GridGain Can Accelerate and Scale Out Your Existing or New Applications
GridGain provides in-memory speed and massive scalability to new or existing applications which can provide the performance needed for digital transformation and omnichannel customer experience initiatives. Built on the Apache Ignite open source project, GridGain is a cost effective solution which can be easily integrated into your existing architectures and infrastructure.
The GridGain in-memory computing platform easily integrates with your systems, deployed as an in-memory computing layer between the application and data layer of your new or existing applications. GridGain can be deployed on-premises, on a public or private cloud, or on a hybrid environment.
GridGain is usually deployed as an in-memory data grid for existing applications and as an in-memory data grid or as an in-memory database for new applications. A Unified API provides easy integration with your existing code with support for SQL, Java, C++, .NET, Scala, Groovy, and Node.js, enabling you to create modern, flexible applications built on an in-memory computing platform which will grow with your business needs. GridGain includes ANSI-99 SQL and ACID transaction support.
A variety of resources including white papers, webinar recordings, application notes, product comparisons, and videos are listed below which discuss use case considerations from a technology standpoint.
The shift to digital payments is taking place in many forms: bitcoins, mobile wallets, “tap and go” payment transactions, peer-to-peer money-transfer apps and more. Worldwide, the mobile payments market alone has grown from $235 billion in 2013 to a projected value of almost $800 billion in 2017 and over a trillion dollars by 2019.
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.
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.
In this presentation, attendees will learn about Apache Ignite and the GridGain in-memory computing platform, which is built on Apache Ignite, and about the key capabilities and features important for financial applications, including ACID compliance, SQL compatibility, persistence, replication, security, fault tolerance, fraud detection and more.
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:
In this webinar, Akmal Chaudhri, Technology Product Evangelist for GridGain and Apache Ignite, will introduce the fundamental capabilities and components of a distributed, in-memory computing platform. With increasingly advanced coding examples, architects and developers will learn about:
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. This is how many retail innovators like Amazon, Expedia/HomeAway or SaaS innovators like Workday have succeeded. This webinar will explain with examples on how to:
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.
Apache Ignite Release 2.4 added built-in machine learning (ML) and deep learning (DL). It not only eliminates any delays caused by transferring data to a different database or store. It delivers near real-time performance by running a variety of ML and DL algorithms in place, in memory, that are optimized for collocated processing.
Learn more about these new capabilities and how to use them in Apache Ignite 2.4. This webinar will provide:
Learn how Apache Ignite™ simplifies development and improves performance for Apache Spark™. This session will explain how Apache Spark and Ignite are integrated, and how they are used to together for analytics, stream processing and machine learning. By the end of this session you will understand:
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With the tight regulatory environment, competition from traditional and non-traditional industries, customer demands, and cost pressures that companies are facing today, e-commerce initiatives require big data technologies that make processes and transactions much faster and more efficient. Large companies accumulating massive amounts of data need to be able to perform analytics on that data in real time in a cost-conscious manner to ensure a good user experience.
With the tight regulatory environment and cost pressures that financial services companies are facing today, they need big data technologies that make their risk management, monitoring, and compliance processes much faster and more efficient. Large financial institutions accumulating massive amounts of data need to be able to perform analytics on that data in real time in a cost-conscious manner to ensure a good user experience.