What is Big Data?
Literally, these terms mean big data, big data or even massive data. They refer to a very large set of data that no conventional database management or information management tool can really work. Indeed, we procreate about 2.5 trillion bytes of data every day. Those are the information from everywhere : messages we send, videos we publish, information weather, GPS signals, transaction transaction records online and many more. These data are called Big Data or massive volumes of data. The giants of the Web, first and foremost Yahoo (but also Facebook and Google), were the first to deploy this type of technology.
However, no precise or universal definition can be given to Big Data. Being an object polymorphic complex, 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 tool providers (computer scientists), categories of users (managers, business managers, policy makers, researchers), health actors and users.
Big data does not derive rules from all technologies, it is also a dual technical system. Indeed, it brings benefits but can also generate disadvantages. Thus, it serves speculators in the financial markets, autonomously with the key, the constitution of hypothetical bubbles.
The arrival of Big Data is now presented by many articles like a new industrial revolution similar to the discovery of steam (beginning of 19e century), electricity (end of 19e century) and computer science (end of 20e century). Others, a little more measured, describe this phenomenon as being the last stage of the third industrial revolution which is actually “information”. In any case, Big Data is considered a source of profound upheaval of society.
Big Data: Mass Data Analysis
Invented by the giants of the web, Big Data is presented as a solution designed for allow everyone to access real-time access to giant databases. It aims to offer a choice of classic database and analysis solutions (Business Intelligence platform in SQL server …).
According to the Gartner, this concept brings together a family of tools that respond to a triple problematic called rule of 3V. These include a Volume considerable data to be processed, a great deal Variety information (from various sources, unstructured, organized, Open …), and a certain level of Velocity to achieve, in other words frequency of creation, collection and sharing of these data.
Technological developments behind Big Data
Technological creations that facilitated the coming and the growth of Big Data can be broadly categorized as two families : Firstly, storage technologiesparticularly as a result of the deployment of Cloud computing. On the other hand, the arrival of adjusted processing technologies, especially the development 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 for optimize processing times on giant databases namely the databases NoSQL (as MongoDB, Cassandra or Redis), server infrastructures for distributing processing on nodes and storing data in memory:
The first solution makes it possible to implement storage systems considered to be more efficient than traditional SQL for bulk data analysis (key / value oriented, document, column or graph).
The second is also called massively parallel processing. The Hadoop Framework is an example. This combines the distributed file system HDFS, the base NoSQL HBase and the algorithm MapReduce.
As for the last solution, it speeds up the processing time of requests.
Evolution of Big Data: the development of Spark and the end of MapReduce
Each technology belonging to the system mégadonnée, has its usefulness, its strengths and its disadvantages. Being a constantly evolving environment, Big Data always seeks to optimize the performance of the tools. So, its technological landscape moves very quickly, and new solutions are born very frequently, with the aim of further optimizing existing technologies. To illustrate this evolution, MapReduce and Spark are very concrete examples.
Described by Google in 2004, MapReduce is a pattern implemented later in Yahoo’s Nutch project, which will become the Apache Hadoop project in 2008. This algorithm has a large capacity in data storage. The only problem 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 leave MapReduce. In 2014, Google announced that it will be replaced by a SaaS solution called Google Cloud Dataflow.
Spark is also an iconic solution allowing to simply write distributed applications and offering classic processing libraries. Meanwhile, with a remarkable performance, it can work on data on disk or data loaded in RAM. He is younger, but he has a huge community. It is also one of the Apache projects with a fast development speed. In short, it’s a solution that turns out to be MapReduce’s successor, especially since it has the advantage of merging many of the necessary tools into a Hadoop cluster.
Continuous training in Big Data: what schools offer
Now, schools offer training in Big Data. Pedagogy wants to give a large part to case studies and feedback. It also highlights the “red threads”. These are business simulation projects that some large companies such as EDF or Capgemini offer.
This kind of training is not limited to a theoretical framework. Apprentices are also led to practice by reinforcing their training through an internship. To integrate these schools, you must be a holder of an engineering degree in computer science or telecommunications, or a scientific or technical university master’s degree in computer science or applied mathematics. They often accept bac +4 scientist provided that the person has at least 3 years of professional experience.
The interest of a digital training oriented Big Data
Increasingly, digital is becoming the cornerstone of every entity wishing to enter the highly modern job market. Companies are snapping up the rare data scientists graduates of schools and organizations delivering 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 growth of Big Data. The latter is therefore related to a major player in the sector. Many start-ups are born and integrate the process in the learning of its teams. The primary goal is to put smart data at the service of education.
Education is undergoing a transformation that began with the emergence of E-Learning. By involving 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 how best to exploit Big Data and its technological tools to promote education. With this in mind, Strategies training offers no less than 80 courses focused on the digital sector. Apprentices will be able to acquire or reinforce skills in terms of digital transformation, search marketing or social media.
Big Data, exclusively for marketing and sales functions?
This technology represents for everyone a privileged commercial issue given its ability to impact deep trade in the global economy integrated. Indeed, businesses, no matter how big they are, are among the first to enjoy the benefits obtained from a good manipulation of massive data.
However, big data also play a vital role in process transformation, of the supply chain, exchanges of Machine-to-Machine ” in order to develop a better “information ecosystem”. They also make decisions more swift and more credible, taking into consideration information that is internal but also external to the organization. In the meantime, they can be used to support risk management and fraud.
Before so much information, how to sort the wheat of the chaff?
As the old saying goes ” too much information is killing information “. This is actually the main problem with big data. The huge amount of information is one of the obstacles. The other obstacle obviously comes from the level of certainty that we can have on a datum.
Indeed, the data that flow from thedigital marketing can be considered as “uncertain” information for example, where we can not be sure who is clicking on an offer included in a URL. The volume of data associated with lack of credibility of these makes its exploitation more convoluted.
However, thanks to statistical algorithms, solutions exist. This is actually, even before wondering if it would possible to collect and store the big data that one should always start by asking oneself about one’s ability to analyze them and their usefulness.
With an appropriately determined purpose and data of sufficient quality, algorithms and statistical methods now allow to design value when it was not yet feasible just a few years ago. In this respect, we can distinguish they kind of schools in the prediction field to know artificial intelligence or “machine learning” and statistics. These two sectors, although they are distinct, finally come together more and more. In addition, they can be used simultaneously in a virtuous and intelligent way to carry out a project.
Where the use of big data in management becomes a vital issue for companies.
The future of Big Data
Being a heavy trend, Big Data is not a fashion. In the field of use, it satisfies a need to work the data more deeply, to create value, along with technological skills that did not exist in the past. However, given the evolution of technologies that does not seem to fade, we can not then speak of a real standard or standards in the field of Big Data.
Many applications of Big Data are just their preludes and we can expect to see appear uses that are not expected today. In a way, Big Data is a turning point for organizations at least as important as the Internet in its day. Every company must start now. If not, there is a risk that they will realize within a few years that they have been overtaken by competition. Governments and public bodies are also looking into the issue through open data.
Massive data: a booming global market
A few years from now, the big data market will be measured in hundreds of billions of dollars. It’s a new eldorado for business. According to studies, it is even a wave of substance where we find the combination of BI (business intelligence), analytics and the Internet of Things. IDC says it is expected to exceed $ 125 billion by the end of 2015. Indeed, several studies are flocking to this assertion and all confirm that the budgets that companies will spend on Big Data will only experience strong growth. Thus, nothing the market of visual solutions of information discoveries related to massive data management will grow 2.5 times faster than BI solutions by 2018.
According to the calculations made by the firm Vanson Bourne, in the world, all spending on Big Data, in IT budgets of large companies, should represent a quarter of the total IT budget in 2018 if he is still 18% currently. Cap Gemini also commissioned a study in March 2015. The result showed that 61% of companies are aware of the usefulness of Big Data as a “growth engine in its own right”. As a result, it is given much more importance than their existing products and services. This same study further indicated that 43% of them have already reorganized or are currently restructuring to exploit the potential of Big Data.