Technology

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.

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.

Our new PII Detection solution enables you to securely utilize your unstructured text by enabling entity-level control. Combined with our suite of data governance tools, you can execute a powerful real-time cyber defense strategy.

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.

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.

If you’ve been working with Kafka Streams and have seen an “unknown magic byte” error, you might be wondering what a magic byte is in the first place, and also, how to resolve the error. This post explains the answers to both questions.

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.

Breaking encapsulation has led to a decade of problems for data teams. But is the solution just to tell data teams to use APIs instead of extracting data from databases? The answer is no. Breaking encapsulation was never the goal, only a symptom of data and software teams not working together.

Stream processing has long forced an uncomfortable trade-off: choose a framework based on its power, or in your preferred programming language. GraalVM may offer an alternative solution to avoid having to choose.

The ML and data streaming markets have socio-technical blockers between them, but they are finally coming together. Apache Kafka and stream processing solutions are a perfect match for data-hungry models.

Apache Kafka and stream processing solutions are a perfect match for data-hungry models. Our community’s solutions can form a critical part of a machine learning platform, enabling machine learning engineers to deliver real-time MLOps strategies.

The big data revolution of the early 2000s saw rapid growth in data creation, storage, and processing. A new set of architectures, tools, and technologies emerged to meet the demand. But what of big data today? You seldom hear of it anymore. Where has it gone?

Use the Confluent CLI and API to create Stream Designer pipelines from SQL source code.

Experienced technology leaders know that adopting a new technology can be risky. Often, we are unable to distinguish between those investments that will be transformational and those that won’t be worthwhile. This post examines how one can decide if event streaming makes sense for them.