Category: Hazelcast

Feed: Blog – Hazelcast. Author: Dan Ortega. The Open Banking initiative, also referred to as PSD2 (Payment Services Directive), is a textbook example of streaming technology applied to modern banking requirements. In this model, banks (primarily in Europe) are required to make their back-end systems for customer accounts and payment services available to other members of the financial payment ecosystem. While this is a consumer-friendly initiative, from a bank’s perspective the broader transactional ecosystem has effectively been force-integrated into their operational systems.  This essentially means a significant increase in sources of streaming data from a more diverse range of service ... Read More

Feed: Blog – Hazelcast. Author: Rafal Leszko. I’m sure you use caching somewhere in your system. This can be either to improve performance, reduce backend load, or to decrease downtime. Everybody uses caching. Caching is everywhere. However, in which part of your system should it be placed? If you look at the following diagram representing a simple microservice architecture, where would you draw the “cache” rectangle? There is more than one right answer to this question. You could place the cache inside each service or as a completely separate cache server. You could also put it in front of each ... Read More

Feed: Blog – Hazelcast. Author: Greg Luck. Introduction We all know that selecting the right technology for your business-critical systems is hard. You first have to decide what characteristics are most important to you, and then you need to identify the technologies that fit that profile. The problem is that you typically only get a superficial view of how technologies work, and thus end up making decisions on limited information. Selecting amongst in-memory technologies – and especially distributed systems – can be especially challenging since many of the system’s attributes may not be easy to uncover. One topic, in particular, ... Read More

Feed: Blog – Hazelcast. Author: Neil Stevenson. In June 2019, we announced the inclusion of Hazelcast Jet as a runner for Apache Beam. Now it’s time for an example showing how it’s done. As a bonus, it’s not “Word Count.” IoT Data The data we will use is a series of 2,000 GPS points and time offsets: # Latitude, Longitude, Time-Offset 45.417,8.179,1629 45.417,8.178,1630 45.416,8.178,1631 45.416,8.177,1632 45.415,8.176,1633 These points are real data. They come from a trip on Italy’s famous Frecciarossa (Red Arrow) train from Milan to Turin. Since we have real data, we can solve some real problems. Since trains move, ... Read More

Feed: Blog – Hazelcast. Author: Enes Akar. As cloud technologies evolve, applications require less human intervention and maintenance. “Serverless” is a term that implies that users should have nothing to do with servers. The most exciting claim of serverless functions is that they scale automatically as the user base and load grows. Moreover, when there is no user activity, there will be no server or resource usage. But, serverless functions are not always the best solution. Many use cases require servers, such as web applications, databases, queues, etc. When you need servers, cloud instances, and VMs in your architecture, then ... Read More

Feed: Blog – Hazelcast. Author: Nazarii Cerkas. Recently, an interesting paper was published by UC Berkeley with their review of serverless computing and quite reasonable predictions: “…Just as the 2009 paper identified challenges for the cloud and predicted they would be addressed and that cloud use would accelerate, we predict these issues are solvable and that serverless computing will grow to dominate the future of cloud computing.” So, why should the industry go serverless at all? A simple answer is that we, as software engineers, are eager to be effective and want to focus on the result. Serverless computing is ... Read More

Feed: Blog – Hazelcast. Author: Emin Demirci. We are excited to announce the new repository for extension modules of Hazelcast Jet. This repository will be home for the Hazelcast Jet extension modules, including connectors (including both sources and sinks), custom aggregations, and context factories. All of these modules will help make the integration easier for 3rd-party products in your pipelines. The main idea of having a separate repository for these extensions is to have a faster software development lifecycle. We have more flexibility for prototyping, developing, and releasing new extensions with this approach. Any contributions, ideas, or implementations are always ... Read More

Feed: Blog – Hazelcast. Author: Dan Ortega. Stream processing refers to real-time management of data entering a banking system (or any information system, actually) at high speed and volume, usually from a broad range of sources. The “management” aspect means data is wholly or partially processed and contextualized before entering an in-memory (operational) system, where pre-processing can significantly accelerate response times. Before in-memory technologies, transactional data was managed as a batch process and analyzed every hour, once a day, etc., depending on the data involved, the bank’s policies for handling data, regulatory and compliance requirements, and so on. While having ... Read More

Feed: Blog – Hazelcast. Author: Can Gencer. After releasing Hazelcast Jet 3.0 in May, we are happy to announce its first update, Hazelcast Jet 3.1. Hazelcast Jet is now an Apache Beam Runner Apache Beam is a framework for building distributed batch and stream processing applications over a unified API. The API itself is decoupled from the underlying execution implementation, making it possible to use Apache Beam with different execution engines. Over the last few months, we have been developing a Runner for it and we are happy to announce that the Hazelcast Jet Runner will be included in the next ... Read More

Feed: Blog – Hazelcast. Author: Greg Luck. Unbounded, unordered, global-scale datasets are increasingly common in day-to-day business – IoT sensor network data streams, mobile usage statistics, large scale monitoring, the list is endless. Numerous applications seek the ability to quickly react to dynamic streaming data, as it is either a mandatory requirement or a competitive advantage.  API ChurnAs a consequence, lots of companies see streaming as a valid technology, however many consider it still to be evolving too fast. The programming interface of Spark changed 3 times over the last 5 years (from low-level RDDs to something similar to database ... Read More