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.
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.
Spread betting offers some compelling advantages, including low entry and transaction costs, preferential tax treatment, and a diverse array of products and options. Traders can bet on any type of event for which there is a measurable outcome that might go in either of two directions – for example, housing prices, the value of a stock-market index, or the difference in the scores of two teams in a sporting event.
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.
Вебинар завершился, но вы можете заполнить форму и получить доступ к видео и слайдам.
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.
In this webinar Alexey Zinoviev, Apache Ignite ML contributor for GridGain will talk about new 2.7 release of Apache Ignite and present the new features that are added to Ignite ML modules.
In the second phase of his presentation he will introduce what a Java programmer needs to do and understand in a typical Big Data and ML projects.
In this webinar you will learn:
- How to choose features
- How to encode features
- How to scale
- How to clear and fill in the missed values
- How to evaluate the quality of the model
Regardless of how mature a data storage technology is, backing up data is a laborious and difficult task that can cost us time, increase our stress levels and jeopardise our jobs.
The 10x growth of transaction volumes, 50x growth in data volumes -- along with the drive for real-time visibility and responsiveness over the last decade -- have pushed traditional technologies including databases beyond their limits. Your choices are either to buy expensive hardware to accelerate the wrong architecture, or do what other companies have started to do and invest in technologies being used for modern hybrid transactional/analytical processing (HTAP).
Вебинар прошел, но вы можете получить доступ к записи и всем материалам, заполнив регистрационную форму.
Вебинар посвящен особенностям распределенных систем, связанных с шардированием как методом обеспечения горизонтального масштабирования. В рамках вебинара Артем Шитов, GridGain Solutions Engineer, расскажет про:
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.
Once your Apache® Ignite™ or GridGain® cluster goes in production, you will need to keep an eye on its state. You will also have to manage your deployment throughout its lifetime. These tasks might seem challenging, but managing and monitoring distributed systems is not cumbersome if you have a right toolkit.
In 1-hour webinar, Andrey Evsukov, Head of Operations at GridGain Systems, will:
Apache Spark is a leading open source fast and general-purpose engine for large-scale data processing of streaming data. Part of its stellar rise has been its adoption as the de-facto processing engine for Apache Hadoop™. But while Spark supplanted MapReduce for processing, no solution for real-time data management has emerged. Spark doesn’t have features for managing data or processing state. As a result, developers using Spark often write extensive code to ingest, prepare and enrich, store and manage data and state.
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.