Apache Flink is an open source system for expressive, declarative, fast, and efficient data analysis on both historical (batch) and real-time (streaming) data. Flink combines the scalability and programming flexibility of distributed MapReduce-like platforms with the efficiency, out-of-core execution, and query optimization capabilities found in parallel databases. At its core, Flink builds on a distributed dataflow runtime that unifies batch and incremental computations over a true-streaming pipelined execution. Its programming model allows for stateful, fault tolerant computations, flexible user-defined windowing semantics for streaming and unique support for iterations. Flink is converging into a use-case complete system for parallel data processing with a wide range of top level libraries ranging from machine learning through to graph processing. Apache Flink originates from the Stratosphere project led by TU Berlin and has led to various scientific papers (e.g., in VLDBJ, SIGMOD, (P)VLDB, ICDE, and HPDC). In this half-day tutorial we will introduce Apache Flink, and give a tutorial on its streaming capabilities using concrete examples of application scenarios, focusing on concepts such as stream windowing, and stateful operators.