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

AWS Big Data

It covers how to use a conceptual, logical architecture for some of the most popular gaming industry use cases like event analysis, in-game purchase recommendations, measuring player satisfaction, telemetry data analysis, and more. A data warehouse is one of the components in a data hub.

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4 ways to ensure CEO support for your digital strategy

CIO Business Intelligence

Data is one of the most important levers the CIO can use to have an effective dialogue with the CEO. But we also have our own internal data that objectively measures needs and results, and helps us communicate with top management.”

Strategy 134
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Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1

AWS Big Data

In this post, we will review the common architectural patterns of two use cases: Time Series Data Analysis and Event Driven Microservices. The collected data is available in milliseconds to allow real-time analytics use cases, such as real-time dashboards, real-time anomaly detection, and dynamic pricing.

Analytics 116
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6 strategic imperatives for your next data strategy

CIO Business Intelligence

The focus here should be on considering all ways your customers currently consume data as well as new ways they might want to achieve better results. So much, in fact, that it’s worth measuring what percentage of your portfolio utilizes data and analytics as part of the offering, and tracking this over time.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

1) What Is Data Quality Management? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. 10) Data Quality Solutions: Key Attributes.

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Building a vision for real-time artificial intelligence

CIO Business Intelligence

Real-time AI brings together streaming data and machine learning algorithms to make fast and automated decisions; examples include recommendations, fraud detection, security monitoring, and chatbots. The underpinning architecture needs to include event-streaming technology, high-performing databases, and machine learning feature stores.