Interest in analyzing the real-time web has reached a fever pitch among academics and corporate executives. Researchers and professionals tasked with capturing and analyzing high volumes of real-time social data have a plethora of open-source databases and machine learning libraries to choose from, but often spend a large fraction of their time writing code (and manually performing) ingestion, cleansing, normalization, and data management.

Apache Streams seeks to break these problems down into self-contained modules based on simple interfaces and foster a community-based approach to connecting and harmonizing data sources and services. Implementers can compose a data workflow from streams components and run their workflow in real-time or batch modes, using a variety of storage services (Kafka, HDFS, Cassandra, etc...) and execution engines (Tomcat, Storm, Amazon Kinesis, etc...)