Data handling became tedious only after huge amounts of it started to accumulate. For example, in 1880, the US Census Bureau needed to handle the 1880 Census data. They realized that compiling this data and converting it into information would take over 10 years without an efficient system. It was at this time that Tabulating Machine was created. This machine completed the task in just a few months. Thus, it is no wonder that the origin of big data is a topic many big data professionals like to explore.
The historical development of big data, in one form or another, started making news in the 1990s. Though it assumes different meanings in different contexts, most organizations use it extensively. The history of big data takes people on an astonishing journey of big data evolution, tracing the timeline of big data.
The Emergence of Data Storage and Processing Technologies
A data storage facility first appeared in the form of punch cards, developed by Basile Bouchon to facilitate pattern printing on textiles in looms. While punch cards were designed in the 1720s, Charles Babbage introduced the Analytical Engine in 1837, a calculator that used the punch card mechanism to process data. Herman Hollerith, a US Bureau employee, developed the analytical engine and strengthened its capacity to store data. This device came to be known as Hollerith Tabulating Machine.
Magnetic tapes were the next step in data storage. Fritz Pfleumer formally registered these as data storage devices and patented them in 1928. After this, several advancements took place until the ENIAC, an electronic digital computer, was developed and used from 1946 to 1955. As ENIAC faded away due to controversies, transistors were invented.
Other data storage devices included hard disk drives, floppy disks, USB flash drives, CR-ROMs, VHS, memory sticks, SD cards, etc. Some of these are now entirely obsolete. Gradually, data storage and processing systems evolved, and today, we see it in one of its most advanced forms, the cloud.
Early Challenges and Limitations in Data Handling
The history of data management in big data can be traced back to manual data processing—the earliest form of data processing, which makes data handling quite painful. These systems hamper data handling to a great extent because errors usually persist. Moreover, the time required to complete the jobs may result in excessive resource usage. Due to the lack of faster data processing systems, machine-based alternatives were introduced. These included calculators, typewriters, printing machines, etc. Such systems are still used today, especially when small, independent tasks need to be executed.
Collating, assimilating, storing, retrieving, and sharing data are crucial activities that affect operations. For example, talking about the history of big data in healthcare, hospitals faced many problems earlier in patient data management, security, and privacy. A hospital’s performance depends largely on how patient data is handled, including accessing and retrieving it for various purposes . Yet, patient data handling was quite a problem earlier. Today, systems that can manage large datasets have eliminated many historical challenges.
Evolution of Distributed Computing
Early computers were designed to handle simple tasks, such as calculations. Gradually, the scope of operations a computer could perform widened. Distributed computing was a key milestone in this journey. If one studies the history of big data innovation, the role of distributed computing in computer science becomes clear. It is an arrangement where several computers operate together to produce computing results. These computers can be on the same premises or in remote locations.
Distributed computing has come a long way, from mainframe computers used between 1960 and 1967 to today’s technologies that include edge computing, IoT, and the cloud. As the mainframe system of communication and sharing depended heavily on the client-server architecture, it was quite expensive to operate.
Cluster networks emerged in the 1970s, but the operating costs of these machines were high like the mainframes. Under this system, workstations or PCs (mostly of the same configuration) connected by LAN were used. The times of the internet and personal computers brought TCP/IP technology to the fore. This was used for many years.
The World Wide Web (WWW) became popular in the 1990s. It enabled users to independently connect to the internet, but information sharing remained slightly tricky even during this period due to centralization. Hence, Peer-to-peer Computing was introduced for decentralization, and middleware ensured interaction between specific computers, called the Data Grid.
Today, cloud, mobile, and IoT have addressed several problems seen with all the earlier technologies and systems described above.
The Era of Big Data
In the era of big data, some of the most serious problems facing us are maintaining data quality and setting up contemporary infrastructure for data ingestion from various sources. Insights can be generated and extracted from large datasets only when the original data is properly stored, transformed, analyzed, and presented in a comprehensible format. Once analytics processes are established, leveraging big data to enhance profitability levels becomes an achievable goal.
Many organizations have extensive data related to sales, customers, vendors, people, process performance, etc. When analyzed, this data creates varied patterns and highlights different industry trends based on their operational needs. Each organization will require customized big data analytics systems and people who can operate them.
You may wonder what this means for you. Online Big Data courses will help you find some of the most lucrative opportunities and stay relevant in the field.
Big Data Technologies in the Early 2000s
Big data technologies comprise software programs and systems that help analyze and process data gathered in large volumes. Without big data technologies, such data will likely never be entirely processed, which means vital information will remain inaccessible. This is not an ideal scenario for businesses that wish to expand and grow. As a remedy to such problems, several significant advancements were initiated.
In 2001, Doug Laney defined big data and highlighted its features. A few years later, Doug Cutting and Mike Cafarella made a groundbreaking development in the form of Apache Hadoop, a system that processed data in huge amounts. With the launch of Amazon Web Services (AWS), the scenario changed completely, and cloud computing became available to enterprises. To understand how these technologies work, it is important to study their types.
Big data technologies can be categorized into four main types—Storage, Analytics, Mining, and Visualization. Software utilities under each type perform specific functions to contribute to the larger picture of big data management.
Big Data and Social Media together are a potent combination—these powerful tools govern the world. Big data does what regular processing systems and DBMS cannot do, and social media brings the world closer.
Both offer unique, almost irreplaceable services. Moreover, social media platforms generate high volumes of data, which demands big data analytics. For example, think about the targeted ads personalized for users. These are based on user searches, location, gender, etc. How do companies secure this information? Social media mining is the answer. It gives them real-time information that can be used to engage audiences and grow the customer base.
In every way imaginable, social media and big data are interlinked and will continue to grow stronger in the foreseeable future.
Rise of the Cloud and Big Data
While virtual systems were seen before 2006, cloud computing took off with the launch of Amazon Web Services. It brought about a sea of change in IT, communications technologies, and enterprise solutions. Since then, the use of cloud services for big data has increased with each passing day.
Shifting from on-premises data management is highly recommended to improve productivity and reduce operational expenditure. Moreover, on-demand computing services strengthen an organization’s security and privacy protocols. For instance, companies that deployed Hadoop to merge big data technologies and cloud computing were successful in creating flexible, secured, and scalable computing ecosystems.
Continuing the trend, many large corporations launched cloud-based services. The five main categories are Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), Anything/Everything as a Service (XaaS), and Function as a Service (FaaS). Cloud computing makes big data technologies affordable and convenient.
Current Trends and Future Directions
Digitization and digitalization are the present and future of every industry. The steady and continuous launch of e-commerce platforms to sell a wide variety of products and services does not surprise anyone today.
Some of the technological changes have positively impacted data integrity and service delivery quality. For example, many industries use predictive analysis to determine their business strategies and manage risks.
Technologies to Watch
Modern, advanced technologies have changed the way we work. They will likely continue to evolve and improve our computing environments. As a consequence, the general quality of online interactions, data sharing, online collaboration, remote working, online educational services, etc., will improve. The following is not an exhaustive list of the many technologies we have today:
- Artificial Intelligence and Machine Learning
- Natural Language Processing (NLP)
- IoT
- Quantum Computing
- Robotics
Each of these technology ingests, uses, and generates data. In return, it facilitates data management, enhances decision-making abilities, and offers companies a competitive edge.
Conclusion
The future of big data is as interesting as the history of big data. Starting with a modest invention of a simple calculator, we have reached artificial intelligence and big data processing systems. Big data trends seen today promise positive impacts on industries, education, commerce, and international trade.
Data privacy and security, a concern many firms and individuals regularly voice, can be addressed better with such newer technologies. Enroll in KnowledgeHut online Big Data courses and build a robust skill-set working with the most powerful big data tools and technologies.
