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!
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A top ten US bank was hitting transaction rate limits when trying to apply fraud detection algorithms against customer data sitting on its old relational database platform. This technically imposed restriction was causing them to break fraud detection SLAs and to ultimately become a blocker on new business.
Hazelcast is being used to store 2TB of customer data, which will eventually grow to 5TB. Because this data is now stored in-memory, the bank is able to easily process 5,000 transactions per second with expectations for this to grow to 10,000 transactions. To further enhance operational availability, they are also using WAN replication to synchronize multiple Hazelcast clusters to ensure the continuation of service in the event of a regional data center outage.
Why Most Datastores Fall Short
Why Hazelcast IMDG Is Ideal
Most data stores are complex and costly to scale according to demand. Many data stores cannot be installed to scale without operator intervention, this is an essential feature for systems that wish to operate cost effectively within on-demand cloud environments.
Scaling a solution built on Hazelcast is as easy as starting an extra process. Hazelcast takes care of the rest, distributing data and tasks fairly, while the system is still running.
Client-side reactive messaging is not an available feature in most data stores, certainly not as a vendor supported, ﬁrst class feature.
Hazelcast provides reliable, highly available messaging across not just data events but also system events. The events can be received by clients and members of the Hazelcast cluster.
For real-time analytics on large volumes of data, NoSQL and RDBMS solutions suffer from latency issues.
As a fully in-memory data store, Hazelcast IMDG can ingest and transform data in microseconds.
Most NoSQL architectures have single processes for writes and other processes for backups. Worse still, during times of failure for these write processes, the system will block further updates until a new write process is elected. This means blocking writes on the entire data set. Introducing sharding to solve these issues results in highly complex topologies.
Every process in a Hazelcast cluster is responsible for handling a portion of the main data and backups, there is no single master in a Hazelcast cluster. Meaning that processes can be dropped without blocking writes to entire data sets.
Data stores do not allow sophisticated code to execute in situ with stored data.
Hazelcast can embed fraud detection code written in Java in its nodes where the customer account data is located, so that a large number of rules can be run within the SLA, all without needing to shift data over the network.
Competent fraud detection systems can help organizations gain a clearer view of entities, relationships, and hidden patterns as they deal with ﬁnancial crimes including payment card fraud, anti-money laundering & anti-terrorist ﬁnancing. Failure to invest in Fraud Detection Systems can be disastrous for a company’s bottom line, in 2017 a major European bank was ﬁned more than £500m by British and American authorities for anti-money laundering failings.
Consumer ﬁnancial transactions are also a target for fraudsters who are using increasingly sophisticated systems of their own, all at a time when today’s detection systems are facing increasing pressure. In the UK alone, payment card spending in Q3 2016 amounted to £163 billion generated from a record 3.7 billion transactions. Total ﬁnancial fraud losses across payment cards, remote banking, and checks in the ﬁrst half of 2016 increased by 25% to £399.5 million, enabled by scams and on-line attacks.
These rates of change mean that today’s Fraud Detection Systems must be ﬂexible to change, not only in their core logic but also system topology. They must report anomalies in near real-time and be highly available 24/7. Hazelcast In-Memory Data Grid and Hazelcast Jet (in-memory stream and fast batch processing) are ideally suited to delivering such systems.
Hazelcast can store account details, recent transaction history and scoring all in one place for powerful, comprehensive checks live within the request. Short SLAs of 500ms means that these checks must be done very fast. Hazelcast’s in-memory technology is a perfect ﬁt.
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.