blog 3 (historical development of Big data)

 

Blog 3: The Early Beginnings of Big Data (1960s–1980s)

The history of big data started long before the term “big data” became popular. During the 1960s and 1970s, businesses and governments began using computers to store large amounts of information. At that time, data was mainly stored using punch cards and magnetic tapes. These systems were slow and had very limited storage capacity compared to modern technology.

One major development during this period was the creation of relational databases. In 1970, computer scientist Edgar F. Codd introduced the relational database model while working at IBM. This model allowed organizations to store data in tables, making it easier to organize and retrieve information. This innovation became the foundation for modern database management systems.

During the 1980s, businesses began using computers more frequently for record keeping. Banks stored customer transactions digitally, hospitals kept patient records electronically, and governments managed census information using databases. Although the amount of data was much smaller than today, organizations were already facing challenges related to storage and processing speed.

Data during this era was considered “structured data,” meaning it was neatly organized into rows and columns. There was very little unstructured data such as videos, images, or social media content because the internet had not yet become widely available.

These early developments laid the foundation for big data technologies used today. Without relational databases and early storage systems, modern data analysis would not exist.




Comments

Post a Comment

Popular posts from this blog

Blog 1 Big Data: Transforming Information into Knowledge

 Benefits of Big Data and How It Defines Me Big Data helps businesses and organizations make better decisions by analyzing large amounts of information to find patterns and trends. It also improves customer experience by allowing companies to offer personalized products and services. For example, companies like Amazon and Netflix use Big Data to recommend products and movies based on users' preferences. Big Data also supports innovation and efficiency by helping organizations improve services, develop new products, and reduce costs. How Big Data Defines Me Big Data affects my daily life by providing personalized recommendations when I search online, watch videos, or use social media. It also helps me access information quickly and learn new things. Overall, Big Data shapes how I use technology and interact with information every day.
Read more

Big data Blog 37 to 39

 Blog 37: The Hidden Limitations of Predictive Analytics in Modern Business Predictive analytics has become a cornerstone of data‑driven decision‑making. Companies rely on it to forecast sales, detect fraud, optimize supply chains, and personalize customer experiences. But despite its power, predictive analytics is far from perfect — and understanding its limitations is essential for using it responsibly. 1. Data Quality Issues Predictive models are only as good as the data they learn from. Incomplete, outdated, or biased datasets can lead to inaccurate predictions. When historical data contains errors, the model simply amplifies them. 2. Overfitting and Model Rigidity Some models perform extremely well on training data but fail in real‑world scenarios. This happens when the model becomes too tailored to past patterns and cannot adapt to new conditions. 3. Unpredictable Human Behavior Human decisions, emotions, and social trends can shift rapidly. Predictive analytics struggles wit...
Read more

Big Data 33

Blog 33: High-Capacity Data Storage Systems One of the most important technological requirements of big data is having enough storage capacity to handle extremely large amounts of information. Traditional storage systems are often unable to manage the massive volume of data generated every second from social media platforms, online shopping websites, healthcare systems, and smart devices. As a result, organisations require advanced storage technologies that can store structured, semi-structured, and unstructured data efficiently. Cloud storage has become one of the most popular solutions for big data storage. Companies such as Amazon Web Services , Google Cloud , and Microsoft Azure provide scalable storage services that allow businesses to expand their storage space whenever needed. This flexibility reduces costs because companies only pay for the storage they use. Distributed storage systems are also essential. Technologies such as Hadoop Distributed File System (HDFS) divide large...
Read more