Learn How to Program for GridGain Distributed In-Memory Computing

GridGain provides in-memory speed and massive scalability to new or existing applications.

The GridGain in-memory computing platform can be integrated with your architectures, deployed as an in-memory computing layer between the application and data layer of your new or existing applications. GridGain is commonly deployed as an in-memory data grid in existing applications. For new applications, GridGain is deployed as either an in-memory data grid on top of your data layer or as an in-memory database which functions as your data layer.

A Unified API, including ANSI-99 SQL and ACID transaction support, provides easy integration with your new or existing code, enabling you to create modern, flexible applications built on an in-memory computing platform which will grow with your business needs. Thin and thick clients are available which support a wide variety of protocols including SQL, Java, C++, .NET, PHP, Scala, Groovy and Node.js.

A variety of resources including white papers, webinar recordings, application notes, product comparisons, and videos are listed below which discuss use case considerations from a development 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.

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.

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.
Combining Apache Ignite with a Relational DBMS can offer enterprises the best of both open-source worlds: a highly-scalable high-velocity grid-based in-memory SQL database, with a robust fully-featured SQL persistent datastore for advanced analytics and data-warehouse capabilities. Learn how
Learn some of the best practices companies have used for making Apache Ignite and Apache Kafka scale. Making stream processing scale requires making all the components—including messaging, processing, storage—scale together.   During this 1-hour webinar by GridGain Systems Professional Services Consultant Alexey Kukushkin, you will learn about:

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.

Lors de ce Webinar en Anglais, Akmal Chaudhri, Promoteur des Produits GridGain System et Apache® Ignite™, présentera les capacités et les composantes fondamentales de la plateforme d’In-Memory Computing avec Apache Ignite et vous expliquera comment passer de la théorie à la pratique. Grâce à des exemples de plus en plus codés, les architectes et développeurs apprendront :Le traitement colocalisé, Le traitement colocalisé pour l’informatique distribuée,Le traitement colocalisé pour les SQL (raccords distribués et autres), L’usage distribué permanent.  
Lors de ce webinar, Akmal Chaudhri, Promoteur de Produit pour GridGain Systems, présentera les capacités et les composantes fondamentales de la plateforme d’In-Memory Computing avec Apache Ignite et vous expliquera comment passer de la théorie à la pratique.
Денис Магда, Apache Ignite PMC Chair и директор по продукту в GridGain, расскажет об основных возможностях и компонентах In-Memory Computing решений на примере Apache Ignite. Вебинар совмещает теорию и практику, после него участники смогут проектировать и писать код под подобные системы.

Apache Ignite native persistence is a distributed ACID and SQL-compliant store that turns Apache Ignite into a full-fledged distributed SQL database. It allows you to have 0-100% of your data in RAM with guaranteed durability using a broad range of storage technologies, have immediate availability on restart, and achieve high volume read and write scalability with low latency using SQL and ACID transactions.

It used to be that the only way to improve application performance was to add a cache. But caches like Redis don't understand SQL. They require you to modify your applications with non-SQL coding and data models, and copy and synch data across two different models. They don't support ACID transactions very well. And they have their limits when it comes to scalability.

Learn some of the best practices companies have used to increase performance of existing or new SQL-based applications up to 1,000x, scale to millions of transactions per second and handle petabytes of data by adding Apache® Ignite™.

Distributed platforms like Apache® Ignite™ rely on a horizontal “scale-out” architecture where you dynamically add more machines to achieve near-linear, elastic scalability. But how does it really work? What are its limits? And how can you optimize performance and scalability?

In this webinar, we will cover the challenges engineers face when designing distributed systems, and the tips and tricks for optimizing Apache Ignite including:

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This data sheet provides the key features and benefits of the GridGain in-memory computing platform.
This data sheet provides the key features and benefits of the GridGain In-Memory Accelerator for Hadoop and Spark.

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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.

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With real-time streaming analytics there is no room for staging or disk.  Learn the best practices used for real-time stream ingestion, processing and analytics using Apache® Ignite™, GridGain®, Kafka™, Spark™ and other technologies. 

Apache Ignite is (an in-memory computing platform OR an in-memory distributed data store and compute grid) with full-fledged SQL, key-value and processing APIs. Many companies have added it as a cache in-between existing SQL databases and their applications to speed up response times and scale. In other projects they've used it as its own SQL database. 

This session will dive into some of the best practices for both types of projects using Apache Ignite. 

Machine learning is a method of data analysis that automates the building of analytical models. By using algorithms that iteratively learn from data, computers are able to find hidden insights without the help of explicit programming. These insights bring tremendous benefits into many different domains. For business users, in particular, these insights help organizations improve customer experience, become more competitive, and respond much faster to opportunities or threats.

If you are trusting a single datacenter to support your newest mission critical or cutting edge in-memory computing application, you may want to reconsider your strategy. No datacenter is 100% secure against natural disasters, hackers or just plain old human error.  In order to maintain all the 9s of availability that you have promised, you need to hedge your bets on an active - active or active - passive set up. The GridGain Multi-Datacenter Replication feature makes doing this a snap.

In this webinar, GridGain System’s Solution Architect Dani Traphagen will walk through the basics of a Kubernetes and Apache Ignite deployment, including:

In this session, Valentin Kulichenko, GridGain System’s Lead Architect, will give an overview of Apache® Ignite™ and GridGain capabilities that allow the delivery as much availability as possible, while not breaking data consistency. Valentin will give specific guidelines on how to build such systems, and will do a deep dive into topics like:

  • In-memory backups
  • Data persistence
  • Data center replication
  • Full and incremental snapshots

In this webinar, GridGain Systems Chief Product Officer and Co-Founder Dmitriy Setrakyan will cover the key new features in GridGain and Apache Ignite, including one-of-a-kind support for distributed, transactional, ACID-compliant disk persistence coupled with full SQL compatibility. These features enable organizations with large, mission-critical datasets to achieve in-memory performance with the durability of disk across thousands of servers.

Dmitriy will also dive into some important new concepts introduced in GridGain 8.1 and Apache Ignite including:

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.