns for real-time analytics using Amazon Kinesis Data Streams, Part 2: AI Applications

AWS Big Data

Now get ready as we embark on the second part of this series, where we focus on the AI applications with Kinesis Data Streams in three scenarios: real-time generative business intelligence (BI), real-time recommendation systems, and Internet of Things (IoT) data streaming and inferencing.

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It’s not your data. It’s how you use it. Unlock the power of data & build foundations of a data driven organisation

CIO Business Intelligence

The survey found the mean number of data sources per organisation to be 400, and more than 20 percent of companies surveyed to be drawing from 1,000 or more data sources to feed business intelligence and analytics systems. zettabytes of data. FOUNDATIONS OF A MODERN DATA DRIVEN ORGANISATION.

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The Enterprise AI Revolution Starts with BI

Jet Global

And while AI algorithms are certainly poised to make an impact in each of these areas, enterprise businesses need to first invest in building the infrastructure to support them. The road to AI supremacy in enterprise business starts with investment in an area most businesses might not think to look at first.

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Better, faster decisions: Why businesses thrive on real-time data

CIO Business Intelligence

To access data in real time — and ensure that it provides actionable insights for all stakeholders — organizations should invest in the foundational components that enable more efficient, scalable, and secure data collection, processing, and analysis. Business Intelligence

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Creating Data Value With a Decentralized Data Strategy

CIO Business Intelligence

For decades organizations chased the Holy Grail of a centralized data warehouse/lake strategy to support business intelligence and advanced analytics. billion connected Internet of Things (IoT) devices by 2025, generating almost 80 billion zettabytes of data at the edge.

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How the Edge Is Changing Data-First Modernization

CIO Business Intelligence

The concept of the edge is not new, but its role in driving data-first business is just now emerging. The advent of distributed workforces, smart devices, and internet-of-things (IoT) applications is creating a deluge of data generated and consumed outside of traditional centralized data warehouses.

IoT 87
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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.