Business Decision Makers Rely on GridGain for Digital Transformation

Enterprise business decision makers are increasingly initiating digital transformations to drive better performance and results. Transforming new or existing applications to perform in real-time and massively scale out requires in-memory computing. Deploying effective in-memory computing solutions on time and within budget requires a trusted technology partner.

Business Decision Makers
In-Memmory Computing
GridGain In-Memory Computing Solutions

GridGain offers business decision makers the in-memory computing platform software, support, and professional services they need to better achieve real-time digital transformation technical challenges. 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 users that span a wide variety of use cases and industries.

Business Decision Makers Need the GridGain In-Memory Platform

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 new or existing code, enabling the creation of modern, flexible applications built on an in-memory computing platform that grows with business needs. Thin and thick clients are available which support a wide variety of protocols including SQL, Java, C++, .NET, PHP, Scala, Groovy and Node.js.

Business Decision Makers
Learn About GridGain In-Memory Computing Solutions for Business Decision Makers

The white papers, webinars, application notes, product comparisons, and videos below can help business decision makers by discussing the business benefits of various in-memory computing use cases.

Resources

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.

Learn how high-performance in-memory computing architecture is rapidly becoming the method of choice for today’s real-time applications that are focused on big data, fast data, streaming analytics or machine and deep learning.
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 the challenges facing today’s insurance industry, the opportunities new technologies can offer, and the crucial edge that providers can gain with solutions such as the GridGain in-memory computing platform.
This white paper discusses how an in-memory computing platform solution like GridGain gives financial services companies the speed, scalability, and flexibility they need to build successful IoT-based applications and services.
This white paper discusses how in-memory computing is helping companies address increasing mobile application usage, real-time data needs, improving the customer experience, fraud prevention, compliance, and other requirements to modernize and accelerate payment solutions.
This white paper will give you a better understanding of how in-memory computing forms the backbone of successful high performance, highly scalable and mission-critical technology solutions in the FinTech industry. You will also learn how in-memory computing helps address many current limitations of legacy financial systems.

 

Businesses have a long wish list for their software solutions. They want stability, reliability, security, scalability, and speed. They can get there today with serverless architectures that rely heavily on virtualization and containerization, distributed systems, and microservice-based architectures.

To achieve competitive application performance, scalability, and analytical sophistication, many financial-services providers are turning to in-memory computing solutions. This white paper will discuss the increased expectations of investors, the new challenges providers are facing, and how providers can gain the edge they need with solutions such as the GridGain in-memory computing platform.
One way to evolve eCommerce technology is to make it as fast, available, and scalable as possible. This white paper discusses how an in-memory computing platform can accomplish this, while both providing competitive advantage and addressing the issues that eCommerce developers face.
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.

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.

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.

Managing distributed systems can be complex and time consuming because there are numerous moving parts. Having a consistent set of tools can give a clear picture of how clusters are performing, when to take action and avoid potential problems, and how to optimize configurations.

Mythili Venkatakrishnan, IBM Distinguished Engineer, will discuss increasing market pressures in key industries such as banking which are leading Z platform users to leverage Digital Integration Hubs for flexible information flow between core transactional systems and hybrid cloud environments. Digital Integration Hubs efficiently integrated with systems of record can significantly accelerate digital transformation journeys and deliver reduced complexity, high throughput, and low latency.

If your company is in an industry such as ecommerce, logistics, online learning, food delivery, or online business collaboration, you may be seeing a huge spike in your business which is straining the limits of your customer-facing or internal applications. If you need to speed up and scale out your applications, one of the fastest approaches is to deploy an in-memory data grid.

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.

This in-memory computing best practices webinar explains how companies add in-process Hybrid Transactional/Analytical Processing (HTAP) architectures for real-time data access, analytics, and decision automation to their existing applications and analytics systems.
In this webinar, you will learn how to add speed and scale to your Oracle Database, Oracle-based applications, APIs and analytics for different use cases. We will discuss when each option makes sense, as well as how to evolve your architecture over time to add the speed, scale, agility and new technologies needed for digital transformation and other initiatives.

It's hard to improve the customer experience when your existing applications can't handle the existing loads and are inflexible to change. This webinar is Part 2 in our In-Memory Computing Best Practices Series. It focuses on the most common first in-memory computing project, adding speed and scale to existing applications. 

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.
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.
Omnichannel banking needs a single, real-time view of the customer that is shared across channels. Companies use the GridGain in-memory computing platform to create infrastructure for 10x or greater digital channel loads, proactively personalize and improve the customer’s experience, and allow real-time analytics and automation. This Industry Brief tells you how.
Insurers must go beyond fulfilling the latest regulatory requirements to maximize the chances of surviving and thriving. They must also innovate for customer and risk analytics, customer experience management, and digital business. Download this Industry Brief to learn how the GridGain in-memory computing platform helps you achieve these goals.
The adoption of big data, advanced analytics, and digital government platforms has driven a 10-1000x growth in interactions and a 50x growth in data over the last decade. Learn how the GridGain in-memory computing platform can help government legacy systems keep up with the exponential growth in data, devices, users, and analytical needs.
Leading banks and fintech companies have already adopted the GridGain in-memory computing platform as the foundation for FRTB and their next generation trading systems. With GridGain, these banks have been able to rapidly implement the required XVA calculations, continuously run their new risk models and price new securities in near real-time. Learn more now.
Learn how companies such as Expedia, HomeAway, JacTravel and TUI Group rely on GridGain and Apache Ignite In-Memory Computing Platforms to deliver a real-time, personalized, seamless end-to-end experience to their customers.

If your company is one of the tens of thousands of organizations that use Apache® IgniteTM or GridGain® Community Edition in a production environment, GridGain Basic Support can provide you with peace of mind that you have a trusted partner to help keep your environment running flawlessly. The service includes....

This Machine Learning and Deep Learning primer, the second in the “Using In-Memory Computing for Continuous Machine and Deep Learning” Series, is a hands-on tutorial that covers how to use the Apache Ignite built-in machine learning algorithms Linear Regression, k-Nearest Neighbor (k-NN), k-Means Clustering, and Compute Mean Entropy.
In this eBook you'll learn the best practices for delivering new applications and APIs with in-memory computing, and how it helps open up existing systems, become more agile, and deliver unlimited speed and scale. This eBook is Part 3 of the best practices for digital transformation series.

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:

In this eBook, you’ll learn best practices for establishing a sound and cost-effective in-memory computing foundation for digital transformation. This eBook is Part 1 of the best practices for digital transformation series.

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:

This eBook, Part 3 in the In-Memory Computing for Financial Services eBook Series, discusses how financial service firms are using in-memory computing platforms such as GridGain and Apache® Ignite™ in their strategy to improve the performance of asset and wealth management, spread betting and banking applications.
This eBook, Part 2 in the In-Memory Computing for Financial Services eBook Series, discusses how financial service firms are using in-memory computing platforms such as GridGain and Apache® Ignite™ in their strategy to improve the performance of payment systems, IoT applications, and bitcoin/blockchain technology.
This eBook, Part 1 in the In-Memory Computing for Financial Services eBook Series, discusses how financial service firms are using in-memory computing platforms such as GridGain and Apache® Ignite™ to address the challenges of high-frequency trading, fraud prevention and real-time regulatory compliance.
If you are new to in-memory computing, curious to learn how in-memory computing can be used for financial applications, or seeking to educate a non-technical team member about the benefits of in-memory computing for financial applications, this eBook can help.

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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
 

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:

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:

Apache Ignite and GridGain can be used as a simple cache, an in-memory data grid (IMDG), and as an in-memory database (IMDB). These data management patterns can be combined with Ignite integration facilities to function as a Digital Integration Hub (DIH) for real-time data access across data sources and applications. Common uses for the DIH architecture include:

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:

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.

Managing distributed systems can be complex and time consuming because there are numerous moving parts. Having a consistent set of tools can give a clear picture of how clusters are performing, when to take action and avoid potential problems, and how to optimize configurations.

Mythili Venkatakrishnan, IBM Distinguished Engineer, will discuss increasing market pressures in key industries such as banking which are leading Z platform users to leverage Digital Integration Hubs for flexible information flow between core transactional systems and hybrid cloud environments. Digital Integration Hubs efficiently integrated with systems of record can significantly accelerate digital transformation journeys and deliver reduced complexity, high throughput, and low latency.

When working with multiple data centers, it is important to ensure high availability of your GridGain cluster. The GridGain Enterprise and Ultimate Editions, built on Apache Ignite®, include a Data Center Replication feature that allows data transfer between caches in distinct topologies, even located in different geographic locations.

Using code examples, we will cover the following topics:

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.