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

PostgreSQL is one of the most popular open source databases. There are over thirty different distributions and products built on PostgreSQL. These products give companies many options: almost too many to choose from when growing their PostgreSQL deployments.

Download this white paper to learn about your options for adding speed, scale and agility to end-to-end IT infrastructure—from SAP HANA to third-party vendors and open source. It also explains how to evolve your architecture over time for speed and scale, become more flexible to change, and support new technologies as needed.

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.

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.

Data lakes, such as those powered by Hadoop, are an excellent choice for analytics and reporting at scale. Hadoop scales horizontally and cost-effectively and fulfills long-running operations spanning big data sets. However, the continual growth of real-time analytics requirements — where operations need to be completed in seconds rather than minutes, or milliseconds rather than seconds — has brought new challenges to Hadoop based solutions.

When you add a distributed in-memory computing cluster to support existing systems or new APIs, you introduce additional moving parts that can be hard to track and troubleshoot for performance issues or failures.

Learn how the veterans monitor various components of a distributed cluster for network, memory, or node-specific issues, and troubleshoot to resolve issues. By the end of this session you'll have a handy check-list and set of tools to consider using for your own deployments.

This session will cover:

MySQL® is arguably the most widely used open source database in the world, even if you don't include MariaDB® or Percona® as a variant. But it has a host of challenges and options to solve them.

Learn how companies have added speed and scale to MySQL deployments for different use cases. This webinar will cover the various options available and when each option makes sense. It will also cover how to evolve your architecture over time to add the speed, scale, agility and new technologies needed for digital transformation and other initiatives.

As an in-memory computing platform, GridGain® and Apache Ignite support native persistence that stores data and indexes transparently on non-volatile memory, SSD or disk. When persistence is enabled, memory becomes a cache for the most frequently used data and indexes. Native persistence is ACID-compliant, durable and enables immediate availability on a restart of each node. Data is never lost; GridGain supports full and incremental snapshots along with continuous archiving, and provides Point-in-Time recovery to an individual transaction.

PostgreSQL is one of the most widely databases globally, especially if you add up all the different distributions. This is in part what makes it challenging to figure out how to lower latency and improve scalability with business-critical PostgreSQL deployments.

Guaranteeing that your in-memory computing solution stays up and running is the most important goal for a rolling out a new production environment. The trick is making sure that you have all the bases covered and have thought through all your requirements, needs, and potential roadblocks.

In this webinar, Apache® Ignite™ PMC Chair Denis Magda shares a checklist to consider for your Apache Ignite production deployments. This checklist includes:

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.

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:

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.

Since DataSynapse was purchased by TIBCO in 2009, the distributed computing market has been transformed by the development of in-memory computing to solve a host of performance and scalability challenges. The performance required for different stress scenarios and back-testing have increased by up to 50x. Existing high-performance computing and analytics infrastructure such as DataSynapse GridServer can’t deliver both the real-time speed and 100x (or greater) scale.

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.

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?