Confluent Blog

Our business at Loggi has grown a lot over the past few years, and with that expansion came the realization that our systems had to be more distributed. We pushed our architecture to a new level so we could keep up with the company's growth by building new event-driven systems and real-time data

Companies are looking to optimize cloud and tech spend, and being incredibly thoughtful about which priorities get assigned precious engineering and operations resources. “Build vs. Buy” is being taken seriously again. And if we’re honest, this probably makes sense. There is a lot to optimize.

Why do our customers choose Confluent as their trusted data streaming platform? In this blog, we will explore our platform’s reliability, durability, scalability, and security by presenting some remarkable statistics and providing insights into our engineering capabilities.

Operating Kafka at scale can consume your cloud spend and engineering time. And operating everyday tasks like scaling or deploying new clusters can be complex and require dedicated engineers. This post focuses on how Confluent Cloud is 1) Resource Efficient, 2) Fully Managed, and 3) Complete.

Data streaming capabilities are transforming everything, from allowing you to see when your ride will arrive to powering curbside pickups of groceries. The immediacy and personalization of those commercial experiences are fast becoming the expectations when using public services and healthcare, too.

Companies in nearly every industry are using Apache Kafka to harness their streaming data and deliver rich customer experiences and real-time business insights. In fact, Kafka has become so widely accepted as the de facto technology for data streaming, that it’s now used by over 70% of the Fortune

The blog introduces Confluent Platform 7.4 and its key features, including enhancing scalability, increasing architectural simplicity, accelerating time to market, reducing ops burden, and ensuring high-quality data streams. It also covers what's new in Apache Kafka 3.4.

In part 2 of our blog series on understanding and optimizing your Kafka costs, we dive into how to estimate costs stemming from the development and operations personnel needed to self-manage Kafka.

Real-time fraud prevention has become a critical capability in the financial services industry. That’s especially true for digital-native banks like EVO Banco, which can be particularly vulnerable to common methods of financial fraud.

This blog post discusses the two generals problems, how it impacts message delivery guarantees, and how those guarantees would affect a futuristic technology such as teleportation.

Hi there, it’s me, Danica Fine, your friendly neighborhood Kafka Summit London Program Committee Chair. Kafka Summit London 2023 is in just over two weeks, and I’ll be joining our incredible community members at the ExCeL in London to talk all things Apache Kafka® over two jam-packed days.

It's hard to properly calculate the cost of running Kafka. In part 1 of 4, learn to calculate your Kafka costs based on your infrastructure, networking, and cloud usage.

When Steven Zhang graduated from college in 2019, he, was searching for intellectually-stimulating work. That search led him to Confluent. Four years in, Steven currently works as a senior software engineer in the Stream Processing and Analytics organization

Shaun Clowes, Confluent’s Chief Product Officer since December of last year, will tell you data is the lifeblood of modern organizations. But he will also tell you he isn’t a big fan of the word “data.”