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


This data sheet provides the key features and benefits of the GridGain in-memory computing platform.
If your company is one of the tens of thousands of organizations that use Apache® IgniteTM or GridGain® Community Edition in a production environment, GridGain Basic Support can provide you with peace of mind that you have a trusted partner to help keep your environment running flawlessly. The service includes....
This data sheet provides the key features and benefits of the GridGain In-Memory Accelerator for Hadoop and Spark.

White Papers

This white paper covers in-depth the architecture, key capabilities and features of GridGain®, and as well as its key integrations such as leading RDBMSs, Apache Spark™, Apache Cassandra™, MongoDB® and Apache Hadoop™. You will learn how GridGain can add in-memory speed and unlimited horizontal scalability to your company’s existing or new OLTP or OLAP applications;…
This paper, written by GridGain founder and CTO, Nikita Ivanov, sums up the architecture and key capabilities of the Apache® Ignite™ project. It discusses the key features of Apache Ignite and integrations with Apache Spark and Apache Cassandra.
Learn how high-performance in-memory computing architecture is rapidly becoming the method of choice for today’s real-time applications that are focused on big data, fast data, streaming analytics or machine and deep learning.
Digital transformation, whether it’s done to improve the customer experience or operations, is the biggest opportunity and threat for most companies. But transforming existing IT infrastructure to support digital business is hard. Digital business can increase query and transaction volumes up 10 to 1000x, and generate 50x or more data about customers, products, and…
Many companies who have succeeded with IoT have solved their challenges around speed, scalability and real-time analytics with in-memory computing. Across these deployments some common architectural patterns have emerged. This whitepaper explains some of the most common use cases and challenges; the common technology components, including in-memory computing…
Applications and their underlying RDBMSs have been pushed beyond their architectural limits by new business needs, and new software layers. Companies must add speed, scale, agility, and new capabilities to support digital transformation and other business critical initiatives.
  MySQL® is a widely used, open source relational database management system (RDBMS) which is an excellent solution for many applications, including web-scale applications. However, its architecture has limitations when it comes to big data analytics.
This white paper will take a detailed look at the challenges faced by companies that have either used Redis and run into its limitations, or are considering Redis and find it is insufficient for their needs. This paper will also discuss how the GridGain in-memory computing platform has helped companies overcome the limitations of Redis for existing and new…
Applications and their underlying RDBMSs have been pushed beyond their architectural limits by new business needs, and new software layers. Companies have to add speed, scale, agility and new capabilities to support digital transformation and other business critical initiatives.
Download this white paper to learn about your options for adding speed, scale and agility to end-to-end IT infrastructure—from SAP HANA to third-party vendors and open source. It also explains how to evolve your architecture over time for speed and scale, become more flexible to change, and support new technologies as needed.
This white paper reviews why IMC makes sense for today’s fast-data and big-data applications, dispels common myths about IMC, and clarifies the distinctions among IMC product categories to make the process of choosing the right IMC solution for a specific use case much easier.
Apache® Cassandra™ is a popular NoSQL database that does certain things incredibly well. It can be always available, with multi-datacenter replication. It is also scalable and lets users keep their data anywhere. However, Cassandra is lacking in a few key areas – particularly speed.
Read this white paper and learn how financial services companies are using in-memory computing to address the technical challenges caused by new and recent financial regulatory initiatives
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.
This paper looks at the current state of high-frequency trading – why it’s popular and what types of strategies and technologies are being used – and then explores how in-memory computing can meet the technological challenges and increase profits within this market segment.

Application Notes/Industry Briefs
Over the last few years, new business demands – from digital transformation to improving the customer experience – have overwhelmed existing SQL infrastructure. The increase in interactions through new Web and mobile apps and their underlying APIs are creating massive volumes of queries and transactions that are overloading existing databases. Improving the customer…
GridGain and Ignite provide the ideal underlying in-memory data management technology for Apache Spark because of its in-memory support for both stored “data at rest” and streaming “data in motion.” Learn how this makes many Spark tasks simple, including stream ingestion, data preparation and storage, stream processing, state management, streaming analytics, and…
By 2020, Gartner expects the Internet of Things (IoT) to have over 20 billion connected things, a conservative estimate compared to other analysts. The information generated by connected devices requires an enormous amount of real-time processing and storage.
Leading banks and fintech companies have already adopted the GridGain in-memory computing platform as the foundation for FRTB and their next generation trading systems. With GridGain, these banks have been able to rapidly implement the required XVA calculations, continuously run their new risk models and price new securities in near real-time. Learn more now.
Leading banks, asset management firms and fintech companies rely on the GridGain in-memory computing platform as their new foundation for real-time risk analytics, portfolio management and regulatory compliance. With GridGain, these companies have brought together many different types of information to achieve a common, real-time view of risk. They have supported the…
Omnichannel banking requires more than a consistent API strategy across channels. It requires a single, real-time view of the customer that can be seamlessly shared across channels; infrastructure that can handle 10x or greater loads created by the increased interactions on digital channels; the ability to proactively personalize, promote and improve each customer’s…
FinTech companies are faced by many of the same challenges as their largest customers with speed, scale and innovation. Their new channels and services, as well as core banking, insurance and real estate systems must deliver 100-1000x speed and scale compared to existing systems.
Being a fast follower or late adopter may have been good enough in the past. But now, to maximize their chances of surviving and thriving, insurers must not only fulfill the latest regulatory requirements. They must also be the first to innovate in three areas: Customer and Risk Analytics, Customer Experience Management, and Digital Business. Download the Industry…
Learn how companies such as Expedia, HomeAway, JacTravel and TUI Group rely on GridGain and Apache Ignite In-Memory Computing Platforms to deliver a real-time, personalized, seamless end-to-end experience to their customers.
Across the federal government, legacy systems are struggling to keep up with the exponential growth in data, devices, users, and analytical needs. The adoption of big data, advanced analytics, and digital government platforms has driven a 10-1000x growth in interactions and a 50x growth in data over the last decade. New infrastructure is needed that can deliver a new…
The opportunity for communications and media companies is to transform into more modern, digital providers. This will help drive renewed growth from new OTT services over IP, as well as from services for security and the Internet of Things (IoT).

Reports and Guides

451 Research believes that an increasing proportion of enterprise operational applications will rely on hybrid operational and analytic processing (HOAP) to support use cases such as recommendations, personalized content and offers, and real-time fraud analysis. The availability of high-performance in-memory computing technology, therefore, lays the foundation for…
While IT shops may be generally familiar with traditional in-memory databases - an
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Complete the form to the right to download this comprehensive 12-page guide written by respected analyst Danie
An expanding use of analytics tools that can converge and extract value from mul
Complete the form to the right to download this comprehensive 12-page guide written by
By reducing the latency in operational and analytical processing, organizations can