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Evolution of Big Data as mentioned earlier in part-1 of this blog series, Big data used without that coined name since we started moving into caves. The size and comprehension of data evolved as we as a human evolved in culture and technology. At any given time or situation, when the amount of data becomes too large, uncontrollable, and response, we build systems to interpret and analyze the data. We can say that even devices like Abacuses are instruments that help us analyze/calculate the data at hand.
However, the amount of data consumed by human society has risen beyond comprehension in the last two decades. And especially in the last decade, the data is rising through the roof, or in this case through the sky. The total amount of data in the world was 4.4Zettabyte (1zettabyte=roughly 1000 exabytes, 1 exabyte = 1000 petabytes, and 1 petabyte = roughly 1000 terabytes) in 2013 and is expected to rise to 44Zettabyte in 2020, which we crossed easily.
And expected to reach 175 Zettabyte in 2025. The comprehension of such a huge data volume is impossible without the development of the technology both to interpret and consume. Even with such increasingly advanced technologies, it is impossible to process all these data. The evolution of Big Data is explained in three phases.
Structured Data
The Database management system is the origin of Big Data and Data analytics. The techniques like storage, extraction, and optimization techniques used in Relational Database Management System were relied upon heavily by the Database management at that time. The first phase of Big Data evolution consisted of database management and database warehousing.
Modern data analytics later formed as an evolution of the database management system. At the time it used techniques like database queries, database processing, and reporting tools. Modern, complex big data solutions often use non-relational databases in their architecture and require a comprehensive quality assurance approach.
HTTP Based Data
The inception of the Internet and WWW started introducing vast new and unique opportunities in terms of data collection and analyses. The commercialization of personal computers by companies like Microsoft, Apple, and IBM, etc., and the availability of internet by internet providers made it easy for more people to access the internet which increased the web traffic through the roof.
This increase in web traffic brought new types of data collected and analyzed for various purposes. Search engines like Google, Yahoo, etc. helped collect data about trends in various industries. Similarly, the birth of social media platforms such as Facebook, Twitter, etc helped companies collect and analyze data about public behavior, consumer behavior, interests, etc. Thus, the opportunity to collect new types of data and resulting analyses opened possibilities beyond comprehension.
This massive increase in the amount of data by the HTTP-based web traffic was mostly semi-structured and unstructured data from a data analytics point of view. Due to this nature of data, the organizations needed to figure out new techniques to store, interpret, and analyze these new data types. The need to interpret the vast amount of data from social media platforms and eCommerce websites. They then convert them into meaningful information and became the need of the hour.
Sensor-Based Data
Many organizations in data analytics consider these semi/unstructured data as the focus. New opportunities to retrieve important data from mobile devices have created a whole new world of possibilities. The third phase of Big Data is dominated by biometric data by IoT devices. Devices like wearable activity trackers allow companies to track health-related data. Along with the user location tracking allows them to analyze much new useful information. Because of these internet-based sensor devices, the data generation is on a different level.
The sensors are embedded in all forms of machines. From daily appliances like washing machines, and refrigerators to cars, trucks to even warehouses to track the inventories. The possibilities of use of these data are endless. The best part is we have only begun to extract/analyze the information from these sources.
Conclusion
Therefore, Data is the most important and powerful commodity in the modern world. At PromptCloud, we do our part in this evolution by servicing companies that require web-based data. We provide fully managed, enterprise-grade, end-to-end web scraping solutions. Make sure to stay tuned into this 3 part series on the evolution of Big Data and its advancements in the last and final part which will be soon published.
