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I shouldn’t have to convince anyone that Apache Kafka is an incredibly useful and powerful technology. As a distributed event streaming platform, it’s adept at storing your event data and serving it up for downstream consuming applications to make sense of that information––in real time or as close to real time as your use case permits. The real beauty of Kafka as a technology is that it can do it with very little effort on your part. In effect, it’s a black box.

Commercial and open source large language models (LLMs) are evolving rapidly, enabling developers to create innovative generative AI-powered business applications. However, transitioning from prototype to production requires integrating accurate, real-time, domain-specific data tailored to your business needs and deploying at scale with robust security measures.

Since its beginnings just five years ago, Apna has become the leading jobs site for tens of millions of workers in India, the largest labor market in the world. Today, Apna has more than 50 million registered users, resulting in more than 5 million interviews and 100,000 jobs activated per month.

Over the years, Amazon DynamoDB has grown into a feature-rich NoSQL database that has deep integrations with various services such as Amazon S3 and AWS Lambda. As businesses increasingly depend on data for decision-making, it is common to use data residing in DynamoDB to contextualize or even drive events at a granular level (as opposed to bulk or batch).

Hey, you! Yeah, you! The puzzled-looking Spring Boot developer, scouring the web for a guide on integrating your microservices with Apache Kafka on Confluent Cloud with Stream Governance. Admit it, you’ve been Googling nonstop for the past hour and all you’ve found are examples using StringSerializer/StringDeserializer with not even the slightest mention of "schema registry-aware" serialization methods. And I bet the examples you found are implemented in Java.

This summer marks five years since we announced our first fully managed connector on Confluent Cloud in 2019, the Amazon S3 Sink Connector. Since then, our connector offerings have not only expanded significantly but also enabled teams to send hundreds of petabytes of data throughput. Today, we support over 80 pre-built, fully managed connectors, custom connectors, and secure private networking.

At Confluent, we are committed to enhancing our product to meet the evolving needs of our customers. In the last year we’ve seen substantial increases in both UI users and UI usage. With such high growth, we’ve been looking for ways to improve experiences where we can. Based on internal and external validation, the Confluent Cloud Homepage was specifically identified as an area that could be improved.

Confluent Cloud, a leading cloud-native platform for building data streaming applications, and BigQuery, Google's serverless data warehouse, are revolutionizing how businesses handle data. Together, they offer a powerful solution for real-time data ingestion, processing, and analysis—now enhanced by BigQuery’s new continuous query. Many organizations grapple with challenges in moving data from their data warehouses to real-time processing platforms.

The Apache Flink community released Apache Flink 1.20 this week. In this blog post, we'll highlight some of the most interesting additions and improvements. You’ll find a comprehensive rundown of all of the updates in the official release announcement. Recent Flink releases have emphasized improving Flink’s usability, not only for developers, but also for those operating Flink clusters, and this theme continues in this latest release.

BT Group's Smart Event Mesh: Centralized Event Streaming With Decentralized Customer Experience, Automation, and a Foundation Built on Confluent. BT Group is a British multinational telecommunications holding company headquartered in London, England. It has operations in around 180 countries and is one of the largest telecommunications companies in the world, providing a range of products and services including fixed-line, broadband, mobile, and TV.