Download datasheets, white papers 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 also about common and emerging use cases.
This data sheet provides the key features and benefits of the GridGain In-Memory Data Fabric.
This data sheet provides the key features and benefits of the GridGain In-Memory Accelerator for Hadoop and Spark.
The time is right to leverage the improved performance and scale provided by in-memory computing to make software and SaaS implementations run optimally. This white paper discusses how an in-memory computing platform like GridGain and Apache Ignite can boost software and SaaS solutions.
To achieve competitive application performance, scalability, and analytical sophistication, many financial-services providers are turning to in-memory computing solutions. This white paper will discuss the increased expectations of investors, the new challenges providers are facing, and how providers can gain the edge they need with solutions such as the GridGain in-memory computing platform.
One way to evolve eCommerce technology is to make it as fast, available, and scalable as possible. This white paper discusses how an in-memory computing platform can accomplish this, while both providing competitive advantage and addressing the issues that eCommerce developers face.
This white paper will discuss bitcoin and blockchain technology, describe the innovative opportunities this technology offers for financial services firms, and examine how in-memory options such as the GridGain in-memory computing platform can address the challenges and boost the effectiveness of tomorrow’s blockchain applications.
This white paper will help you understand how incorporating Apache Ignite into your architecture can empower dramatically faster analytics and transactions when augmenting your current MySQL® infrastructure.
Spread betting is a high-volatility, minimally regulated market with significant risks. To limit these risks – and increase the rewards – financial institutions involved in spread betting are using advanced mathematical models to analyze large amounts of data, predict outcomes, and devise optimal strategies. These computationally intense actions must be performed at very high speeds to take advantage of current market conditions.
Fortunately, there are technologies today that provide the real-time speed needed for such strategies. This white paper will discuss the advantages and risks of spread betting, the technologies being used for it, and the reasons why in-memory computing is becoming the technology of choice for brokerages, asset managers, online gambling firms, and other players who want to succeed at spread betting.
Fraud prevention is no simple task, and firms must tackle it simultaneously with other crucial tasks such as ensuring regulatory compliance. Fortunately, today’s in-memory technologies provide power tools for combatting fraud – tools that perform complex processing, modeling, and analysis of big data in real-time.
This white paper discusses how firms are addressing fraud, and why in-memory computing technologies such as GridGain are perfectly suited to the task of detecting and stopping fraud.
This white paper will discuss the lack of in-memory capabilities in Apache® Cassandra™, why in-memory solutions make sense for today’s web and cloud applications, and how combining Cassandra with Apache® Ignite™ or GridGain can create an architecture that provides a substantial speed boost and a host of other benefits.
This white paper takes a look at popular use cases for and components of IoT. It then discusses the data architecture and resource challenges caused by typical approaches to IoT. You will learn how the GridGain in-memory computing platform provides a way to simplify this architecture while reducing your team’s technical learning curve.
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.
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
GridGain in memory computing platform, built on Apache® Ignite™ , enables high-performance transactions, real-time streaming, and fast analytics in a single, comprehensive data access and processing layer. This white paper covers the architecture, key capabilities and features of GridGain, and its integrations with Apache® Spark™ and Apache® Cassandra™.
This paper, based on a presentation by GridGain CEO Abe Kleinfeld, discusses the current state and future impact of in-memory computing on society.
This white paper provides an overview of in-memory computing technology with a focus on in-memory data grids. It discusses the advantages and uses of in-memory data grids and introduces the GridGain In-Memory Data Fabric. Finally, it presents a deep dive on the capabilities of the GridGain solution.
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.
This white paper describes common streaming functionality along with a comparison with other approaches, and explains specific real-time streaming capabilities provided by the GridGain In-Memory Data Fabric.
This white paper discusses performance-related challenges typically found when processing Hadoop data, and describes the GridGain plug-and-play approach to accelerating MapReduce jobs in memory.
Access this important brief that presents common in-memory computing use cases for financial enterprises as well as real-world examples of results attained by GridGain customers.
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.
Reports and Guides
Despite only being open sourced as Apache Ignite in 2014, the core of the GridGain in-memory computing platform is picking up impressive momentum. Downloads, adoption – and indeed GridGain staff and paying customers – are all going in the right direction. The company is flush with another round of funding, and appears to be riding a wave of enthusiasm for not only Apache Ignite, but also the GridGain Enterprise Edition. Read this report by 451 Research to learn more.
Access this essential guide to Hyperscaling your SaaS (or PaaS or IaaS) infrastructure through In-memory technology written by the leading Big Data publishing firm, Inside BIGDATA.
Download this in-depth technical insight paper by respected IT analyst firm the Evaluator Group which covers how fast data technologies such as the In-Memory Data Fabric enable faster decision making and new business opportunities.
Download this insightful infographic showing the results of a survey of about 200 IT decision-makers — including project managers, network managers, software and business analysts, and other technology professionals working in the financial services industry.
Download this informative Guide to In-Memory Computing and Data Fabric technology by Inside BIGDATA, one of the most authoritative publishers in the field of Big Data Analytics.
Proactively Building Competitive Advantage: Mastering Business with the Speed of In-Memory Computing
Read EMA’s thorough coverage of how In-Memory Computing and Data Fabric technology are driving the growth of industries like finance, utilities and consumer retailing.
Access this third-party report by respected analyst firm The Taneja Group on the importance of GridGain’s approach to Big Data and Hadoop acceleration in particular.