Spark Streaming makes it easy to build scalable, robust stream processing applications — but only once you’ve made your data accessible to the framework. If your data is already in one of Spark Streaming’s well-supported message queuing systems, this is easy. If not, an ad hoc solution to import data may work for a single application, but trying to scale that approach to complex data pipelines integrating dozens of data sources and sinks with multi-stage processing quickly breaks down. Spark Streaming solves the realtime data processing problem, but to build large scale data pipeline we need to combine it with another tool that addresses data integration challenges. The Apache Kafka project recently introduced a new tool, Kafka Connect, to make data import/export to and from Kafka easier. This talk will first describe some data pipeline anti-patterns we have observed and motivate the need for a tool designed specifically to bridge the gap between other data systems and stream processing frameworks. We will introduce Kafka Connect, starting with basic usage, its data model, and how a variety of systems can map to this model. Next, we’ll explain how building a tool specifically designed around Kafka allows for stronger guarantees, better scalability, and simpler operationalization compared to other general purpose data copying tools. Finally, we’ll describe how combining Kafka Connect and Spark Streaming, and the resulting separation of concerns, allows you to manage the complexity of building, maintaining, and monitoring large scale data pipelines.

1. BUILDING REALTIME DATA PIPELINES
WITH KAFKA CONNECT AND SPARK
STREAMING
Ewen Cheslack-Postava
Confluent

4. Stream Processing

8. Introducing
Kafka Connect
Large-scale streaming data import/export for Kafka

11. Separation of Concerns

21. Data Integration as a Service

27. THANK YOU
@ewencp
@confluentinc
http://confluent.io/download/
http://connectors.confluent.io