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

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.

Bitcoin and blockchain, the digital-ledger technology behind this electronic currency, are generating enormous amounts of interest in the financial services industry. Most of the larger banks are investigating this area, and many technology companies are building platforms to enable blockchain technology for financial services firms.

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 transformations are arguably the most important initiatives for companies. They can literally make or break a business.  But transformation is not easy because there’s a big digital divide between the speed, scale and computing needed for new digital channels and APIs, and what existing systems can deliver. Learn how leading digital innovators have solved these problems by using in-memory computing, and the roadmaps that worked for them.

GridGain Cloud, which enables companies to create an in-memory SQL and key-value database in minutes, is now in Beta. Learn from the experts how to use GridGain Cloud, and get up and running. This 60-minute hands-on session will:

It's hard to improve the customer experience when your existing applications can't handle the ever-increasing customer loads, are inflexible to change and don't support the real-time analytics or machine learning needed to improve the experience at each step of the way. Join us for part 1 of the in-memory computing best practices series. Learn how companies are not only adding speed and scale without ripping out, rewriting or replacing their existing applications and databases, but also how they're setting themselves up for future projects to improve the customer experience.

The need to engage more intelligently in real-time during each transaction or interaction, whether it's to add personalization and recommend products or to help improve the overall customer experience across multiple channels, is driving the need for new infrastructure with much lower latency and much higher scalability. The solution that many companies have adopted is to move all the transactional and analytic data, and to collocate computing together in memory using In-Memory Computing technologies.

Ever-changing financial regulatory compliance policy is causing unprecedented and growing technical challenges. Banks and other financial institutions must continuously monitor, collect, and analyze vast amounts of data from multiple, disparate sources in real-time. Coping with these challenges in an efficient way requires not only an extremely fast, scalable, and cost-effective data technology, but also one that can incorporate and handle new requirements as they arise.

Data is critical to the success of financial services companies. Market data, customer data, trade data, and compliance data are retained, processed and analyzed to help firms not only stay afloat but also ahead of the competition. During this webinar, we will discuss the different types of financial data, ways financial and fintech companies process it, and show how in-memory computing is used to instantaneously analyze and make decisions based on internally and externally available data.  We will discuss:

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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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:

This presentation will show how to add speed and scale to your Oracle Database, Oracle-based applications, APIs and analytics for different use cases. It 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.

By the end of this presentation, you will understand:

This presentation covers 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 presentation 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.

Once you've put in-memory computing in place to add speed and scale to your existing applications, the next step is to innovate and improve the customer experience. Join us for part 2 of the in-memory computing best practices series. Learn how companies build new HTAP applications with in-memory computing that leverage analytics within transactions to improve business outcomes.

Learn more about the role of In-Memory Computing in supporting the real-time transactional, analytical and engagement needs for digital business and improving the customer experience.

During this webinar, we discuss how in-memory computing is being used to increase the performance and scalability of the following:

  • Network provisioning and management
  • Service delivery
  • Mobile commerce (m-commerce)
  • Fraud prevention
  • High-speed messaging
  • Customer facing self-service applications

At the end of this webinar, you will understand how GridGain’s in-memory computing platform helps telcos provide faster service at greater scale.

During this webinar, we will discuss the different types of financial data, ways financial and fintech companies process it, and show how in-memory computing is used to instantaneously analyze and make decisions based on internally and externally available data. 

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.