Systems | Development | Analytics | API | Testing

A conversation with Thrivent and Improving Why would a membership-owned fraternal financial services organization need to invest in their data architecture? The answer lies at the heart of their corporate mission-putting their customer first.

When it comes to video platforms, user experience is king. Video creators and viewers expect a rich toolset and highly engaging platforms that cater to their needs and interests in real time, with zero buffering or other performance hiccups. Add to this sheer scale-hundreds of millions of users, a hundred billion views to date-and data pipelines become vital for ingesting and processing real-time data.

The Confluent Cloud Q3 '23 Launch brings you new features that enable you to deliver intelligent, secure and cost-effective data pipelines. Learn about our Kora Engine, Enterprise Clusters, our secure, cost-effective, and serverless Kafka powered by the Kora Engine.

While the benefits from a data mesh architecture are clear, what's not as obvious is the fact that implementing it brings its own set of challenges for businesses, especially as many are working with legacy data pipelines. Building a data mesh the right way requires a modern approach to data pipelines that includes streaming data so you can get the right data to the right teams in the right format at the right time.

Apache Kafka® makes real-time data available to all the applications that need to use it. Customers across industries use real-time data streaming to stay ahead of customer needs, build modern apps, and connect teams and systems across the organization. Use cases range from stock trading and fraud detection to transportation, data integration, and real-time analytics-and plenty more.

Stream processing plays a critical role in the infrastructure stack for data streaming. Developers can use it to filter, join, aggregate, and transform their data streams on the fly to power real-time applications and streaming data pipelines. Among stream processing frameworks, Apache Flink has emerged as the de facto standard because of its performance and rich feature set. However, self-managing Flink (like self-managing other open source tools like Kafka) can be challenging due to its operational complexity, steep learning curve, and high costs for in-house support.