Systems | Development | Analytics | API | Testing

Bringing real-time capabilities to your product or software-as-a-service (SaaS) is no longer a nice-to-have; it’s a competitive necessity. Whether you're building a real-time payment platform, a patient monitoring system, or any product where instant data processing fuels great user experiences and artificial intelligence (AI)-driven innovation, Apache Kafka data streaming is likely at the core.

Data is in motion, and it’s moving faster than ever. For developers and data architects building modern real-time data platforms, the ability to get data from anywhere and analyze it instantly is a superpower. That’s why we’re excited to announce a major step forward in this journey: the fully managed sink connector for Clickhouse, now generally available on Confluent Cloud.

Playlists for every mood and occasion. Media recommendations grouped by the most niche theme from your watch history. Sophisticated ad algorithms that optimize pay-per-click ads for the customer experience. Whether you call them digital-native, disruptors, or just tech giants, the likes of Spotify, Netflix, and Amazon have long made uncannily personal experiences a key part of their differentiation or business models.

This is the second post in our series exploring the architectural innovations that make Confluent Cloud more cost-effective at scale. Building on our previous post about the operational complexities of Apache Kafka and our cloud-native architecture's solutions, we'll now dive into how we solved a core challenge for any data streaming workload: high cloud networking costs.

Today, Confluent announced a $200 million commitment over the next three years designed to supercharge the growth and impact of its global partner ecosystem. This investment expands on Confluent’s partner-centric strategy; well over 20%* of the data streaming pioneer’s business in the past year has been partner-sourced, underscoring the ecosystem’s vital role in unlocking real-time use cases at scale.

Operational and analytical estates have been separated since data warehouses were first introduced in the 1990s. The operational estate includes microservices, software-as-a-service (SaaS) apps, and enterprise resource planning systems (ERPs) that have become the beating heart of an organization. The analytical estate consists of the data warehouses, lakehouses, artificial intelligence (AI)/machine learning (ML) platforms, and other custom batch workloads that support business analysis and reporting.

Businesses need to be able to respond quickly to macroeconomic uncertainty and changing market conditions, whether those are caused by trade disputes, public health crises like the COVID-19 pandemic, or simply abrupt changes in consumer demand. Amid that swirl of disruptions, technology leaders are also being pressured by management and investors to optimize their businesses and drive as much efficiency as possible.

For Taohao, being a Senior Software Engineer means never standing still. Whether it’s picking up a new skill, diving deeper into a complex system, or exchanging ideas with teammates, he thrives in an environment where curiosity, innovation, and collaboration come together. Let’s find out what makes Confluent the place where engineers like Taohao aren’t just solving problems but are constantly learning, challenging each other, and building what’s next.

A joint post from the teams at NeuBird and Confluent For organizations leveraging Confluent, ensuring smooth operations is mission-critical. While Confluent Cloud eliminates the operational burden of managing Apache Kafka, application teams still need to monitor and troubleshoot client applications connecting to Kafka clusters.