Data streaming is a really difficult problem. Despite 10+ years of attempting to simplify it, teams building real-time data pipelines can spend up to 80% of their time optimizing it or fixing downstream output by handling bad data at the lake. All we want is a service that will be reliable, handle all kinds of data, connect with all kinds of systems, be easy to manage, and scale up and down as our systems change. Oh, it should also have super low latency and result in good data. Is it too much to ask?

In this presentation, you’ll learn the basics of data streaming and architecture patterns such as DLQ, used to tackle these challenges. We will then explore how to implement these patterns using Apache Flink and discuss the challenges that real-time AI applications bring to our infra. Difficult problems are difficult, and we offer no silver bullets. Still, we will share pragmatic solutions that have helped many organizations build fast, scalable, and manageable data streaming pipelines.