Today, we’re introducing Confluent Cloud’s fully managed service for Apache Flink®, improvements to Kora Engine, how AI and streaming work together, and much more.
As of today, Apache Flink® on Confluent Cloud is available for preview in select regions on AWS. In this post, learn how we’ve re-architected Flink as a cloud-native service on Confluent Cloud.
Learn how to build a Java pipeline that consumes clickstream data from Apache Kafka®. Consuming clickstreams is something that many businesses have a use for and it can also be generalized to consuming other types of streaming data.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
Learn how modern data management approaches like data mesh and event-driven architecture (EDA) can be used to manage data platforms and how to take advantage of them.