Leverage GridGain to Accelerate and Scale Out Your Current or Future Architectures

GridGain provides in-memory speed and massive scalability to new or existing applications which can provide the performance needed for digital transformation and omnichannel customer experience initiatives.

The GridGain in-memory computing platform easily integrates with your architectures, deployed as an in-memory computing layer between the application and data layer of your new or existing applications. GridGain is typically deployed as an in-memory data grid for existing applications and as either an in-memory data grid or as an in-memory database for new applications. A Unified API, including ANSI-99 SQL and ACID transaction support, provides easy integration with your existing code, enabling you to create modern, flexible applications built on an in-memory computing platform which will grow with your business needs.

A variety of resources including white papers, webinar recordings, application notes, product comparisons, and videos are listed below which discuss use case considerations from an architectural standpoint.


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 how GridGain has helped improve the customer experience.
The in-memory computing solutions of the future must not only offer the key capabilities that database users expect, such as SQL support, but also provide a bridge to emerging use cases, such as machine learning and deep learning, and transformative new storage technologies, such as non-volatile memory. This white paper delves into these application-crucial topics and then shows how Apache Ignite and GridGain are addressing them.
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.

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.

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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. Because it stores data on disk, Cassandra is not fast enough for some of today’s extreme OLTP workloads. Also, Cassandra shares certain limitations of other NoSQL databases, such as limited querying capabilities.

Fortunately, there is a simple way to make Cassandra much faster and more flexible.

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 technologies; and how they fit into an IoT architecture. It also explains how Apache® Ignite™ (Ignite) and GridGain® are used for IoT.

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 interactions. It also requires companies to act in real-time.

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; new or existing HTAP applications; streaming analytics; and continuous learning use cases involving machine or deep learning.
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.

Вебинар завершился, но вы можете заполнить форму и получить доступ к видео и слайдам.

Over the last decade, the 10x growth of transaction volumes, 50x growth in data volumes, and drive for real-time response and analytics has pushed relational databases beyond their limits. Scaling an existing RDBMS vertically with hardware is expensive and limited. Moving to NoSQL requires new skills and major changes to applications. Ripping out the existing RDBMS and replacing it with another RDBMS with a lower TCO is still risky. 

In this webinar Alexey Zinoviev, Apache Ignite ML contributor for GridGain will talk about new 2.7 release of Apache Ignite and present the new features that are added to Ignite ML modules.

In the second phase of his presentation he will introduce what a Java programmer needs to do and understand in a typical Big Data and ML projects.

In this webinar you will learn:

- How to choose features

- How to encode features

- How to scale

- How to  clear and fill in the missed values

- How to evaluate the quality of the model

Regardless of how mature a data storage technology is, backing up data is a laborious and difficult task that can cost us time, increase our stress levels and jeopardise our jobs. 

The 10x growth of transaction volumes, 50x growth in data volumes -- along with the drive for real-time visibility and responsiveness over the last decade -- have pushed traditional technologies including databases beyond their limits. Your choices are either to buy expensive hardware to accelerate the wrong architecture, or do what other companies have started to do and invest in technologies being used for modern hybrid transactional/analytical processing (HTAP).

Digital transformations are arguably the most important initiatives for companies. They can literally make or break a business.  But transformation is not easy because there’s a big digital divide between the speed, scale and computing needed for new digital channels and APIs, and what existing systems can deliver. Learn how leading digital innovators have solved these problems by using in-memory computing, and the roadmaps that worked for them.
Learn some of the best practices companies have used for making Apache Ignite and Apache Kafka scale. Making stream processing scale requires making all the components—including messaging, processing, storage—scale together.   During this 1-hour webinar by GridGain Systems Professional Services Consultant Alexey Kukushkin, you will learn about:

Вебинар прошел, но вы можете получить доступ к записи и всем материалам, заполнив регистрационную форму. 

Вебинар посвящен особенностям распределенных систем, связанных с шардированием как методом обеспечения горизонтального масштабирования. В рамках вебинара Артем Шитов, GridGain Solutions Engineer, расскажет про:

In this presentation, attendees will learn about Apache Ignite and the GridGain in-memory computing platform, which is built on Apache Ignite, and about the key capabilities and features important for financial applications, including ACID compliance, SQL compatibility, persistence, replication, security, fault tolerance, fraud detection and more.

Once your Apache® Ignite™ or GridGain® cluster goes in production, you will need to keep an eye on its state. You will also have to manage your deployment throughout its lifetime. These tasks might seem challenging, but managing and monitoring distributed systems is not cumbersome if you have a right toolkit.

In 1-hour webinar, Andrey Evsukov, Head of Operations at GridGain Systems, will:

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 experience requires performing real-time analytics and automation during transactions and interactions, not after. Traditional data warehouses and other related tools cannot address these needs because, by design, they work on a copy of the data that is almost immediately out of data as it is extracted, transformed and loaded (ETL) from operational systems. They also don’t support the new analytical approaches, from stream processing to artificial intelligence, needed for these new initiatives. The good news is that several companies have successfully implemented these new approaches to real-time analytics with the GridGain® in-memory computing platform. Download this Application Note and learn GridGain adds speed, scalability, and in-memory computing to SQL.
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 needs of trading, settlement, accounting, customer portfolio management, risk management, internal and regulatory compliance. They have achieved all of this on a common platform with in-memory speed, unlimited horizontal scalability and broad integration to support any future needs. Download this Industry Brief to learn how.


Apache Spark is a leading open source fast and general-purpose engine for large-scale data processing of streaming data. Part of its stellar rise has been its adoption as the de-facto processing engine for Apache Hadoop™. But while Spark supplanted MapReduce for processing, no solution for real-time data management has emerged. Spark doesn’t have features for managing data or processing state. As a result, developers using Spark often write extensive code to ingest, prepare and enrich, store and manage data and state. 

Compares GridGain and Redis features in 22 key areas: in-memory data grid functionality, caching, data querying, transactions, security and more.
Compares GridGain and Pivotal GemFire features in 25 areas: in-memory data grid functionality, caching, data querying, transactions, security and more.
Compares GridGain and Hazelcast features in 22 key areas: in-memory data grid functionality, caching, data querying, transactions, security and more.
Compares GridGain and GigaSpaces features in 22 key areas: in-memory data grid functionality, caching, data querying, transactions, security and more.
Compares GridGain and Terracotta features in 22 key areas: in-memory data grid functionality, caching, data querying, transactions, security and more.
Compares GridGain and Oracle Coherence features in 22 areas: in-memory data grid functionality, caching, data querying, transactions, security and more.

Valentin Kulichenko, lead architect at GridGain Systems, spoke June 26 at the In-Memory Computing Summit Europe 2018 in London. His talk at the In-Memory Computing Summit Europe 2018, June 25-26 in London, was titled: "Want Extreme Performance at Scale? Do Distributed the RIGHT Way!" It is well-known that distributed systems rely on horizontal scalability. The more machines in your cluster, the better performance of your application. Well, not always.