CTOs/CIOs Use GridGain to Accelerate and Massively Scale Applications

CTOs and CIOs are increasingly initiating digital transformations to drive better business decision making and performance. Meeting new customer experience requirements requires improving applications to deliver real-time performance and massive scalability. Getting access to data in real-time allows your business to make better decisions. In-memory computing is the answer for businesses pursuing digital transformation. Deploying effective in-memory computing solutions in time and within budget requires a trusted technology partner.

In-Memmory Computing
GridGain In-Memory Computing Solutions

GridGain offers the in-memory computing platform software, support, and professional services you need to better achieve your real-time digital transformation technical challenges. The GridGain in-memory computing platform easily integrates with your new or existing applications and provides real-time performance and massive scalability.

Built on the open source Apache Ignite project, GridGain is a cost-effective solution for accelerating and massively scaling out new or existing applications with users that span a wide variety of use cases and industries.

The GridGain in-memory computing platform easily integrates with your systems, deployed as an in-memory computing layer between the application and data layer of your new or existing applications. GridGain can be deployed on-premises, on a public or private cloud, or on a hybrid environment.

The GridGain In-Memory Platform Helps CTOs and CIOs Achieve Goals

For existing applications, GridGain is typically used as an in-memory data grid between the application and data layer, with no rip-and-replace of the underlying database. For new applications, GridGain is used as an in-memory data grid or an in-memory database. A Unified API provides easy integration with your existing code with support for SQL, Java, C++, .NET, Scala, Groovy, and Node.js, enabling you to create modern, flexible applications built on an in-memory computing platform which will grow with your business needs. GridGain includes ANSI-99 SQL and ACID transaction support.

Business Decision Makers
Learn How GridGain Provides In-Memory Computing Solutions for CTOs and CIOs

The white papers, webinars, application notes, product comparisons, and videos below discuss the business benefits for CTOs and CIOs looking for in-memory computing solutions.


This white paper provides insight on improving the scale, speed, and agility of MySQL so that it can support the digital transformation initiatives of today's enterprises. New business needs and performance demands have pushed many applications beyond MySQL's (and other RDBMSs) architectural limits. In many cases, the issues cannot be remedied by just fixing MySQL.
This white paper explains how to use in-memory computing to add PostgreSQL speed and scale options to end-to-end IT infrastructure—both from PostgreSQL-centric vendors and from other open source and third-party products. It also explains how help create flexible IT infrastructure over time to both increase speed and scale.
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.
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 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 best long term approach is in-memory computing.
This white paper discusses the challenges facing today’s insurance industry, the opportunities new technologies can offer, and the crucial edge that providers can gain with solutions such as the GridGain in-memory computing platform.
This white paper discusses how an in-memory computing platform solution like GridGain gives financial services companies the speed, scalability, and flexibility they need to build successful IoT-based applications and services.
This white paper discusses how in-memory computing is helping companies address increasing mobile application usage, real-time data needs, improving the customer experience, fraud prevention, compliance, and other requirements to modernize and accelerate payment solutions.
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.
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.

Attendees will be introduced to the fundamental capabilities of in-memory computing platforms (IMCPs). IMCPs boost application performance and solve scalability problems by storing and processing unlimited data sets distributed across a cluster of interconnected machines.

Apache Ignite® and GridGain® allow you to perform fast calculations and run highly efficient queries over distributed data. Both Ignite and GridGain provide a flexible configuration that can help you make cluster operations more secure. In this webinar, we will cover the following security topics:

  • The secure connection between nodes (SSL/TLS)
  • User authentication
  • User authorization

Using live examples, we will go through the configurations for:

Change Data Capture (CDC) has become a very efficient way to automate and simplify the ETL process for data synchronization between disjointed databases. It is also a useful tool for efficient replication schemas. We will cover the fundamental principles and restrictions of CDC and review examples of how change data capture is implemented in real life use cases. By the end of this session you will understand:

With most machine learning (ML) and deep learning (DL) frameworks, it can take hours to move data and to train models. It can also be hard to scale with data sets that are increasingly frequently larger than the capacity of any single server. The size of the data can also make it hard to incrementally test and retrain models in near real-time to improve business results.

Deployment models for Apache Ignite® and applications connected to it vary depending on the target production environment. A bare metal environment provides the most flexibility and fewer restrictions on configuration options. When using Docker and Kubernetes environments, you need to decide how Ignite and its associated applications will interact before writing the first line of code.

To take full advantage of an in-memory platform, it’s often not enough to upload your data into a cluster and start querying it with key-value or SQL APIs. You need to distribute the data efficiently and tap into distributed computations that minimize data movement over the network.

In this webinar, you’ll see how to design and execute distributed computations considering all the pros and cons. In particular, the following will be covered:

Apache Ignite is a powerful in-memory computing platform. The Apache IgniteSink streaming connector enables users to inject Flink data into the Ignite cache. Join Saikat Maitra to learn how to build a simple data streaming application using Apache Flink and Apache Ignite. This stream processing topology will allow data streaming in a distributed, scalable, and fault-tolerant manner, which can process data sets consisting of virtually unlimited streams of events.

Most enterprises have PostgreSQL deployments that they will be using for years to come for transactional, big data, mobile, and IoT use cases. How can Postgres continue to support the current and emerging use cases which demand ever higher performance and more scalability into the future? In this webinar we will discuss methods to accelerate and scale out this highly popular database including caching, sharding, and in-memory data grids such as Apache® Ignite.

Learn some of the best practices and the different options for maximizing availability and preventing data loss. This session explains in detail the various challenges including cluster and data center failures, and the best practices for implementing disaster recovery (DR) for distributed in-memory computing based on real-world deployments. Topics include:

This webinar discusses deploying Apache Ignite into production in public and private clouds. Companies have faced many challenges when deploying in-memory computing platforms such as Apache Ignite in the cloud, but they have also discovered many best practices that have made success possible.

No results found

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 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), and of new types of data including social and machine data. Their increased adoption has driven up transaction, query, and data volumes, as well as the new for real-time responsiveness.

This eBook explains how to:

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 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:

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.
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 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.
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.