What is Big Data?

Big Data is a term that represents large amounts of data. This data is most of the time too voluminous and complex and therefore cannot be analyzed by traditional IT tools. They come from different sources such as online transactions, social networks and connected devices. Big Data is mainly used in the discovery of new models or hidden information at the heart of data, which can be used in decision-making and in improving operations.

The birth of Big Data

The term Big Data emerged during the 2000s, when companies began massive production and collection of digital data. Despite this movement by companies in the 2000s, it was already possible to see that since the 1960s, some scientists and companies were already using computers to process huge quantities of data. With the constant development of the Internet, it has become relatively easy and profitable to store and process large quantities of data.

The main characteristics of Big Data

The volume

Big Data is characterized by a gigantic volume of data, which can reach tens of terabytes or petabytes.

Variety

Big Data constitutes different types of data: we find structured data (recorded in a database), semi-structured data (such as XML or JSON files for example) and unstructured data (such as emails and chat messages).

Velocity

Big Data is generated at phenomenal speed, which requires processing tools available in real time in order to be able to extract useful information.

Value

Big Data contains very valuable data for businesses because it provides insights into their customers, products and operations. Once this data is collected, it allows you to make better decisions and thus improve performance.

Variability

In some cases, the structure of Big Data can be unstable and inconsistent. This instability can prove harmful in the management and analysis of Big Data.

Big Data processing and analysis tools

Hadoop

Hadoop is an open source framework that allows you to store and process large amounts of data on a cluster of servers. Its objective is to manage large amounts of data in a distributed and parallel manner, ideally suited to Big Data.

Hadoop is made up of different components such as:

• HDFS (Hadoop Distributed File System): it is a file system sent from Hadoop which ensures the storage of data on the cluster in a distributed manner.
• YARN (Yet Another Resource Negotiator): it is a Hadoop resource manager that manages the execution of various tasks on the cluster.
• MapReduce: MapReduce is a Hadoop data processing algorithm. This ensures parallelism in data processing.

Spark

Apache Spark is an open source real-time computing engine that processes large amounts of data at very high speed. It is both fast and flexible, since it is capable of processing data in a distributed and parallel manner.

Spark is versatile since it is used for many processing tasks such as real-time analysis, data transformation and machine learning.

Flink

Apache Flink is also an open source real-time computing engine. However, it allows processing of streaming data at high performance. Its design gives it exemplary speed and reliability allowing it to process data in a distributed and parallel manner on a cluster of servers.

The use of Flink arises when processing streaming data, such as real-time analysis, data transformation and processing of continuous data streams.

Hive

Apache Hive is a data management tool which, in conjunction with Apache Hadoop, allows you to work with large quantities of data. Hive has a SQL interface for analyzing data, so it is a tool accessible to regular SQL users. This tool transforms SQL queries into MapReduce tasks, thus guaranteeing certain efficiency in data processing.

Pig

Apache Pig is a data manipulation tool that offers syntax close to SQL language. Pig transforms queries written in its own syntax into running MapReduce tasks, allowing it to efficiently process large amounts of data.

Many processing and analysis tools are offered by the Apache Foundation, a non-profit organization that supports the open source ecosystem. Created in 1999, the foundation now has more than 350 different open source projects and has been recognized for the quality of its big data management and analysis tools.

The contributions of Big Data

Optimize business operations

By analyzing large amounts of data, companies can better understand customer spending habits and adapt their strategies accordingly.

Improve the quality of products and/or services

By using Big Data, companies have the opportunity to identify potential quality problems with their products and/or services and correct them more quickly.

Optimize the supply chain

With in-depth analysis of different stock levels and demand patterns, companies improve their supply planning and thus reduce the costs linked to possible shortages.

Improve decision making

With accurate and up-to-date data, companies now have all the elements necessary for rapid and informed decision-making.