Companies need a data-processing solution that increases the speed of business agility, not one that is complicated by too many technology requirements. This requires a system that delivers continuous/real-time data-processing capabilities for the new business reality.
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.
Setting up servers and configuring software can get in the way of the problems you are trying to solve. With Hazelcast Cloud we take all of those pain points away.
Watch this webinar to learn how you can instantly fire up and then work with Hazelcast Cloud from anywhere in the world. With our auto-generated client stubs for Java, Go, Node.js, Python and .NET, we can have you connected and coding in less than a minute!
Hazelcast In-Memory solutions deliver against core requirements for financial services, across a broad range of use cases.
Hazelcast In-Memory technology powers the most demanding financial services environments.
Financial services is dependent upon timely access to financial market data. Unfortunately, maintaining complex feeds of commodity market data between systems is costly and introduces complex project management burdens. That’s why the creation of internally managed and centralized market data systems have long been the goal of many financial services firms. Hazelcast IMDG makes it possible.
Hazelcast provides a centralized API for use by front-, middle- and back-office systems to a single source of market data.
IMDG allows control over the Data Domain Model, cutting down on duplication of market data across multiple systems.
In-memory speed provides tighter control access to market data across the bank and provides a centralized point of authorization and audit.
In-memory allows for more predictable and accurately derived calculations with a single source of market data.
When sub-second speed matters, and it always does
2-3X faster than Redis
Customer data stored in-memory
Zero delay in transaction processing
Delight your traders and analysts by providing an instant response to rapidly changing market conditions.
Meet Burst Requirements
Move gracefully through extreme high-demand periods associated with the normal daily routine of financial market trading.
Scale and improve your performance elastically, reducing hardware requirements and driving efficiency.
Why Most Data Stores Fall Short
Why Hazelcast IMDG is Ideal
Market data systems require the ability to onboard new data feeds quickly. RDBMS and NoSQL solutions typically require brittle manual integrations or expensive middleware to connect systems.
Hazelcast uses a single standard Java API and provides native language clients for Java, Scala, Node.js, Python, .NET, and C++, as well as for publishing an Open Client Network Protocol for building any other native language client needed.
Traditional relational databases such as Oracle or Microsoft use normalized data schemata that read-write from disk – not memory. This leads to poor application query performance resulting in unacceptable online customer experiences.
Hazelcast is a schema-less In-Memory data store, and is approximately 1,000 times faster than a RDBMS, achieving query and update times measured in microseconds for high volumes of data.
Integrating of multiple market data feeds quickly and easily without impacting existing throughput is a critical requirement for market data systems. Using an RDBMS, schema changes can impact systems and introduce narrow maintenance window requirements.
Hazelcast in contrast is a schema-less data store that is able to handle new data types without down-time.
Market data systems often used by trading desk systems demand low-latency access to data measured in microseconds.
Hazelcast’s unique in memory management is able to drastically narrow latency volatility to levels that are unachievable with NoSQL and RDBMS systems.
RDBMS and NoSQL are narrowly focused on being a datastore – integration with other systems, utilization of memory, and DevOps are secondary considerations often requiring purchase or integration of more software or hardware.
With Hazelcast IMDG, return on investment can be achieved in several areas, chiefly through reduced data integration effort, maximization of economies of scale in terms of memory utilization, and ongoing operational costs, both in terms of hardware and software.
The fastest in-memory solution for financial market data.
Hazelcast IMDG is the leading Open Source in-memory data grid (IMDG). 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 the data 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 to operate at real-time speeds.
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 data the better.
The benefits of moving to the cloud are well known and applicable to virtually every industry. Hazelcast offers our customers the flexibility to deploy to the cloud on their terms, whether it's a dedicated cloud, on-premise cloud, hybrid cloud, or private cloud.
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 enabling real-time engineering and model retraining.
Risk is currently the driver of many development efforts in the finance industry. When developing financial risk systems you are faced with a number of challenges such as implementation and distribution of complex algorithms, Big Data, class modelling and UX design. Selecting the right technology can help you tremendously and simplify your architectures. Hazelcast® forms the foundation of Sungard's risk product offering and delivers significant benefits to us in a number of areas. The open architecture allows us to understand and find the optimal solutions to the challenges we are up against.
This webinar aims to give you an overview of our main use cases and how Hazelcast helped us to deliver award-winning risk systems to some of the biggest financial institutions in the world. We will demonstrate how it is used as the execution engine in our analytics grid, our Portable implementation of our security master and market data repository and how we integrate it with Node.js.
Learn about the requirements of how investment banks build financial market data systems to compete using in-memory computing technology.
This case study presents a comparison of alternative data stores and approaches, showing how Hazelcast IMDG® is used to cache session objects for their API gateway which all digital channels, from mobile to Internet Banking as well as other groups, access backend systems through.
Hazelcast IMDG® 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.
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.