Big Data: Definition, technologies, uses and training

Big Data refers to the large amount of data collected by companies in all industries, analyzed to derive valuable insights. Find out everything you need to know about the subject.

What is called Big Data?

Before defining Big Data it is important to understand what Data is. This term defines quantities, characters or symbols that are operated on by a computer. Data can be stored or transmitted as electrical signals and recorded on a mechanical, optical or magnetic medium. The term Big Data refers to large sets of data collected by companies that can be mined and analyzed to derive actionable information or used for Machine Learning projects. Big Data is often defined by the “3 V’s” that characterize it: the Volume, Variety of data, and the Velocity with which it is generated, collected and processed. This is what differentiates “Big data” from traditional data. These three characteristics were first identified in 2001 by Doug Laney, an analyst for Meta Group Inc. and were later popularized by Gartner following its acquisition of Meta Group in 2005. Today, other characteristics are sometimes attributed to Big Data such as veracity, value and variability

What is Big Data used for?

Companies in all industries are using Big Data in their systems for a variety of purposes. This can include improving operations, providing better customer service, creating personalized marketing campaigns based on consumer preferences, or simply increasing revenue. With Big Data, companies can achieve a competitive advantage over their non-data-driven competitors. They can make faster and more accurate decisions based directly on the information acquired.  For example, a company can analyze Big Data to uncover valuable information about its customers’ needs and expectations. This information can then be used to create new products or targeted marketing campaigns to increase customer loyalty or conversion rates. A company that relies entirely on data to guide its evolution is called “data-driven“. Big Data is also used in the field of medical research. In particular, it allows the identification of risk factors for diseases, or to make more reliable and accurate diagnoses. Medical data can also be used to anticipate and track potential epidemics. Big data is used in almost every sector without exception. The energy industry uses it to discover potential drilling areas and monitor their operations or the power grid. Financial services use it to manage risk and analyze market data in real time. Manufacturers and transportation companies manage their supply chains and optimize their delivery routes with data. Similarly, governments are leveraging Big Data for crime prevention or Smart City initiatives.

What are the sources of Big Data?

Big Data can come from a wide variety of sources. Common examples include transaction systems, customer databases, and medical records. Similarly, Internet user activity generates a myriad of data. Click logs, mobile applications, and social networks capture a lot of information. The Internet of Things is also a source of data thanks to their sensors, whether they are industrial machines or “consumer” connected objects such as bracelets dedicated to sports activity. To better understand, here are some concrete examples of Big Data sources. The New York Stock Exchange alone generates about one terabyte of data per day. This is huge, but it is nothing compared to social networks. For example, Facebook ingests over 500 terabytes of new data into its databases every day. This data is mainly generated by photo and video uploads, message exchanges and comments left under posts. In just 30 minutes of flight, a single aircraft engine can generate more than 10 terabytes of data. As you can see, Big Data is now flowing in from multiple sources and the data is getting bigger and bigger as technology advances…

What are the different types of Big Data?

Big Data comes from a variety of sources, and can therefore take many forms. There are several main categories. When data can be stored and processed in a fixed and well-defined format, it is called “structured” data. Thanks to the many advances made in the field of information technology, techniques now make it possible to work efficiently with this data and to extract all its value. However, even structured data can be problematic because of its massive volume. As the volume of a dataset now reaches several zettabytes, storage and processing represents a real challenge. Data with unknown format or structure, on the other hand, is considered “unstructured” data. This type of data presents many challenges in terms of processing and exploitation, beyond its massive volume. A typical example is a heterogeneous data source containing a combination of text, image and video files. In the digital and multimedia age, this type of data is increasingly common. As a result, companies have vast amounts of data at their fingertips, but struggle to take advantage of it because of the difficulty of processing this unstructured information… Finally, “semi-structured” data is halfway between these two categories. For example, it can be data that is structured in terms of format, but not clearly defined within a database. Before unstructured or semi-structured data can be processed and analyzed, it must be prepared and transformed using various types of data mining or data preparation tools.

What are the techniques for analyzing Big Data?

Different techniques are used to analyze Big Data. Here are some examples. Benchmarking, for example, allows a company to compare the performance of its products and services with those of its competitors. Marketing analytics is about analyzing data to promote new products and services in a more informed and innovative way. Sentiment analysis aims to evaluate customer satisfaction with a brand, notably by reviews or comments left on the internet. In the same way, social network analysis allows highlighting the reputation of a company based on what Internet users say about it on the networks. It then becomes possible to identify new target audiences for marketing campaigns.

How is Big Data processed and stored?

The volume, velocity and variety of big data implies specific IT infrastructure requirements. A single server or even a cluster of servers will quickly be overtaxed by Big Data. To achieve sufficient processing power, it may be necessary to combine thousands of servers to distribute the processing work. These servers must collaborate within a cluster architecture, often based on dedicated technologies such as Hadoop or Apache Spark. The costs can be very high, which is why many business leaders are reluctant to invest in infrastructure that is suitable for storing and processing Big Data workloads. As an alternative, many organizations are turning to the public cloud. Today, it is the preferred solution. That’s why the growth of cloud computing has accompanied the growth of Big Data. A public cloud provider can expand its storage capacity unlimitedly according to its customers’ Big Data processing needs. The company pays for the resources it uses. So there are no capacity restrictions, and no unnecessary expenses. Among the most widely used cloud storage solutions for Big Data are Hadoop Distributed File System (HDFS), Amazon Simple Storage Service (S3), or various relational or NoSQL databases. Beyond storage, many public cloud providers offer Big Data processing and analysis services. We can mention Amazon EMR, Microsoft Azure HADInsight or Google Cloud Dataproc. However, there are also Big Data solutions designed for on-premises deployments. These solutions generally use open source Apache technologies in combination with Hadoop and Spark. Examples include the YARN resource manager, the MapReduce programming framework, the Kafka data streaming platform, the HBase database and SQL query engines such as Drill, Hive, Impala or Presto.

How to learn about Big Data?

Processing and exploiting Big Data requires mastery of the various tools and techniques discussed in this report. These skills are highly soughted by companies in all sectors, as many organizations want to take advantage of the data at their disposal. To learn the different professions of Big Data, you can choose Liora training courses. We offer different training courses enabling you to quickly become a Data Scientist, Data Analyst, Data Engineer or Machine Learning Engineer. Don’t wait any longer and discover our training courses now.