in the course 4 of the big data data analyst , we will focus on big data which is a crucial element in data analyst
The Big Data phenomenon
The quantitative explosion of digital data has forced researchers to find new ways of seeing and analyzing the world. It is about discovering new orders of magnitude in the capture, search, sharing, storage, analysis and presentation of data . Thus was born “ Big Data ”. It is a concept for storing an indescribable amount of information on a digital basis. According to the archives of the Association for Computing Machinery (or ACM) digital library in scientific articles regarding the technological challenges of visualizing “large data sets”, this name appeared in October 1997.
Big Data, what is it?
Literally, these terms mean big data , large data or big data . They refer to a very large set of data that no conventional database management or information management tool can really work with. In fact, we procreate about 2.5 trillion bytes of data every day . It’s the information coming from everywhere : messages we send each other, videos we post, weather information , GPS signals, online shopping transaction records and much more. This data is called Big Data or massive volumes of data. The giants of the Web, foremost among which Yahoo ( but also Facebook and Google), were the very first to deploy this type of technology.
However, no precise or universal definition can be given to Big Data . Being a polymorphic complex object , its definition varies according to the communities that are interested in it as a user or a service provider. A transdisciplinary approach makes it possible to understand the behavior of the different actors: the designers and suppliers of tools (IT specialists), the categories of users (managers, business leaders, political decision-makers , researchers), health actors and users.
Big data does not derive from the rules of all technologies, it is also a dual technical system . Indeed, it brings benefits but can also generate disadvantages . Thus, it is used by speculators on the financial markets, independently, with the creation of hypothetical bubbles.
The advent of Big Data is now seen by many items like a new industrial revolution similar to the discovery of steam (early 19 th century), electricity (late 19 th century) and IT ( late 20 th century). Others, a little more measured, qualify this phenomenon as being the last stage of the third industrial revolution , which is in fact that of “information”. In any case, Big Data is considered to be a source of profound upheaval in society .
Big Data: Bulk Data Analysis
Invented by the giants of the web, Big Data is presented as a solution designed to allow everyone to access giant databases in real time . It aims to offer a choice to classic database and analysis solutions (Business Intelligence platform in SQL server, etc.).
According to Gartner, this concept brings together a family of tools that respond to a triple problem known as the 3V rule. These include a considerable volume of data to process, a large variety of information (coming from various sources, unstructured, organized, open, etc.), and a certain level of velocity to be reached, in other words frequency. creation, collection and sharing of this data.
Technological developments behind Big Data
The technological creations that have facilitated the arrival and growth of Big Data can be broadly categorized into two families : on the one hand, storage technologies , particularly driven by the deployment of Cloud Computing . On the other hand, the arrival of adjusted processing technologies , especially the development of new databases adapted to unstructured data (Hadoop) and the development of high performance computing modes (MapReduce) .
There are several solutions that can come into play to optimize processing times on giant databases, namely NoSQL databases (such as MongoDB , Cassandra or Redis ), server infrastructures for distributing processing to nodes and data storage in memory:
The first solution makes it possible to implement storage systems considered to be more efficient than traditional SQL for mass data analysis (key / value oriented, document, column or graph).
The second is also called massively parallel processing. The Hadoop Framework is one example. This combines the HDFS distributed file system , the NoSQL HBase database and the MapReduce algorithm .
As for the last solution, it speeds up the processing time of requests.
Evolution of the Big Data: the development of Spark and the end of MapReduce
Each technology, belonging to the big data system , has its uses, its advantages and its disadvantages . Being an environment in perpetual evolution, Big Data always seeks to optimize the performances of the tools. Thus, its technological landscape is moving very quickly, and new solutions are born very frequently , with the aim of further optimizing existing technologies. To illustrate this evolution, MapReduce and Spark represent very concrete examples.
Described by Google in 2004 , MapReduce is a pattern subsequently implemented in Yahoo’s Nutch project, which will become the Apache Hadoop project in 2008. This algorithm has a large data storage capacity . The only catch is that it is a bit slow. This slowness is particularly visible on modest volumes. Despite this, the solutions, wishing to offer almost instantaneous treatments on these volumes, are starting to abandon MapReduce. In 2014, Google therefore announced that it will be succeeded by a SaaS solution called Google Cloud Dataflow.
Spark is also an emblematic solution for easily writing distributed applications and offering classic processing libraries . Meanwhile, with remarkable performance, it can work on data on disk or data loaded in RAM . Sure, he’s younger, but he has a huge community. It is also one of the Apache projects with a fast development speed. In short, it is a solution that turns out to be the successor of MapReduce , especially since it has the advantage of merging a large part of the necessary tools into a Hadoop cluster.
The main players in the market
The Big Data sector has attracted several. The latter quickly positioned themselves in various sectors. In the IT sector , we find the historical suppliers of IT solutions such as Oracle, HP, SAP and IBM . There are also the actors of the Web including Google, Facebook, or Twitter. As for specialists in Data and Big Data solutions , we can cite MapR, Teradata, EMC or Hortonworks. CapGemini, Sopra, Accenture and Atos are integrators, always major players in big data. In the analytics sector , as BI editors , we can cite SAS, Micro-strategy and Qliktech. This sector also includesproviders specializing in analytics such as Datameer or Zettaset. Along with these main participants, many SMEs specializing in Big Data have appeared, across the sector’s value chain. In France, the pioneers were Hurence and Dataiku for Big Data equipment and software ; Criteo, Squid, Captain Dash and Tiny Clues for data analysis and Ysance for consulting.
Continuing education in Big Data: what the grandes écoles offer
Now, large schools offer training in Big Data . The pedagogy wants to give a large part to case studies and feedback. It also highlights the “red threads”. These are professional simulation projects that some large companies such as EDF or Capgemini offer.
This kind of training is not limited to a theoretical framework . The apprentices are also brought to practice by reinforcing their training through an internship. To join these schools, you must have an engineering degree in computer science or telecommunications, or a master’s degree in science or technology, computer science or applied mathematics. They often accept the scientific bac +4 provided that the person has at least 3 years of professional experience.
The interest of a digital training focused on Big Data
More and more, digital is illustrated as the cornerstone of each entity wishing to break into the now very modern employment market. Companies are snapping up the rare data scientists who have graduated from schools and organizations providing digital training. They justify their approach on the principle that data analyzes have the capacity to optimize a profile thanks to the advent of digital technology and the rise of Big Data . The latter is therefore akin to a major player in the sector. A number of start-ups are born and integrate the process into the learning of its teams. The primary objective is to put intelligent data at the service of education.
Education is undergoing a real transformation which began with the emergence of E-Learning . By including Big Data in their strategy, companies guarantee the competitiveness of their brand and optimize the follow-up of their customers. In addition, researchers are gradually working to dissect the best way to use Big Data and its technological tools to promote education. Based on this observation, Stratégies formations offers no less than 80 training courses focused on the digital sector. Apprentices will thus be able to acquire or strengthen skills in terms of digital transformation, search marketing or social media. Find the module that suits you Comundi.fr, click on the following link to see the site.
Data Scientist: THE business of Big Data
Responsible for the management, analysis and exploitation of big data in companies, the job of Data Scientist is among the 25 best jobs in the world according to a study by the job site Glassdoor. He represents the evolution of the Data Analyst and is highly sought after today for his specialized skills. Indeed, this position of high responsibility requires a high level of education on the subject and requires very specific knowledge.. These will allow you to acquire the necessary tools to be successful in this profession of the future. This includes the study of statistics, mastery of different programming languages through notions of machine learning. For your information, the average salary for a Data Scientist in the US in 2020 was $ 110,000 .