GridGain for Architects

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.

In-Memmory Computing
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.

Business Decision Makers
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.

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.
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 will take a detailed look at the challenges faced by companies that have either used Redis and run into its limitations, or are considering Redis and find it is insufficient for their needs. This paper will also discuss how the GridGain in-memory computing platform has helped companies overcome the limitations of Redis for existing and new applications, and how GridGain has helped improve the customer experience.
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 discusses how incorporating Apache Ignite into your architecture can empower dramatically faster online analytics processing (OLAP) and online transaction processing (OLTP) when augmenting your current MySQL infrastructure. Read this white paper to learn more about how Apache Ignite can eliminate the pain points of MySQL.

Spread betting offers some compelling advantages, including low entry and transaction costs, preferential tax treatment, and a diverse array of products and options. Traders can bet on any type of event for which there is a measurable outcome that might go in either of two directions – for example, housing prices, the value of a stock-market index, or the difference in the scores of two teams in a sporting event.

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.

GridGain Control Center is a comprehensive troubleshooting, management and monitoring solution for Apache Ignite and the GridGain platform. While Apache Ignite and the GridGain platform can enable building high-performance and scalability applications, troubleshooting and monitoring distributed clusters can be tricky.

Join us for a presentation by Greg Stachnick, Director of Product Management for Control Center to lear learn how Control Center simplifies key troubleshooting tasks, watch technical demos, and learn about the new Control Center SaaS subscription options.

Running Apache Ignite on Kubernetes can help Ignite developers streamline deployment and management of applications in cloud environments, both private and public. Apache Ignite is Kubernetes-friendly with features that simplify cluster provisioning and minimize the operational and management burden.

In the webinar, Colin will introduce the capabilities of the joint Ignite and Kubernetes solution and demo how set up Apache Ignite with Kubernetes quickly and reliably. Topics covered include:

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

Digital Integration Hubs (DIH) solve a key challenge enterprises face when driving toward real-time business processes in an environment where data is spread across disparate databases. The GridGain in-memory computing platform has proven to be a key component of DIH architectures. Download the application note to learn more about the Digital Integration Hub architecture with GridGain and read about real-world deployment use-cases.
Communications and media companies have the opportunity to transform into more modern, digital providers to help drive renewed growth from new OTT services over IP, as well as from services for security and the Internet of Things (IoT). Download this industry brief to discover why telecommunications companies turn to in-memory computing for digital transformation and OTT services.
By 2020, Gartner expects the Internet of Things (IoT) to have over 20 billion connected things. Many companies have succeeded with IoT using GridGain and Apache Ignite to solve their challenges around speed, scalability, and real-time analytics. Download this application note to learn more.
FinTech companies face many of the same challenges as their largest customers. Their new channels and services, as well as core banking, insurance, and real estate systems, must deliver 100-1000x speed and scale compared to existing systems. Download this Industry Brief and learn how the GridGain In-Memory Computing Platform can address these issues and more.
Download this Application Note and learn how GridGain fosters digital transformation and improves the customer experience by adding speed, scalability, and in-memory computing to SQL architectures overwhelmed by the huge increase in data and the need to implement new business demands.
Leading banks, asset management firms, and fintech companies rely on the GridGain in-memory computing platform as a foundation for real-time risk analytics, portfolio management, and regulatory compliance. These companies use Gridgain to achieve a common, real-time view of risk by bringing together many types of information. Download this Industry Brief to learn how.
GridGain and Ignite provide an ideal underlying in-memory data management technology for Apache Spark because it supports both in-memory “data at rest” and “data in motion.” Learn how this simplifies many Spark tasks like stream ingestion, data preparation and storage, stream processing, state management, streaming analytics, and machine and deep learning.

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.

Compares GridGain and Pivotal GemFire features in 25 areas: in-memory data grid functionality, caching, data querying, transactions, security and more.

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.

Compares GridGain and GigaSpaces features in 22 key areas: in-memory data grid functionality, caching, data querying, transactions, security and more.
Compares GridGain and Terracotta features in 22 key areas: in-memory data grid functionality, caching, data querying, transactions, security and more.

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.

In this video from the Bay Area In-Memory Computing Meetup on Wednesday, July 17, 2019, GridGain's Director of Product Management Greg Stachnick, discusses some of the in-memory computing cloud deployment best practices for in-memory data grid (IMDG) and in-memory database (IMDB) in the cloud. 
This IMCS Europe 2019 talk discusses the various components of Apache Ignite and GridGain, including memory storage, networking layer, compute grid, to help explain in-memory computing best practices for DevOps, high availability, proper testing, fault tolerance, and more.
This IMCS Europe 2019 video discusses some best practices for monitoring distributed in-memory computing systems, including how to monitor applications, cluster logs, cluster metrics, operating systems, and networks. It provides guidance on tools like Elasticsearch, Grafana, and GridGain Web Console.
This IMCS Europe 2019 talk discusses migrating an in-memory computing platform to the cloud. It covers best practices, special considerations, tools, and differences between public and private clouds.
This IMCS Europe 2019 keynote is a panel discussion of current and emerging trends in in-memory computing for enterprises looking to enable digital transformation.
This talk demonstrates how to implement integrating Apache Kafka with Apache Ignite in practice, explains the architectural reasoning and the benefits of real-time integration, and shares common usage patterns. The presenters build a streaming data pipeline using nothing but their bare hands, Apache Ignite, Kafka Connect, and KSQL.
GridGain Meetups provide the in-memory computing community with a venue to discuss in-memory computing issues, solutions, and examples. Our summertime-themed edition Meetup on June 26, 2019, featured three talks on analytics from GridGain, Confluent, Oracle, and Alluxio.
GridGain Meetups provide the in-memory computing community with a venue to discuss in-memory computing issues, solutions, and examples. Our summertime-themed edition Meetup on June 26, 2019, featured three talks on analytics from GridGain, Confluent, Oracle, and Alluxio.
In this IMCS Europe 2019 session, Denis Magda describes how Apache Ignite and GridGain as an in-memory computing platform can modernize existing data lake architectures, enabling real-time analytics that spans operational, historical, and streaming data sets.

/*-->*/ 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.