Originally posted on Data Science Central

Architecture:

In 2000, Seisint Inc. (now LexisNexis Group) developed a C++-based distributed file-sharing framework for data storage and query. The system stores and distributes structured, semi-structured, and unstructured data across multiple servers. Users can build queries in a C++ dialect called ECL. ECL uses an “apply schema on read” method to infer the structure of stored data when it is queried, instead of when it is stored. In 2004, LexisNexis acquired Seisint Inc. and in 2008 acquired ChoicePoint, Inc. and their high-speed parallel processing platform. The two platforms were merged into HPCC (or High-Performance Computing Cluster) Systems and in 2011, HPCC was open-sourced under the Apache v2.0 License. Currently, HPCC and Quantcast File System are the only publicly available platforms capable of analyzing multiple exabytes of data.

In 2004, Google published a paper on a process called MapReduce that uses a similar architecture. The MapReduce concept provides a parallel processing model, and an associated implementation was released to process huge amounts of data. With MapReduce, queries are split and distributed across parallel nodes and processed in parallel (the Map step). The results are then gathered and delivered (the Reduce step). The framework was very successful, so others wanted to replicate the algorithm. Therefore, an implementation of the MapReduce framework was adopted by an Apache open-source project named Hadoop.

MIKE2.0 is an open approach to information management that acknowledges the need for revisions due to big data implications identified in an article titled “Big Data Solution Offering”.The methodology addresses handling big data in terms of useful permutations of data sources, complexity in interrelationships, and difficulty in deleting (or modifying) individual records.

Recent studies show that a multiple-layer architecture is one option to address the issues that big data presents. A distributed parallel architecture distributes data across multiple servers; these parallel execution environments can dramatically improve data processing speeds. This type of architecture inserts data into a parallel DBMS, which implements the use of MapReduce and Hadoop frameworks. This type of framework looks to make the processing power transparent to the end user by using a front-end application server.

Big Data Analytics for Manufacturing Applications can be based on a 5C architecture (connection, conversion, cyber, cognition, and configuration).

The data lake allows an organization to shift its focus from centralized control to a shared model to respond to the changing dynamics of information management. This enables quick segregation of data into the data lake, thereby reducing the overhead time.

Technologies

A 2011 McKinsey Global Institute report characterizes the main components and ecosystem of big data as follows:

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 tensors, which can be more efficiently handled by tensor-based computation, such as multilinear subspace learning. Additional technologies being applied to big data include massively parallel-processing (MPP) databases, search-based applications, data mining,distributed file systems, distributed databases, cloud-based infrastructure (applications, storage and computing resources) and the Internet.[citation needed]

Some but not all 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.

DARPA‘s Topological Data Analysis program seeks the fundamental structure of massive data sets and in 2008 the technology went public with the launch of a company called Ayasdi.

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 analytic systems that thrive on system performance, commodity infrastructure, and low cost.

Real or near-real time information delivery is one of the defining characteristics of big data analytics. Latency is therefore avoided whenever and wherever possible. Data in memory is good—data on spinning disk at the other end of a FC SAN connection is not. The cost of a SAN at the scale needed for analytics applications is very much higher than other storage techniques.

There are advantages as well as disadvantages to shared storage in big data analytics, but big data analytics practitioners as of 2011 did not favor it.

Source: Intro to Bigdata Architecture