Big Data

Introduction

In the 21st century, mobility, social networks, bandwidth, cloud computing, geolocation, social networks and reduced connectivity costs have increased. This caused a problem, since data volumes began to grow exponentially, as an example we can cite that in the year 2012 2.8 ZB of data (1ZB = 1 billion gigabytes) were created worldwide, according to data of the consulting firm IDC (International Data Corporation) in its study "The Digital Universe of Data 2012" published in December of 2012.

Big Data is already an established reality. Data analysts and data scientists are and will be the most sought-after professionals by companies. The amount of data that is generated every day, captured, stored and analyzed later has given rise to the new trend known as Big Data. Big Data is not only about taking information from social networks, but also encompasses a broader concept, such as M2M (machine to machine) or internet of things, improvements in Medical Diagnosis, Smart Cities, etc. This data revolution proposes a change in the decision-making. In the future, companies that do not implement Big Data will be in inferiority of conditions with respect to those that do it. New emerging professions have emerged in the world of technology, such as the Data Scientists, who must have statistical and analytical skills. Data analysis has been evolving, as large volumes of data have been growing and business intelligence tools have been taking the technologies either from OLAP (Online Analytical Processing) or from reports and queries (Reporting, Queries). Big Data instead, takes Web mining, text mining and social information for decision-making.

Big Data

According to one of the most prestigious technology-consulting firms, IDC, Big Data is a new generation of technologies, architectures and strategies designed to capture and analyze large volumes of data from multiple sources at high speed, also extracting economic value. As it is seen in the definition, it emphasizes 3 factors, velocity, variety and volume, however, also adding value and truth, forming what is known as "Model V5".

Big Data Architecture

The architecture of Big Data should consider the integration of new technologies and tools of the large volumes of data and their integration with the traditional data and with the existing infrastructure in the companies.

Inputs:

• Social Media, M2M, mobility, biometrics, etc.

• Structured data (sqlserver, Oracle, etc.)

• Unstructured data (Cassandra, MongoDB, CouchDB, etc.)

• DataWarehouse • Databases in memory (SAP-HANA)

Process:

• Hadoop; Framework for processing large volumes of data.

Output:

• Analysis and reporting tools

• Queries (query)

• Display (dashboards)

Strategic Sectors

The sectors that are seen and will be more affected by Big Data will be:

Health: mainly in genomic research, clinical operations, patient care and citizen collaboration.

Public sector: where it is applied and applied in education, internal and external security, relations with the citizen, etc.

Consumption: distribution, travel and lodging taking data from social networks.

Electronic commerce: integrating the large number of texts, images, clicks, etc. with profiles of clients to improve the effectiveness of electronic commerce.

Big Data Present and Future

In conclusion, the trends that can be seen for the coming years are divided into 4 pillars: cloud (cloud computing), social (social media), mobility (devices) and Big Data. In the following years, the concept of Big Data is expected to become popular in organizations and companies. By 2020, according to IDC forecasts, there will be 40 ZBs (zettabytes) of digital information across the globe and that information will have to be leveraged through Big Data.