Introduction: Data processing applications have played a central role in the software industry since the inception of COBOL in 1959. Over the years, technologies have evolved, but the need to process ever-increasing volumes of data at faster speeds has remained critical. This article explores the evolution of data processing applications, focusing on transitioning from traditional relational database management systems (RDBMS) to modern Big Data management platforms.
The Foundation: COBOL and RDBMS: COBOL, the pioneering language designed in 1959, marked the first serious attempt at enabling data processing. Subsequently, RDBMS technology, spearheaded by Oracle's founding in 1977, achieved significant success in this domain. RDBMS systems, such as Oracle, SQL Server, PostgreSQL, MySQL, Teradata, and Exadata, have been widely used, offering features like SQL for data querying, scripting languages like PL/SQL and Transact SQL, and interfaces for other programming languages such as JDBC and ODBC.
The Shifting Data Landscape: While RDBMS systems have been the backbone of data processing applications for decades, the data landscape has undergone significant changes. Initially, data was primarily seen in rows and columns, stored in plain text files like CSV or advanced file formats like DBF used by RDBMS. However, the introduction of new data formats, such as JSON and XML, challenged the ability of RDBMS systems to store and process these semi-structured data types.
Structured, Semi-Structured, and Unstructured Data: The emergence of JSON, XML, and other formats led to categorizing data into three main types: structured, semi-structured, and unstructured. Structured data follows a standardized row-column table format. Semi-structured data, exemplified by JSON and XML, deviates from the tabular structure but adheres to well-defined key/value pairs. Unstructured data lacks predefined patterns and includes text files, PDFs, images, and videos.
The Need for Modern Data Processing: With the proliferation of the internet, social media, and mobile apps, organizations now collect vast amounts of semi-structured and unstructured data, surpassing the amount of structured data. However, RDBMS systems were not designed to handle the complexity and scale of unstructured data. Modern data processing applications must address three key challenges:
Handling structured, semi-structured, and unstructured data
Managing large data volumes in the terabytes and petabytes
Processing data at high velocities, as data is collected rapidly
The Big Data Problem: The combination of these challenges gives rise to the Big Data problem. While it matured over decades, RDBMS systems cannot handle Big Data effectively. So, the industry required a Big Data Management platform capable of storing high volumes of data arriving at high velocities, accommodating the variety of structured, semi-structured, and unstructured data, and processing the data at high velocities.
Conclusion: Data processing applications have continuously evolved to meet the growing demands of handling diverse data types, high volumes, and fast velocities. The transition from RDBMS systems to modern Big Data management platforms signifies the industry's response to the changing data landscape. By embracing these advancements, organizations can effectively store, process, and make sense of the vast amounts of data generated in today's digital age.
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