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.
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.
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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:
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: