Fabian Hueske discusses why SQL is a great approach to unify batch and stream processing. He gives an update on Apache Flink's SQL support and shares some interesting use cases from large-scale production deployments. Finally, Fabian presents Flink's new query service that enables users and applications to submit streaming and batch SQL queries and retrieve low-latency updated results.

Complex event processing (CEP) helps detect patterns over continuous streams of data. DNA sequencing, fraud detection, shipment tracking with specific characteristics (e.g., contaminated goods), and user activity analysis fall into this category. Kostas Kloudas offers an overview of Flink's CEP library and explains the benefits of its integration with Flink.

Big data applications have been an almost exclusive domain of Java and Scala developers. This not only frustrates engineers who prefer other languages and their ecosystems, but also impedes companies …

Aljoscha Krettek offers an overview of the modern stream processing space, details the challenges posed by stateful and event-time-aware stream processing, and shares core archetypes ("application blueprints) for stream processing drawn from real-world use cases with Apache Flink.

Dean Wampler compares and contrasts data processing with Akka Streams and Kafka Streams, microservice streaming applications based on Kafka. Dean discusses the strengths and weaknesses of each tool for particular design needs and contrasts them with Spark Streaming and Flink, so you'll know when to choose them instead.

Join Dean Wampler and Boris Lublinsky to learn how to build two microservice streaming applications based on Kafka using Akka Streams and Kafka Streams for data processing. You'll explore the strengths and weaknesses of each tool for particular design needs and contrast them with Spark Streaming and Flink, so you'll know when to choose them instead.

Fabian Hueske and Shuyi Chen explore SQL's role in the world of streaming data and its implementation in Apache Flink and cover fundamental concepts, such as streaming semantics, event time, and incremental results. They also share their experience using Flink SQL in production at Uber, explaining how Uber leverages Flink SQL to solve its unique business challenges.

Steven Wu explains how Netflixs SPaaS platform empowers users to focus on extracting insights from data streams and build stream processing applications and shares lessons learned building and operating the largest SPaaS use case: Netflixs Keystone data pipeline, a self-serve platform for creating near-real-time event pipelines that processes three trillion events and 12 PB of data every day.

Dean Wampler and Boris Lublinsky walk you through building several streaming microservices applications based on Kafka using Akka Streams and Kafka Streams for data processing. You'll explore the strengths and weaknesses of each tool, helping you choose the best tools for your needs, and contrast them with Spark Streaming and Flink, so you can determine when to choose them instead.