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Video Highlights: Change Data Capture With Apache Flink

The featured video resource provided by Decodable is a webinar in which CDC experts provide an overview of CDC with Flink and Debezium. There is a growing role Change Data Capture (CDC) plays in real-time data analytics (specifically, stream processing with open source tools like Debezium and Apache Flink). CDC lets users analyze data as it’s generated by leveraging streaming from systems like Apache Kafka, Amazon Kinesis, and Azure Events Hubs to track and transport changes from one data system to another.

Interview: Ashok Reddy, Chief Executive Officer, KX

I recently caught up with Ashok Reddy, CEO of KX to discuss how his company’s work in the area of real-time analytics. more specifically, using AI and machine learning to provide insights for making better decisions quickly is one of the most exciting spaces in technology today. Increasingly, machine data from sensors and IoT, and AI and ML are influencing this strategic shift we’re seeing toward real-time forecasts and recommendations.

Verta Insights Study Reveals that Fewer than Half of Companies Are Ready to Scale Real-time AI Within Three Years

Verta Inc., a leading provider of enterprise model management and operational artificial intelligence (AI) solutions, released findings from the 2022 State of Machine Learning Operations study, which surveyed more than 200 machine learning (ML) practitioners about their use of AI and ML models to drive business success. The study was conducted by Verta Insights, the research practice of Verta Inc., and found that although companies across industries are poised to significantly increase their use of real-time AI within the next three years, fewer than half have actually adopted the tools needed to manage the anticipated expansion.

Real-time, Real Value: 80% of Businesses See Revenue Increases Thanks to Real-time Data

KX and the Centre for Business & Economics (CEBR) have published ‘The Speed to Business Value’ an industry report showing the commercial and operational benefits to be gained by businesses adopting real-time data analytics technologies.

Analyze-then-Store: The Journey to Continuous Intelligence – Part 6

This multi-part article series by our friends at Swim is intended for data architects and anyone else interested in learning how to design modern real-time data analytics solutions. It explores key principles and implications of event streaming and streaming analytics, and concludes that the biggest opportunity to derive meaningful value from data – and gain continuous intelligence about the state of things – lies in the ability to analyze, learn and predict from real-time events in concert with contextual, static and dynamic data. This article series places continuous intelligence in an architectural context, with reference to established technologies and use cases in place today.

Analyze-then-Store: The Journey to Continuous Intelligence – Part 5

This multi-part article series by our friends at Swim is intended for data architects and anyone else interested in learning how to design modern real-time data analytics solutions. It explores key principles and implications of event streaming and streaming analytics, and concludes that the biggest opportunity to derive meaningful value from data – and gain continuous intelligence about the state of things – lies in the ability to analyze, learn and predict from real-time events in concert with contextual, static and dynamic data. This article series places continuous intelligence in an architectural context, with reference to established technologies and use cases in place today.

Analyze-then-Store: The Journey to Continuous Intelligence – Part 4

This multi-part article series by our friends at Swim is intended for data architects and anyone else interested in learning how to design modern real-time data analytics solutions. It explores key principles and implications of event streaming and streaming analytics, and concludes that the biggest opportunity to derive meaningful value from data – and gain continuous intelligence about the state of things – lies in the ability to analyze, learn and predict from real-time events in concert with contextual, static and dynamic data. This article series places continuous intelligence in an architectural context, with reference to established technologies and use cases in place today.

Analyze-then-Store: The Journey to Continuous Intelligence – Part 3

This multi-part article series by our friends at Swim is intended for data architects and anyone else interested in learning how to design modern real-time data analytics solutions. It explores key principles and implications of event streaming and streaming analytics, and concludes that the biggest opportunity to derive meaningful value from data – and gain continuous intelligence about the state of things – lies in the ability to analyze, learn and predict from real-time events in concert with contextual, static and dynamic data. This article series places continuous intelligence in an architectural context, with reference to established technologies and use cases in place today.

Analyze-then-Store: The Journey to Continuous Intelligence – Part 2

This multi-part article series by our friends at Swim is intended for data architects and anyone else interested in learning how to design modern real-time data analytics solutions. It explores key principles and implications of event streaming and streaming analytics, and concludes that the biggest opportunity to derive meaningful value from data – and gain continuous intelligence about the state of things – lies in the ability to analyze, learn and predict from real-time events in concert with contextual, static and dynamic data. This article series places continuous intelligence in an architectural context, with reference to established technologies and use cases in place today.

Real-Time Analytics from Your Data Lake Teaching the Elephant to Dance

This whitepaper from Imply Data Inc. explains why delivering real-time analytics on a data lake is so hard, approaches companies have taken to accelerate their data lakes, and how they leveraged the same technology to create end-to-end real-time analytics architectures.