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.

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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.

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:

Join this webinar to get started with an Apache Ignite as a Digital Integration Hub for real-time data access across data sources and applications.
This webinar will introduce you to the role of the networking layer in distributed systems with Apache Ignite as an example. You will gain practical insights and learn how to maximize the performance and reliability of your applications running on distributed systems.

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.

Apache Ignite is an excellent tool for external RDBMS, NoSQL or Hadoop database acceleration and offloading. The Ignite in-memory computing platform can power real-time applications that need to process terabytes of data with in-memory speed.

Join us for this webinar to learn about the various Ignite deployment options for database acceleration including:

Join us for a special webinar presented by Branimir Angelov, Co-Founder and CTO of Kubo, Software Architecture Consultant in Obecto, and Member of the Comrade Cooperative.

Many machine learning (ML) and deep learning (DL) platforms are slow in production environments. It can sometimes take hours or days to update ML models. This is a result of having the ML processing run on a different system from the operational transactions system in order to avoid a performance degradation.

In this webinar, Glenn Wiebe, GridGain Solution Architect, will introduce developers to Apache Ignite as an in-memory database (IMDB). He will review how an IMDB differs from a cache or an in-memory data grid (IMDG). He will discuss the key characteristics of an IMDB and also highlight core Ignite features and facilities. Finally, Glenn will demonstrate a repeatable and extensible process to:

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.