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.

Resources

This white paper will take a detailed look at the challenges faced by companies that have either used Redis and run into its limitations, or are considering Redis and find it is insufficient for their needs. This paper will also discuss how the GridGain in-memory computing platform has helped companies overcome the limitations of Redis for existing and new applications, and how GridGain has helped improve the customer experience.
The in-memory computing solutions of the future must not only offer the key capabilities that database users expect, such as SQL support, but also provide a bridge to emerging use cases, such as machine learning and deep learning, and transformative new storage technologies, such as non-volatile memory. This white paper delves into these application-crucial topics and then shows how Apache Ignite and GridGain are addressing them.
This white paper reviews why IMC makes sense for today’s fast-data and big-data applications, dispels common myths about IMC, and clarifies the distinctions among IMC product categories to make the process of choosing the right IMC solution for a specific use case much easier.

 

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.

Many companies who have succeeded with IoT have solved their challenges around speed, scalability and real-time analytics with in-memory computing. Across these deployments some common architectural patterns have emerged. This whitepaper explains some of the most common use cases and challenges; the common technology components, including in-memory computing technologies; and how they fit into an IoT architecture. It also explains how Apache® Ignite™ (Ignite) and GridGain® are used for IoT.

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.

This white paper covers in-depth the architecture, key capabilities and features of GridGain®, and as well as its key integrations such as leading RDBMSs, Apache Spark™, Apache Cassandra™, MongoDB® and Apache Hadoop™. You will learn how GridGain can add in-memory speed and unlimited horizontal scalability to your company’s existing or new OLTP or OLAP applications; new or existing HTAP applications; streaming analytics; and continuous learning use cases involving machine or deep learning.
This paper, written by GridGain founder and CTO, Nikita Ivanov, sums up the architecture and key capabilities of the Apache® Ignite™ project. It discusses the key features of Apache Ignite and integrations with Apache Spark and Apache Cassandra.

Node.js is a very popular and powerful JavaScript runtime environment. It is lightweight and efficient and benefits from an extensive package ecosystem. Apache® Ignite™ (Ignite) is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at scale. The benefits of Ignite are now available to Node.js developers with the addition of the Node.js Thin Client for Ignite.

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:

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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 how to deploy Ignite as a service with just a few clicks, and how to use Ignite as a distributed cache or in-memory database (IMDB) as a service. This session is hands-on, showing all the steps you need to take to get up and running, and how to develop, deploy, monitor and manage an Ignite cluster in the cloud.

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:

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

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.

Over the last few years, new business demands – from digital transformation to improving the customer experience – have overwhelmed existing SQL infrastructure. The increase in interactions through new Web and mobile apps and their underlying APIs are creating massive volumes of queries and transactions that are overloading existing databases. Improving the customer experience requires performing real-time analytics and automation during transactions and interactions, not after. Traditional data warehouses and other related tools cannot address these needs because, by design, they work on a copy of the data that is almost immediately out of data as it is extracted, transformed and loaded (ETL) from operational systems. They also don’t support the new analytical approaches, from stream processing to artificial intelligence, needed for these new initiatives. The good news is that several companies have successfully implemented these new approaches to real-time analytics with the GridGain® in-memory computing platform. Download this Application Note and learn GridGain adds speed, scalability, and in-memory computing to SQL.
Leading banks, asset management firms and fintech companies rely on the GridGain in-memory computing platform as their new foundation for real-time risk analytics, portfolio management and regulatory compliance. With GridGain, these companies have brought together many different types of information to achieve a common, real-time view of risk. They have supported the needs of trading, settlement, accounting, customer portfolio management, risk management, internal and regulatory compliance. They have achieved all of this on a common platform with in-memory speed, unlimited horizontal scalability and broad integration to support any future needs. Download this Industry Brief to learn how.
GridGain and Ignite provide the ideal underlying in-memory data management technology for Apache Spark because of its in-memory support for both stored “data at rest” and streaming “data in motion.” Learn how this makes many Spark tasks simple, including stream ingestion, data preparation and storage, stream processing, state management, streaming analytics, and machine and deep learning.
Compares GridGain and Redis features in 22 key areas: in-memory data grid functionality, caching, data querying, transactions, security and more.
Compares GridGain and Pivotal GemFire features in 25 areas: in-memory data grid functionality, caching, data querying, transactions, security and more.
Compares GridGain and Hazelcast features in 22 key areas: in-memory data grid functionality, caching, data querying, transactions, security and more.
Compares GridGain and GigaSpaces features in 22 key areas: in-memory data grid functionality, caching, data querying, transactions, security and more.
Compares GridGain and Terracotta features in 22 key areas: in-memory data grid functionality, caching, data querying, transactions, security and more.
Compares GridGain and Oracle Coherence features in 22 areas: in-memory data grid functionality, caching, data querying, transactions, security and more.

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.