How can algorithms that require iterative processing of a large dataset that may or may not contain connected entities be processed in an efficient and timely manner?

How can different distributed processing frameworks be used to process large amounts of data without having to learn the programmatic intricacies of each framework?

How can confidence be instilled in results whose computation involves applying a series of processing steps in a Big Data environment?

Storing large amounts of data, arriving at fast pace, as a dataset and processing it in a batch manner incurs processing latency, causing a delay before analysis results become available.

How can the complete re-execution of a series of processing steps be avoided in case an error occurs partway through?

How can large amounts of data be processed without investing in any Big Data processing infrastructure and only paying for the amount of time the processing resources are actually used?

How can complex processing tasks be carried out in a manageable fashion when using contemporary processing techniques?

The Big Data Processing Environment represents an environment capable of handling the range of distinct requirements of large-scale dataset processing.

How can very large amounts of data be processed with maximum throughput?