Learn How to perform robust Batch/Stream processing with Apache Flink
Apache Flink is an open source stream processing framework with powerful stream- and batch-processing capabilities.
A streaming-first runtime that supports both batch processing and data streaming programs
Elegant and fluent APIs in Java and Scala
A runtime that supports very high throughput and low event latency at the same time
Support for event time and out-of-order processing in the DataStream API, based on the Dataflow Model
Flexible windowing (time, count, sessions, custom triggers) accross different time semantics (event time, processing time)
Fault-tolerance with exactly-once processing guarantees
Natural back-pressure in streaming programs
Libraries for Graph processing (batch), Machine Learning (batch), and Complex Event Processing (streaming)
Built-in support for iterative programs (BSP) in the DataSet (batch) API
Custom memory management for efficient and robust switching between in-memory and out-of-core data processing algorithms
Compatibility layers for Apache Hadoop MapReduce and Apache Storm
Integration with YARN, HDFS, HBase, and other components of the Apache Hadoop ecosystem
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