GridGain for CTOs/CIOs - GridGain Systems

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.

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.

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.

Resources

Options for Adding Speed and Scale to MySQL

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.

PostgreSQL Speed and Scale Options with In-Memory Computing

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.

A Roadmap for In-Memory Computing: Current and Emerging Use Cases

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.

In-Memory Computing Options for SAP Customers: When to Use SAP HANA, or Something Else

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.

Increasing Microsoft SQL Server Speed and Scale with In-Memory Computing

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.

Empowering Insurance Companies with 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.

Boost Performance of Financial Services IOT Projects with In-Memory Computing

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.

Modernize and Accelerate Payment Solutions with In-Memory Computing

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.

Choosing the Right In-Memory Computing Solution

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.

Empowering Fintech with In-Memory Computing

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.

Moving Apache Ignite into Production: Best Practices For Disaster Recovery and High Availability

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:

Moving Apache® Ignite™ into Production: Best Practices for Deploying Apache Ignite in the Cloud

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.

In-Memory Computing Best Practices for Real-Time Analytics, HTAP, and Automation

This in-memory computing best practices webinar explains how companies add in-process Hybrid Transactional/Analytical Processing (HTAP) architectures for real-time data access, analytics, and decision automation to their existing applications and analytics systems.

Enabling Real-Time Analytics for Hadoop Data Lakes with GridGain

This webinar discusses how an in-memory computing platform such as GridGain or Apache Ignite can modernize existing data lake architectures, enabling real-time analytics that spans operational, historical, and streaming data sets.

Moving Apache® Ignite™ into Production: Best Practices for Monitoring Distributed In-Memory Computing

Learn how to monitor various components of a distributed cluster for network, memory, or node-specific issues, and troubleshoot to resolve issues. By the end of this session you'll have a handy checklist and set of tools to consider using for your own deployments.

Options for Adding Speed and Scale to MySQL Deployments

Learn how companies have added speed and scale to MySQL deployments for different use cases. This webinar will cover the various options available and when each option makes sense. It will also cover how to evolve your architecture over time to add the speed, scale, agility and new technologies needed for digital transformation and other initiatives.

Moving Apache® Ignite™ into Production: Best Practices for Native Persistence and Data Recovery

As an in-memory computing platform, GridGain® and Apache Ignite support native persistence that stores data and indexes transparently on non-volatile memory, SSD or disk. This webinar provides insights into the underlying architecture and best practices for implementing native persistence in production.

Options for Adding Speed and Scale to PostgreSQL Deployments

Learn how companies have added speed and scale to PostgreSQL deployments for different use cases. This webinar will cover the various options available and when each option makes sense. It will also cover how to evolve your architecture over time to add the speed, scale, agility and new technologies needed for digital transformation and other initiatives.

Moving Apache® Ignite™ into Production: An Initial Checklist

Guaranteeing that your in-memory computing solution stays up and running is the most important goal for a rolling out a new production environment. In this webinar, we will review a going-into-production checklist to make sure you have all the bases covered and have thought through all your requirements, needs and potential roadblocks.

Achieving Continuous Machine and Deep Learning with Apache Ignite and TensorFlow

With most machine learning (ML) and deep learning (DL) frameworks, it can take hours to move data, and hours to train models. Learn how Apache Ignite eliminates runs model training and execution in near-real-time and makes continuous learning possible.

In this Webinar, Yuri Babak, the head of ML/DL framework development at GridGain and major contributor to Apache Ignite, will explain how ML and DL work with Apache Ignite, and how to get started. Topics include:

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Building New Apps and APIs

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.

Adding Speed and Scale to Existing Applications

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:

Building an In-Memory Computing Foundation and Roadmap

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.

A Machine and Deep Learning Primer

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:

In-Memory Computing for Financial Services eBook: Part 3

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.

In-Memory Computing for Financial Services eBook: Part 2

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.

In-Memory Computing for Financial Services eBook: Part 1

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.

eBook: An Overview of In-Memory Computing for High Performance Financial Applications

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.

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