This short video explains why companies use Hazelcast for business-critical applications based on ultra-fast in-memory and/or stream processing technologies.
Stream processing is a hot topic right now, especially for any organization looking to provide insights faster. But what does it mean for users of Java applications, microservices, and in-memory computing?
In this webinar, we will cover the evolution of stream processing and in-memory related to big data technologies and why it is the logical next step for in-memory processing projects.
Now, deploying Hazelcast-powered applications in a cloud-native way becomes even easier with the introduction of Hazelcast Cloud Enterprise, a fully-managed service built on the Enterprise edition of Hazelcast IMDG. Can't attend the live times? You should still register! We'll be sending out the recording after the webinar to all registrants.
The European Union “Revised Payment Service Directive” (PSD2) will bring banking and financial institutions closer to enable bank customers, both consumers and businesses, to use third-party providers to manage their finances. Third-parties will now have access to bank data and customer data to create value-added services such as secured and unsecured loans, mortgages, etc. Welcome to the age of Open Banking; it will touch our day-to-day lives by providing services tailored to individual needs in purchasing, financial services, travel, health insurance, financial technology, and investing.
All of these opportunities will require:
Open Banking requires a different level of speed due to more complex back-end integration requirements across a broader range of services. This is where the speed of in-memory can ensure you thrive.
Open Banking exposes what had been a closed system to a broad array of service providers, all of whom require not just speed, but speed on a much larger and more complex scale. Hazelcast’s distributed processing architecture can help you stay one step ahead of a fast-moving demand curve.
The challenge with Open Banking is that if something fails on the back-end, it doesn’t just affect your bank, it impacts your entire payment ecosystem. A cloud-based distributed architecture provides instant redundancy, so system failures become a low probability event.
A groundswell of new companies accessing your back-end systems means more opportunity for fraud. Hazelcast IMDG enables fraud detection algorithms that easily exceed even the most stringent SLAs. This specific use case is one of our core competencies.
The Hazelcast in-memory computing platform delivers the necessary ingredients for success such as performance under higher workload, scalability both horizontally and vertically, support for analytic and transactional processing, cross-application programming language support and more. The platform natively integrates both Hazelcast IMDG (data storage and compute) and Hazelcast Jet (event stream processing) to present a platform for delivering elastically scalable Open APIs with ease.
Aggregations per second
Across multiple networks and transaction types
Security in Complex Environments
10K TPS w/SSL/TLS 1.2 w/Open SSL
Consistent security across a broad and dynamic payment ecosystem
Open Banking introduces substantial risk as well as opportunity across your partner and customer ecosystem. In-memory technology gives you the adaptability and speed needed to exceed performance and security SLAs under the most demanding conditions.
Meet burst requirements
Burst requirements in Open Banking are going to have much broader data sources and more integration complexity, while expectations will continue to climb. This is an ideal scenario for the speed, stability, and security enabled by distributed in-memory computing.
Hazelcast is delivering industry-leading performance at scale for some of the most demanding banks and payment processors in the world, while driving core enablement capabilities against a broad, complex, and demanding ecosystem.
The Hazelcast in-memory computing platform has Hazelcast IMDG at its core as the fastest in-memory data and compute layer. This allows a bank to hold all or active data sets in memory while integrating with disk-based stores like RDBMS, Hadoop, MongoDB, etc. Hazelcast Jet, which is a stream and batch processing engine, uses the IMDG to provide high-speed event processing with niche features like temporal sequencing, processing and comparing multiple event streams, performing analytical functions, etc. Hazelcast Jet is a Directed Acyclic Graph (DAG) engine ready for programming the business logic — once again cutting down on setup and integration time.
Both Hazelcast IMDG and Jet do not have any dependencies and can be embedded in other applications. Built in Java, it is also very easy to integrate with other applications and services built using Hazelcast technology can be integrated with middleware like an Enterprise Service Bus (ESB), such as Mulesoft, to be exposed through APIs in the Open Banking ecosystem.
The leading in-memory platform for Open Banking.
Hazelcast IMDG is the leading in-memory data grid (IMDG) supporting Open Banking. IMDGs are designed to provide high-availability and scalability by distributing data across multiple machines. Hazelcast IMDG enriches your application by providing the capability to quickly process, store and access data from a broad and diverse range of sources, with the speed of RAM.
Hazelcast Jet is an application embeddable, distributed stream processing platform for building IoT and microservices-based applications. The Hazelcast Jet architecture is high performance and low-latency driven, based on a parallel, distributed core engine enabling data-intensive applications operating at real-time speeds in support of PSD2 compliance.
The benefits of moving to the cloud are well known and applicable to virtually every industry. Hazelcast offers customers the flexibility to deploy to the cloud on their terms, whether it's a dedicated, on-premises, hybrid, or private cloud.
High-Density Memory Store adds the ability for Hazelcast Enterprise HD IMDG to store very large amounts of cached data in Hazelcast members (servers) and in the Hazelcast Client (near cache), limited only by available RAM for extreme scale-up.
Stream processing is how Hazelcast processes data on-the-fly, prior to storage, rather than batch processing, where the data set has to be stored in a database before processing. This approach is vital when the value of the information contained in the data decreases rapidly with age. The faster information is extracted from the data better.
The speed of the Hazelcast in-memory computing platform enables new levels of real-time predictive model servicing in support of delivering artificial intelligence solutions, as well as allowing real-time engineering and model retraining.
In the search for growth and competitive advantage, banks are opening up their core capabilities to third parties. The ambition is to create innovative products and services more tailored to customers’ needs and expectations. This is setting the stage for dramatic changes in the banking industry which, in turn, creates new technical challenges. In this white paper, you will gain insight into how these challenges can be overcome with the Hazelcast Open Banking platform.
This white paper is intended for line-of-business managers, software developers, and systems architects who are researching technology solutions to capitalize on U.K. Open Banking Standard and European Union “Revised Payment Service Directive” PSD2.
Hazelcast Financial Use Cases is intended to give systems engineers and architects in the financial industry an idea of the types of application use cases Hazelcast® is solving in production today. We'll take a look at three specific applications and discover how they integrate into existing work flows and systems. We'll uncover a Market Data Management System using Hazelcast as its core component, how a major investment bank in New York is making use of Hazelcast’s rich event API to wire together the various systems that make up its collateral management process, and how a leading investment bank in London uses Hazelcast to manage and interact with external foreign exchange brokerages to provide quotes from internal traders.
Learn about the requirements of moving from legacy mainframe systems to multi-channel customer self-service systems and the role that in-memory technology plays in that transition.
This case study presents a comparison of alternative data stores and approaches, showing how a global top ten bank is using Hazelcast IMDG® to enable older systems migration to web-based customer interfaces for online banking.
Whether you're interested in learning the basics of in-memory systems, or you're looking for advanced, real-world production examples and best practices, we've got you covered.