Leverage GridGain to Accelerate and Scale Out Your Current or Future Architectures
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.
The GridGain in-memory computing platform easily integrates with your architectures, deployed as an in-memory computing layer between the application and data layer of your new or existing applications. GridGain is typically deployed as an in-memory data grid for existing applications and as either an in-memory data grid or as an in-memory database for new applications. A Unified API, including ANSI-99 SQL and ACID transaction support, provides easy integration with your existing code, enabling you to create modern, flexible applications built on an in-memory computing platform which will grow with your business needs.
A variety of resources including white papers, webinar recordings, application notes, product comparisons, and videos are listed below which discuss use case considerations from an architectural standpoint.
Applications and their underlying RDBMSs have been pushed beyond their architectural limits by new business needs, and new software layers. Companies have to add speed, scale, agility and new capabilities to support digital transformation and other business critical initiatives.
MySQL® is a widely used, open source relational database management system (RDBMS) which is an excellent solution for many applications, including web-scale applications. However, its architecture has limitations when it comes to big data analytics.
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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. Also, Cassandra shares certain limitations of other NoSQL databases, such as limited querying capabilities.
Fortunately, there is a simple way to make Cassandra much faster and more flexible.
Digital transformation, whether it’s done to improve the customer experience or operations, is the biggest opportunity and threat for most companies. But transforming existing IT infrastructure to support digital business is hard. Digital business can increase query and transaction volumes up 10 to 1000x, and generate 50x or more data about customers, products, and interactions. It also requires companies to act in real-time.
With most machine learning (ML) and deep learning (DL) frameworks, it can take hours to move data, and hours to train models. Learn how Apache Ignite eliminates runs model training and execution in near-real-time and makes continuous learning possible.
In this Webinar, Yuri Babak, the head of ML/DL framework development at GridGain and major contributor to Apache Ignite, will explain how ML and DL work with Apache Ignite, and how to get started. Topics include:
The Oracle® Database is one of the most scalable RDBMSs on the market. But even Oracle has been pushed beyond its architectural limits by new business needs and software layers. The reason is simple: the performance issues cannot be solved by making changes to the database.
In this webinar, using examples, we will cover the specifics of how to use Node.js with Ignite, including:
Whether you are getting started with Apache® Ignite™ or already deployed, this session is for you. Learn the best practices that the GridGain® Customer Solutions team has used to troubleshoot hundreds of deployments. We will share with you how to set up deployments to make them easier to monitor, manage and keep up and running properly. In this session, you will see best practice examples on how to:
Whether you are getting started with Apache Ignite or already deployed, this session is for you. Learn the best practices that the GridGain® Customer Solutions team has used to troubleshoot hundreds of deployments. We will share with you how to set up deployments to make them easier to monitor, manage and keep up and running properly. In this session, you will see best practice examples on how to:
Learn what's new with Apache Ignite 2.7. This session, given by Akmal Chaudhri, GridGain Evangelist for Apache Ignite, is for all Apache Ignite users. You will learn how the new capabilities of Apache Ignite work. You will also understand more about some of the other changes made to Apache Ignite, and the reasoning behind them. Come with your questions, and learn from the questions of your peers. Topics covered include:
Many modern applications provide full text search as a core feature of the overall product.
There are good open source and commercial options available already, but what if all your data is in Apache Ignite? Do you really need to pull in another system to solve this problem?
The short answer is no. Ignite has built in support for fulltext indexing of content. If you use the SQL APIs there is an off the shelf option which just works and suits many use cases. If you use the key value APIs this is less true.
Digital transformation is arguably the most important initiative in IT today, in large part because of its ability to improve the customer experience and business operations, and to make a business more agile.
But delivering a responsive digital business is not possible at scale without in-memory computing. This session, the third in the In-Memory Computing Best Practices Series, dives into how in-memory computing acts as a foundation for digital business. Topics include how in-memory computing is used to:
Learn what's new with Apache Ignite 2.7. This session, given by Denis Magda, Apache Ignite PMC Chair, is for all Apache Ignite users. You will learn how the new capabilities of Apache Ignite work. You will also understand more about some of the other changes made to Apache Ignite, and the reasoning behind them. Come with your questions, and learn from the questions of your peers. Topics covered include:
The opportunity for communications and media companies is to transform into more modern, digital providers. This will help drive renewed growth from new OTT services over IP, as well as from services for security and the Internet of Things (IoT).
By 2020, Gartner expects the Internet of Things (IoT) to have over 20 billion connected things, a conservative estimate compared to other analysts. The information generated by connected devices requires an enormous amount of real-time processing and storage.
To realize the benefits of IoT, you need to choose the right architecture and set of technologies that can process large data streams, identify important events and react in real-time.
FinTech companies are faced by many of the same challenges as their largest customers with speed, scale and innovation. 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. They must adopt new technologies from streaming analytics to machine and deep learning to implement real-time analytics and decision automation. The latest regulations also require 100-1000x more computations than before.
Valentin Kulichenko, lead architect at GridGain Systems, spoke June 26 at the In-Memory Computing Summit Europe 2018 in London. His talk at the In-Memory Computing Summit Europe 2018, June 25-26 in London, was titled: "Want Extreme Performance at Scale? Do Distributed the RIGHT Way!" It is well-known that distributed systems rely on horizontal scalability. The more machines in your cluster, the better performance of your application. Well, not always.
Presented by Dmitriy Setrakyan of GridGain at the Bay Area Hadoop Meetup in Sunnyvale, CA on August 17, 2016.
Apache Spark™ and Apache® Ignite™ are two of the most popular open source projects in high-performance Big Data and Fast Data. But did you know that one of the best ways to boost performance for your next-generation, real-time applications is to use them together?