Many developers’ experience with Spring began with JDBC-based data access, and while the template design pattern remains prevalent, Spring’s suite of data libraries has continued to evolve (over the past decade!) with the increased popularity of NoSQL data stores. Spring has also evolved to support higher-level patterns, such as ETL (Extract-Transform-Load) and EIP (Enterprise Integration Patterns), with Spring Batch and Spring Integration, respectively. Taken together Spring Data, Batch and Integration provide a comprehensive foundation for working with data and building message-driven applications. In this session, we’ll walk through the libraries and frameworks with an emphasis on introductory demos.

Big Data is quickly proving to be more than hype, as organizations face an increasing volume of information and the challenge to react to that information effectively. New tools and techniques are needed to address that challenge. However, Spring Integration and Spring Batch are both flexible enough to support these new tools and techniques upon their proven foundations for messaging and batch-processing.

In this session, we will walk through a couple Big Data use-cases implemented with Spring Integration, Spring Batch, and the Spring for Apache Hadoop project. The underlying theme is that the frameworks can be combined to provide a platform for Big Data processing, both real-time and batch-oriented, while also incorporating new tools and techniques such as HDFS and Map Reduce.