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In the history of big data, no one knows exactly how the term Big Data originated. It has been used since the 1990s. John R. Mashey, a Silicon Graphics professional, is credited with popularizing the term. It may surprise many that Big Data is not a term coined in recent years. Data analysis and techniques related to analysis were used by people over the course of centuries to help them make better decisions. The speed and volume of data generation have increased incredibly over the last two decades. It is now reached a level where it has sprung beyond measures of human comprehension.

Data analysis, analytics, and the concept of Big Data are all connected to data management. They rely on various features and techniques, including storing, extracting, and optimizing data stored in Relational Database Management Systems (RDBMS).

The key components in the first phase were database management and data warehousing. They provided the base for the further development of modern data analysis.

The process of advanced data collection and data analysis features began in early 2000. During this period, web traffic and online stores started proliferating. Leading organizations dealing with Big Data initiated the detailed analysis of customer behavior by researching aspects such as search logs, click rates, and location data. It also opened up a whole new world of possibilities.

HTTP-based web traffic helped drive efforts in proper analysis and storage of semi-structured and unstructured data. Organizations were keen to find solutions for the storage and analysis of standard structured data as well as new data types so that they could be analyzed efficiently. Additionally, the rapid growth of social media data greatly intensified the need for the right tools, techniques, and technologies to extract meaningful information from this unstructured data.

Organizations have to deal with the exceptional challenges posed by web-based unstructured content for data analysis, data analytics, and big data. An answer to this problem seems to emerge from mobile devices.

Mobile devices have the technology to analyze behavioral data. It also allows for storing and analyzing location-based data (GPS data). With mobile devices becoming smarter by the day, tracking various aspects of human behavior and arriving at definite conclusions is possible.

With internet speeds improving, making it possible to spew data at exponentially faster rates, the stage was set for the next giant leap in Big Data history.

With the introduction of the World Wide Web and the development of HTML, URLs, and HTTP, access to data became relatively easy and decidedly faster. In 1996, digital storage became an affordable way of storing information compared to storing data on paper. In addition the search engine system took shape in 1997 with the registration of Google as a domain name. The development of several other tech innovations also took place alongside. These included areas of machine learning, big data, and analytics. In 1999, a book published by Hal R. Varian and Peter Lyman made efforts to quantify the volume of digital information available across the globe.

The real big change in Big Data happened in the 21st century. Doug Laney from Gartner coined the term 3Vs of Big Data. He defined how volume, velocity, and variety impacted Big Data. Since then, other Vs., such as veracity, value, and variability, are also used in the Big Data context.

2005 saw the creation of Apache Hadoop, the open-source framework, by scientists Doug Cutting and Mike Cafarella. This framework is used to store and process large data sets. Soon after, in 2006, Amazon Web Services (AWS) began its web-based computing infrastructure services (cloud computing). It is also a dominant name in the current cloud services industry.

Some of the key developments that happened during this era are:

Edge computing is a new technology that defines the data management process for critical sectors of the economy. This kind of computing is done near the source of data collection rather than in the cloud or a centralized data center.

The explosive increase in the use of connected devices, the dependence on the cloud, and the upcoming edge computing revolution have played important roles in the growth of Big Data. Enhanced use of technologies such as Machine Learning, Artificial Intelligence, and IoT analytics have also contributed to the ability to process and analyze data. Over the years, we can expect major developments in Big Data, which will help accelerate the analytics process and boost the efficiency and ease of use of tools for leveraging Big Data.

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A Quick Guide To The History of Big Data - Baseline