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.
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.
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.
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.
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.
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.
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:
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 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:
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:
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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:
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.
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 webinar hosted by GridGain Systems and 451 Research, you’ll hear about the compelling drivers for in-memory computing technologies: especially in-memory databases, data grids, and platforms.
451 Research’s data platforms analyst Jason Stamper will explain how in-memory computing helps to overcome many of the challenges faced by the modern enterprise when it comes to data processing and analytics. He will also gaze into his crystal ball to predict how in-memory computing technologies will evolve in coming years.
Topics covered include:
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.