Big Data Consulting And Analytics Services

The term big data has been in use since the 1990s, with some giving credit to John Mashey for popularizing the term. Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. Big data philosophy encompasses unstructured, semi-structured and structured data, however the main focus is on unstructured data. Big data "size" is a constantly moving target; as of 2012 ranging from a few dozen terabytes to many zettabytes of data. Big data requires a set of techniques and technologies with new forms of integration to reveal insights from data-sets that are diverse, complex, and of a massive scale.

"Variety", "veracity" and various other "Vs" are added by some organizations to describe it, a revision challenged by some industry authorities. The Vs of big data were often referred to as the "three Vs", "four Vs", and "five Vs". They represented the qualities of big data in volume, variety, velocity, veracity, and value. Variability is often included as an additional quality of big data.

A 2018 definition states "Big data is where parallel computing tools are needed to handle data", and notes, "This represents a distinct and clearly defined change in the computer science used, via parallel programming theories, and losses of some of the guarantees and capabilities made by Codd's relational model.

Big data vs. business intelligence

The growing maturity of the concept more starkly delineates the difference between "big data" and "business intelligence".

• Business intelligence uses applied mathematics tools and descriptive statistics with data with high information density to measure things, detect trends, etc.
• Big data uses mathematical analysis, optimization, inductive statistics and concepts from nonlinear system identification to infer laws (regressions, nonlinear relationships, and causal effects) from large sets of data with low information density^] to reveal relationships and dependencies, or to perform predictions of outcomes and behaviors.

• Techniques for analyzing data, such as A/B testing, machine learning and natural language processing
• Big data technologies, like business intelligence, cloud computing and databases
• Visualization, such as charts, graphs and other displays of the data

Multidimensional big data can also be represented as OLAP data cubes or, mathematically, tensors. array database systems have set out to provide storage and high-level query support on this data type. Additional technologies being applied to big data include efficient tensor-based computation, such as multilinear subspace learning., massively parallel-processing (MPP) databases, search-based applications, data mining, distributed file systems, distributed cache (e.g., burst buffer and Memcached), distributed databases, cloud and HPC-based infrastructure (applications, storage and computing resources) and the Internet. Although, many approaches and technologies have been developed, it still remains difficult to carry out machine learning with big data.

Some MPP relational databases have the ability to store and manage petabytes of data. Implicit is the ability to load, monitor, back up, and optimize the use of the large data tables in the RDBMS.

The practitioners of big data analytics processes are generally hostile to slower shared storage, preferring direct-attached storage (DAS) in its various forms from solid state drive (SSD) to high capacity SATA disk buried inside parallel processing nodes. The perception of shared storage architectures—storage area network (SAN) and network-attached storage (NAS) —is that they are relatively slow, complex, and expensive. These qualities are not consistent with big data analytics systems that thrive on system performance, commodity infrastructure, and low cost.