Technical Papers for Developers

Download datasheets, white papers, application notes, industry briefs, reports, and eBooks from GridGain® Systems on a range of topics related to in-memory computing. These free resources discuss the technology behind GridGain and Apache® Ignite and discuss common and emerging use cases for in-memory computing. Leaders in the in-memory computing field write about the current state of in-memory computing technology as well as common and emerging use cases.

All GridGain literature is available for free download.

eBooks
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.
White Paper
Financial fraud detection and prevention is not a simple task, and firms must tackle it simultaneously with other crucial tasks such as ensuring regulatory compliance. To accomplish these data-intensive tasks in a timely manner, financial firms need solutions that are flexible, scalable, reliable, and fast enough to analyze extremely large datasets in real-time.
Application Notes
Omnichannel banking needs a single, real-time view of the customer that is shared across channels. Companies use the GridGain in-memory computing platform to create infrastructure for 10x or greater digital channel loads, proactively personalize and improve the customer’s experience, and allow real-time analytics and automation. This Industry Brief tells you how.
Product Comparison
This in-depth feature comparison shows how the most current versions of GridGain Professional Edition, Enterprise Edition, Ultimate Edition and Redis Enterprise (and their respective open source projects where relevant) compare in 25 categories.
White Paper
This white paper discusses how to increase and accelerate your Oracle database speed and scale using in-memory computing. There are many Oracle® options for adding speed and scale to Oracle Database, or for replacing it—including Oracle RAC, Oracle Database In-Memory, Oracle Exadata, Oracle GoldenGate, Oracle TimesTen Classic, Oracle TimesTen Scaleout, and Oracle Coherence—and…
Case Study
Finastra deployed the GridGain in-memory computing platform to embrace real-time services and satisfy evolving compliance, reporting regulations, and customer demands in Europe. As a large financial technology company, Finastra solutions must manage huge amounts of trade and accounting data. Finastra used GridGain to help to implement a new Java-based IT stack that supports…
White Paper
This white paper covers the architecture, key capabilities, and features of GridGain®, as well as its key integrations for leading RDBMSs, Apache Spark™, Apache Cassandra™, MongoDB® and Apache Hadoop™. It describes how GridGain adds speed and unlimited horizontal scalability to existing or new OLTP or OLAP applications, HTAP applications, streaming analytics, and continuous…
eBooks
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.
White Paper
This white paper will give you a better understanding of how in-memory computing forms the backbone of successful high performance, highly scalable and mission-critical technology solutions in the FinTech industry. You will also learn how in-memory computing helps address many current limitations of legacy financial systems.
Case Study
Intelligentpipe Leverages GridGain® In-Memory Computing Platform for Real-Time Analytics Big Data Software Company Leverages GridGain for Terabytes of Mobile User Traffic Data With offices in Finland and Singapore, Intelligentpipe is a b
Report or Guide
While IT shops may be generally familiar with traditional in-memory databases - an
eBooks
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.
White Paper
Spread betting offers some compelling advantages, including low entry and transaction costs, preferential tax treatment, and a diverse array of products and options.
Application Notes
Leading banks, asset management firms, and fintech companies rely on the GridGain in-memory computing platform as a foundation for real-time risk analytics, portfolio management, and regulatory compliance. These companies use Gridgain to achieve a common, real-time view of risk by bringing together many types of information. Download this Industry Brief to learn how.
Product Comparison
This product comparison describes the advantages and benefits of migrating from DataSynapse to GridGain as an in-memory computing solution to power mission-critical and data-intensive applications.
White Paper
This white paper discusses how to increase Microsoft® SQL Server® speed and scale using in-memory computing. There are options for adding speed and scale to Microsoft SQL Server® at the database level—including SQL Server Always On Availability Groups and SQL Server In-Memory OLTP—and each has its place. But when the speed and scale needs extend beyond the database layer, the…
Case Study
FSB deployed GridGain to speed up its Postgres database and scale-out their cluster. For FSB Technology to support live betting during sporting events, their betting platform must be real-time and highly performant. Huge amounts of event data must be constantly updated, and immediately available to a vast number of clients. GridGain Enterprise Edition helped FSB add nodes in…