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.
Capital markets applications often require high performance, massive scalability, and high-performance data access across the enterprise to meet the demands of modern, digital business activities. In addition, digital transformations may require capital markets companies to design architectures that enable multiple business applications to access data from multiple, disparate data sources in real time.
The latest release of the GridGain in-memory computing platform features enhanced support for the platform’s multi-tier database engine, that scales up and out across memory and disk. The changes enable customers to leverage the disk tier of the database engine to query much larger data sets, reduce cost of ownership, and secure sensitive or personal data at rest. Companies can use GridGain for a greater number of production use cases, ranging from complex real-time analytics to mission-critical transactional workloads
GridGain Nebula, a managed services offering (MSO) for Apache Ignite and GridGain, can offload the management of your in-memory computing environment to provide maximum reliability at a fraction of the cost of staffing an internal team. This allows your organization to focus on developing applications built on GridGain or Ignite without requiring an internal IT team to manage your in-memory computing environment.
Typically, operations exceed fifty percent of the cost of an IT system’s life cycle. By developing applications that can be easily managed, developers can significantly reduce the cost of ownership. Manageability is especially important for distributed applications because they are especially complex and often mission-critical.
In this workshop, Alexey discusses the following topics:
Kafka with Debezium and GridGain connectors enables change data capture (CDC) based synchronization between third-party databases and GridGain clusters. Synchronization that is based CDC does not require coding; all it requires is to prepare configuration files for each of the points. Developers and architects who can’t yet move from a legacy system can deploy this solution to boost the performance of their applications or to enable their applications to access data from multiple data silos and store it in one place
Apache Ignite can scale horizontally to accommodate the data that your applications and services generate. However, in practice, most of us cannot scale out a cluster instantly.
In this webinar, Denis Magda will introduce several architectural techniques that can help you keep your cluster operational and your applications running even if memory becomes a scarce resource. During the webinar demo you will learn how to use those techniques in practice. Topics covered include:
Serverless computing allows you to design and build scalable cloud-native applications without thinking about infrastructure provisioning and orchestration. With Apache Ignite, you can bootstrap an in-memory cluster in the cloud and access data 100-1000x faster than with disk-based databases.
In this webinar, Denis Magda will discuss architectural patterns and design considerations for deploying Apache Ignite in a serverless computing environment. In particular, you will learn the following:
Debugging distributed-system applications can be more complex than debugging traditional monolithic applications. As API calls jump across nodes in the cluster, it can be tricky to follow the execution just by analyzing application logs. Tracing adds a useful tool to the root-cause-analysis toolbox. Tracing makes it easier to follow the execution path, analyze timings, and align API calls with logs and stack traces. Apache Ignite 2.9 introduces new tracing instrumentation that is based on the OpenCensus/OpenTelemetry standard.
Growing customer traffic, the launch of new services, seasonal traffic spikes or a host of other load-related issues can slow down customer-facing applications, resulting in poor customer experiences. In-memory computing offers a solution to overcome performance-related challenges and deliver outstanding customer interactions. Inserted between existing application and data layers, in-memory computing platforms can improve application performance up to 100x and enable massive application scalability.
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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: