Downloadable eBooks on In-Memory Computing Topics

GridGain® ebooks are collections of helpful and insightful posts from GridGain in-memory computing technology experts and the GridGain support, consulting, and training services teams. GridGain ebooks present best practices for implementing in-memory computing solutions that leverage the GridGain and Apache® Ignite in-memory computing platforms. The ebooks are based on our in-depth experience developed through developing in-memory computing software, providing in-memory computing services, and working with customers to deploy in-memory computing solutions. The ebooks present information on a variety of in-memory computing topics faced by in-memory computing developers, enterprise architects, CTO/CIOs, and other enterprise business decision makers.

All GridGain ebooks are available for free download.

This Machine Learning and Deep Learning primer, the second in the “Using In-Memory Computing for Continuous Machine and Deep Learning” Series, is a hands-on tutorial that covers how to use the Apache Ignite built-in machine learning algorithms Linear Regression, k-Nearest Neighbor (k-NN), k-Means Clustering, and Compute Mean Entropy.
In this eBook, you’ll learn best practices for establishing a sound and cost-effective in-memory computing foundation for digital transformation. This eBook is Part 1 of the best practices for digital transformation series.
This eBook explains the best practices for adding speed and scale to existing applications that offer the least disruption and help meet the long term goals of transforming the business. Performance and scalability challenges exist because of the adoption of new customer-facing Web and mobile channels, of new technologies such as the Internet of Things (IoT),…
In this eBook you'll learn the best practices for delivering new applications and APIs with in-memory computing, and how it helps open up existing systems, become more agile, and deliver unlimited speed and scale. This eBook is Part 3 of the best practices for digital transformation series.
This Machine and Deep Learning Primer, the first eBook in the “Using In-Memory Computing for Continuous Machine and Deep Learning” Series, is designed to give developers a basic understanding of machine and deep learning concepts. Topics covered include:
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This eBook, Part 1 in the In-Memory Computing for Financial Services eBook Series, discusses how financial service firms are using in-memory computing platforms such as GridGain and Apache® Ignite™ to address the challenges of high-frequency trading, fraud prevention and real-time regulatory compliance.
This eBook, Part 2 in the In-Memory Computing for Financial Services eBook Series, discusses how financial service firms are using in-memory computing platforms such as GridGain and Apache® Ignite™ in their strategy to improve the performance of payment systems, IoT applications, and bitcoin/blockchain technology.
This eBook, Part 3 in the In-Memory Computing for Financial Services eBook Series, discusses how financial service firms are using in-memory computing platforms such as GridGain and Apache® Ignite™ in their strategy to improve the performance of asset and wealth management, spread betting and banking applications.
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If you are new to in-memory computing, curious to learn how in-memory computing can be used for financial applications, or seeking to educate a non-technical team member about the benefits of in-memory computing for financial applications, this eBook can help.
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