New connectors, cluster shrink, & more within our Q1 Cloud Launch | Read the blog

Learnings from the Field. Lessons from Working with Dozens of Small & Large Deployments

If your data platform is powered only by batch data processing, you know you are always trailing your customer. Your databases aren’t always up to date. Your inability to have a synchronized data flow across systems leads to operational inefficiencies. And, your dreams of running advanced real-time AI and ML applications can’t be fulfilled. However, you might be wary of the implications of turning your product into an event-driven one. In this presentation we’ll share our experience transforming our CDP-based marketing orchestration engine to be both real-time and highly scalable with the Kafka ecosystem. We will look into how we saved resources with Connect when ingesting and syncing data with NoSQL databases, data warehouses and third-party platforms. What we did to turn ksqlDB into our data transformation, aggregation and querying hub, reducing latency and costs. How Streams helps us activate multiple real-time applications such as building identity graphs, updating materialized views in high frequency for efficient real-time lookups and inferencing machine learning models. Finally, we will look at how Confluent Cloud solved our pre-rollout sizing and scaling questions, significantly reducing time-to-market.


Mitchell Henderson