Architects Use GridGain to Accelerate and Scale-Out Applications
Enterprise architects need to speed up and scale out new or existing enterprise applications to drive new digital initiatives. Digital transformation requires a new generation of business applications that ingest, process and analyze data in real-time to drive optimal user experiences. With GridGain, you can create modern, flexible applications built on an in-memory computing platform that can scale with your business needs.
GridGain In-Memory Computing Solutions
GridGain offers enterprise architects the in-memory computing platform software, support, and professional services they need to better achieve real-time digital business goals. The GridGain in-memory computing platform easily integrates with 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 features and abilities that span a wide variety of use cases and industries.
How GridGain Helps Enterprise Architects
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, including ANSI-99 SQL and ACID transaction support, provides easy integration with existing applications and databases. GridGain can be deployed anywhere including on-premises, on a public or private cloud, or in a hybrid environment.
Learn About GridGain In-Memory Computing Solutions for Enterprise Architects
The white papers, webinars, application notes, product comparisons, and videos below discuss use case considerations from an architectural standpoint.
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.
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This white paper discusses how to accelerate Apache® Cassandra™ and improve Cassandra performance. Apache Cassandra is a popular NoSQL database that does certain things incredibly well. It can be always available, with multi-datacenter replication. It is also scalable and lets users keep their data anywhere. However, Cassandra is lacking in a few key areas – particularly speed. Because it stores data on disk, Cassandra is not fast enough for some of today’s extreme OLTP workloads.
The latest Apache Ignite release includes many new features, such as expanded thin client support, improved cluster monitoring, and enhanced cluster self-tuning. Join Vladimir as he discusses and demonstrates the key improvements that Apache Ignite 2.10 offers:
Apache Ignite and Spark are complementary in-memory computing solutions that can be used together to achieve superior performance and functionality to process SQL data.
Spring is a popular framework, and Apache Ignite is a fast layer for data storage. Adding Ignite enables you to manage http sessions, cache-application data, and so on. During this 1.5 hour session, Semyon uses the Spring framework to create a simple web application and demonstrates how Apache Ignite can empower the application. He walks you through steps such as the following:
- Adding authorization to the application
- Using Spring to store the data solution
- Configuring Apache Ignite to increase the application’s durability
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.
The GridGain Operator for Kubernetes enables you to deploy and manage Apache Ignite and GridGain clusters efficiently. The automation that Kubernetes and the Operator provide simplifies provisioning and minimizes the operational and management burden.
During this webinar, you learn the difference between in-memory clusters and persistent clusters, move step by step through the configuration, and use The GridGain Operator for Kubernetes to deploy Apache Ignite in AWS. You will see how to do the following:
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:
This product comparison describes the advantages and benefits of migrating from DataSynapse to GridGain as an in-memory computing solution to power mission-critical and data-intensive applications.
This in-depth feature comparison shows how the most current versions of GridGain Professional Edition, Enterprise Edition, Ultimate Edition and Redis Enterprise (and their respective open source projects where relevant) compare in 25 categories.
This in-depth feature comparison shows how the most current versions of GridGain Professional Edition, Enterprise Edition, Ultimate Edition and Hazelcast (and their respective open source projects where relevant) compare in 25 different categories.
This in-depth feature comparison shows how the most current versions of GridGain Professional Edition, Enterprise Edition, Ultimate Edition and Oracle Coherence (and their respective open source projects where relevant) compare in 25 different categories.
Over the last decade, the 10x growth of transaction volumes, 50x growth in data volumes, and drive for real-time response and analytics has pushed relational databases beyond their limits. Scaling an existing RDBMS vertically with hardware is expensive and limited. Moving to NoSQL requires new skills and major changes to applications. Ripping out the existing RDBMS and replacing it with another RDBMS with a lower TCO is still risky.