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
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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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.
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).
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.
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.
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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.