Streaming applications are applications that deal with a large amount of data arriving continuously. In processing streaming application data, the data can arrive late, arrive out of order, and the processing can undergo failure conditions. It can be appreciated that tools designed for previous generations of big data applications may not be ideally suited to process and store streaming application data.
Enabling streaming applications to store large amounts of data from a storage perspective can be challenging. There is a need to determine the proper storage primitive that would ideally be suited for building a new generation of streaming applications in conjunction with existing tools like Apache Flink. In using a Lambda architecture, a developer may use a complex combination of middleware tools that include batch style middleware influenced by platforms like Apache Hadoop and continuous processing tools like Apache Storm, Apache Samza, Apache Kafka and others. Batch style processing can be used to deliver accurate but potentially out of data analysis of data. It can be appreciated that “real-time” processing may deliver faster results but could come at a cost of reduced accuracy. Furthermore, there may be a need for two copies of application logic because the programming models of a speed layer are different than those used in a batch layer.
Conventionally, Lambda architectures may be expensive to develop and expensive to deploy and manage in production. In some implementations, as more application like Internet of Things (“IoT”), require continuous processing, it may not be beneficial to use Lambda architectures and conventional style middleware. Therefore, there exists a need for a simpler approach to Lambda to process streaming application data.