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.
Most web applications often persist data from one request to the next. Web applications are dynamic, making calculations and database queries to serve requests. This overhead can be expensive, especially when we need the same data repeatedly. Django-based applications are no exception to this challenge. Ideally, we’d like a place to persist data between requests […]
The Hazelcast Python Client lets developers connect to Hazelcast clusters from Python apps and services. We can use the client to handle all of Hazelcast’s distributed data structures. We can also subscribe to cluster and data structure events. In this tutorial, we’ll explore how to install the client and use some of its key features. […]
Generating content served from an ASP.NET application can be slow and expensive — particularly when there’s heavy use of database calls. Relational databases are historically challenging to scale. Implementing caching means that the application doesn’t have to fetch and process the same data again and again. It’s one of the most effective ways to improve […]
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The Hazelcast .NET client is the entry point to all interactions with a Hazelcast cluster in a .NET application. This guide will get you up and running quickly with the Hazelcast .NET client and demonstrate some of its key features. Let’s cover a few prerequisites before we dive in. Before going further, you should: Understand […]
A lot, if not all, of data science projects, require some data visualization front-end to display the results for humans to analyze. Python seems to boast the most potent libraries, but do not lose hope if you’re a Java developer (or if you’re proficient in another language as well). In this post, I will describe […]
About two months ago, we announced the first BETA release of the Hazelcast Platform 5.0. Since then we’ve spent much effort in making the product adhere to the stability and production-ready standards which are expected from any Hazelcast release. For those who haven’t yet heard of Hazelcast Platform, we will first explain what the Hazelcast […]
It’s said a wise person learns from their mistakes, and a wiser person learns from the mistakes of others. In this blog, we’ll look at some common mistakes made when deploying Hazelcast, so you can avoid them and be that wiser person. In other words, what are the “anti-patterns” that don’t ensure failure, but do […]
Uniformity and balance are key principles for data grids. All grid members should hold the same amount of data, do the same amount of compute and have the same amount of resources (CPU, etc) available. At least approximately. It doesn’t have to be exactly even, but pretty close. A hot spot in the grid means […]
Recently, at Hazelcast, we have migrated our Management Center to TypeScript. Not just TypeScript, but the strictest TypeScript there is. If you are interested in why we decided to enable all the strict flags and benefits they provide, welcome to this post. Prelude 50k lines of JavaScript code of a React app. A small team […]
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