Learn About In-Memory Computing
In this webinar, Akmal Chaudhri, Technology Product Evangelist for GridGain and Apache Ignite, will introduce the fundamental capabilities and components of a distributed, in-memory computing platform. With increasingly advanced coding examples, architects and developers will learn about:
Once your Apache® Ignite™ or GridGain® cluster goes in production, you will need to keep an eye on its state. You will also have to manage your deployment throughout its lifetime. These tasks might seem challenging, but managing and monitoring distributed systems is not cumbersome if you have a right toolkit. In 1-hour webinar, Andrey Evsukov, Head of Operations at GridGain Systems, will:
Learn the best practices used for real-time stream ingestion, processing and analytics using Apache® Ignite™, GridGain®, Kafka™, Spark™ and other technologies. Join GridGain System’s Director of Product Management and Apache Ignite PMC Chair Denis Magda for this 1-hour webinar
This 1-hour webinar presented by GridGain System’s Sr. Technical Consultant Lucas Beeler will discuss architectural best practices many companies have used in their journey to multi-cluster computing applications.
Akmal B. Chaudhri, Rob Meyer
GridGain Cloud, which enables companies to create an in-memory SQL and key-value database in minutes, is now in Beta. Learn from the experts how to use GridGain Cloud, and get up and running. This 60-minute hands-on session will:
Best Practices for Deploying Distributed Databases and In-Memory Computing Platforms with Kubernetes
In-memory computing technologies such as in-memory data grids (IMDG) and databases (IMDB), NoSQL and NewSQL databases can make so many things easier for a developer. But implementing DevOps for these distributed technologies and the related storage can be difficult. Luckily Kubernetes has come to the rescue! Learn how Kubernetes can orchestrate a distributed database or in-memory computing solutions using Apache Ignite as an example. This session will explain how to:
Akmal B. Chaudhri
In this webinar, Akmal Chaudhri, GridGainTechnical Evangelist, will introduce the fundamental capabilities and components of an in-memory computing platform with a focus on Apache Ignite, and demonstrate how to apply the theory in practice. With increasingly advanced coding examples, architects and developers will learn about: Cluster configuration and deployment Data processing with key-value APIs Data processing with SQL This is Part 1 of a 2-part webinar series designed for software developers and architects.
Ivan Rakov, Rob Meyer
When deployed properly, it's hard to beat a horizontal distributed architecture's scalability, availability and reliability. The trick is deploying it properly to compensate for individual node, data or network failures. Learn some of the best practices for setting up your clusters properly to maximize availability, reliability and flexibility for future needs.
Once you've put in-memory computing in place to add speed and scale to your existing applications, the next step is to innovate and improve the customer experience. Join us for part 2 of the in-memory computing best practices series. Learn how companies build new HTAP applications with in-memory computing that leverage analytics within transactions to improve business outcomes. This is how many retail innovators like Amazon, Expedia/HomeAway or SaaS innovators like Workday have succeeded. This webinar will explain with examples on how to:
It's hard to improve the customer experience when your existing applications can't handle the ever-increasing customer loads, are inflexible to change and don't support the real-time analytics or machine learning needed to improve the experience at each step of the way. Join us for part 1 of the in-memory computing best practices series. Learn how companies are not only adding speed and scale without ripping out, rewriting or replacing their existing applications and databases, but also how they're setting themselves up for future projects to improve the customer experience.
Akmal B. Chaudhri
Apache Ignite Release 2.4 added built-in machine learning (ML) and deep learning (DL). It not only eliminates any delays caused by transferring data to a different database or store. It delivers near real-time performance by running a variety of ML and DL algorithms in place, in memory, that are optimized for collocated processing. Learn more about these new capabilities and how to use them in Apache Ignite 2.4. This webinar will provide:
Akmal B. Chaudhri
Learn how Apache Ignite™ simplifies development and improves performance for Apache Spark™. This session will explain how Apache Spark and Ignite are integrated, and how they are used to together for analytics, stream processing and machine learning. By the end of this session you will understand:
Matt Aslett, Rob Meyer
The need to engage more intelligently in real-time during each transaction or interaction, whether it's to add personalization and recommend products or to help improve the overall customer experience across multiple channels, is driving the need for new infrastructure with much lower latency and much higher scalability. The solution that many companies have adopted is to move all the transactional and analytic data, and to collocate computing together in memory using In-Memory Computing technologies.
The 10x growth of transaction volumes, 50x growth in data volumes and drive for real-time visibility and responsiveness over the last decade have pushed traditional technologies including databases beyond their limits. Your choices are either 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).
The need for real-time computing has resulted in the growth of many different in-memory computing (IMC) technologies. This includes caches, in-memory data grids, in-memory databases, streaming technologies and broader IMC platforms. But what are the best technologies for each type of project? Learn about your options from one of the leading IMC veterans. This webinar will explain the evolution of IMC, the different types of technologies available today, and when to use them, including:
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.
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.