It cuts costs by providing an efficient compression algorithm based on row level compression using a dictionary to store repetitive patterns.

It complements your existing infrastructure by extending your data collection and analysis capabilities.

It integrates data from a variety of structured and unstructured sources.

It enables analysis at scale.

MapReduce jobs are used to process vast amounts of data in-parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner.

MapReduce jobs are used to process small amounts of data in-parallel on expensive hardware, without fault-tolerance.

MapReduce jobs are used to process structured data in sequence, with fault-tolerance.

MapReduce jobs are used to execute sequential search outside the Hadoop environment using a built-in UDF to access information stored in non-relational databases.

Big data solutions are ideal for analyzing not only raw structured data, but semi structured and unstructured data from a wide variety ofsources.

Big data solutions are ideal when all, or most, of the data needs to be analyzed versus a sample of the data; or a sampling of data isn nearlyBig data solutions are ideal when all, or most, of the data needs to be analyzed versus a sample of the data; or a sampling of data isn? nearlyas effective as a larger set of data from which to derive analysis.

Big data solutions are ideal for Online Transaction Processing (OLTP) environments.

Big data solutions are ideal for iterative and exploratory analysis when business measures on data are not predetermined.