Learn About In-Memory Computing
GridGain Systems Solution Architects Christos Erotocritou and Rachel Pedreschi have helped numerous customers get started with Apache® Ignite™ and GridGain. During this 1-hour webinar, they will share answers to the most common questions asked prior to deployment. They will also provide guidance that will save you time and make deploying GridGain or Apache Ignite a more enjoyable experience. Questions they will address include:
If your eCommerce initiatives are struggling to keep up with customer demand, then in-memory computing may be the solution you need to take performance and scale to the next level. Yesterday’s application and data architectures cannot achieve the speed and scale offered by in-memory computing. In addition, in-memory computing is more efficient than disk-based systems and can frequently be used to drive infrastructure consolidation projects and decrease costs.
Technology is changing traditional retail banking business models. Online banking and mobile banking are generating significantly more transactions than live teller-based banking. This increase in automation is challenging banks to redesign their legacy systems to accommodate the increased quantity and complexity of transactions and analytics. Such changes require a fast, scalable, distributed and secure architecture.
The Apache® Ignite™ in-memory computing platform enables you to dramatically accelerate and scale out your existing data-intensive applications without ripping and replacing your existing databases. You insert Apache Ignite between your application and database layers to significantly accelerate your existing solution’s overall performance. The Apache Ignite Web Console, an interactive management and configuration tool for Apache Ignite, provides an automated way to accomplish the following:
Software and Software-as-a-Service (SaaS) developers face a rapidly escalating set of challenges as they strive to make applications faster and more scalable. Software developers must meet the performance and scalability requirements dictated by unprecedented numbers of internal and external users. The challenges faced by SaaS developers are even more dramatic as they create high-performance, hyper-scale services that provide quick response times even under a growing volume of users and data. Software and SaaS are being revolutionized by in-memory computing.
Asset and wealth management continues to be one of the most attractive segments of the financial services industry, promising profitability and growth in a world of financial uncertainty. However, most financial institutions are observing a shift in their client demographics. The new client base demands different ways of placing and managing their financial holdings. As a result, technology is the single most important driving force in maintaining a competitive edge in this highly regulated industry.
Bitcoin and blockchain technology has driven a lot of discussions around its possible use within the banking and financial services industries. According to analysts, 80% of top banks have launched experimental blockchain projects. While an estimated 15% of these banks will likely launch new blockchain-based services into the mass market in the next 12-24 months, an enormous investment in new technology may be required.
Nikita Ivanov, Peter Zaitsev
MySQL® is an extremely popular and widely used RDBMS. Apache® Ignite™ is the leading open source in-memory computing platform which can provide speed and scale to RDBMS-based applications. Apache Ignite is inserted between existing application and data layers and works with all common RDBMS, NoSQL and Hadoop® databases. Join Nikita Ivanov, GridGain CTO and Co-Founder, and Peter Zaitsev, CEO and Co-Founder of Percona, as they discuss how you can supplement MySQL with Apache Ignite. You'll learn:
Distributed in-memory computing systems such as Apache® Ignite™ improve performance and scalability of user applications. However, when dealing with distributed systems, proper deployment and tuning are very important. In this webinar, we will go over several deployment anti-patterns and demonstrate various optimization techniques and features available in Apache Ignite and GridGain to facilitate most distributed deployments. We will cover:
In-memory computing solutions boost performance due to their key-value based architecture, however, flexible SQL support is required for both application and business needs. Many in-memory data grid (IMDG) vendors address the need for SQL support by inventing their own query engines or by providing limited SQL support, but neither approach meets real world needs.
Financial spread betting has been one of the major global growth markets since the financial crisis of 2008. There are several reasons for this, including preferential tax treatment, low entry and transaction costs, wider bid-offer spreads, less regulation, and a diverse universe of products and options.
Fraud is a growing multi-billion dollar business. Detecting fraud requires complex data processing, modeling and analysis performed in real-time. Financial services institutions are committed to protecting their customers from fraud, protecting themselves from losses due to financial crime and complying with a myriad of local and global regulations. To accomplish their fraud prevention goals, financial services companies need flexible, scalable, reliable and extremely fast solutions that provide controls to detect and stop fraud. This webinar will examine:
In-memory computing has transformed so many times over the past twenty-five years, it is easy to get confused. Is it database caching or a data grid? Is it streaming or acceleration? What about in-memory databases, computing platforms and data fabrics? This webinar will discuss why there are so many different types of in-memory computing solutions, where they came from, what they are, and when to use them. We’ll discuss:
Rachel Pedreschi, Igor Rudyak
Apache® Cassandra™ is a popular NoSQL database which promises an “always available” and scalable architecture that appeals to many distributed system developers. But Cassandra is not fast enough for some of today’s extreme OLTP workloads. It pulls data from disk and does not support in-memory workloads so it may not meet tight SLAs or solve extreme performance needs. However, the GridGain In-Memory Data Fabric, the production ready version of Apache® Ignite™, may be able to help.
Apache Spark™ and Apache Ignite™ are two powerful solutions for high-performance Big Data and Fast Data. Using Spark and Ignite together is an easy way to boost performance by orders of magnitude for your next generation real-time applications. With Spark plus Ignite, you can share state across Spark jobs, applications, and workers and your Spark queries will also run much faster.
High frequency trading is becoming much more intense as financial services companies compete on latency, performance and analytical complexity. High frequency, algorithmic and quantitative trading is becoming the norm. At the same time, transactional-level compliance and risk management controls must be in place. Financial services firms now face unprecedented and growing technical challenges as a result of these requirements.
Traditional data warehouse architectures and techniques are quickly becoming obsolete due to hardware and software technology innovations. In addition, users are much more demanding and sophisticated, requiring access to both transactional and analytical data in real-time. Like many data-driven organizations, your company may be re-architecting its data warehouse to meet these needs and provide multiple 9’s of availability.
During this webinar, Dmitry Setrakyan will provide a deep dive into GridGain and Apache Ignite support for .NET. He will describe how these solutions can allow you to decrease data query times by 1,000x and massively scale out your .NET applications with in-memory computing. Dmitriy Setrakyan is the Co-Founder & Chief Product Officer for GridGain as well as the Chairman of the Apache Ignite Project Management Committee. Topics covered during this webinar will include:
Regulatory tightening is required by Dodd Frank, Volker, Best Execution, Basel, MiFID, and CCAR. AML, KYC and fraud detection demand real-time compliance. Financial services organizations face unprecedented and growing technical challenges. Validating financial transactions is necessary to satisfy demanding regulatory and client protection requirements. It requires monitoring, collecting and analyzing real-time data from multiple, disparate sources.
In-memory computing addresses the toughest Big Data challenges in the financial services industry. Join GridGain’s Eric Karpman, a 30-year veteran of the financial services industry, as he shares how some of the world’s largest financial institutions use the power of in-memory computing for: