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.
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.
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.
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.
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.
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.
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?