1. Traditional data: Traditional data is the structured data that is being majorly maintained by all types of businesses starting from very small to big organizations. In a traditional database system, a centralized database architecture used to store and maintain the data in a fixed format or fields in a file. For managing and accessing the data Structured Query Language (SQL) is used.
Traditional data is characterized by its high level of organization and structure, which makes it easy to store, manage, and analyze. Traditional data analysis techniques involve using statistical methods and visualizations to identify patterns and trends in the data.
Traditional data is often collected and managed by enterprise resource planning (ERP) systems and other enterprise-level applications. This data is critical for businesses to make informed decisions and drive performance improvements.
2. Big data: We can consider big data an upper version of traditional data. Big data deal with too large or complex data sets which is difficult to manage in traditional data-processing application software. It deals with large volume of both structured, semi structured and unstructured data. Volume, Velocity and Variety, Veracity and Value refer to the 5’V characteristics of big data. Big data not only refers to large amount of data it refers to extracting meaningful data by analyzing the huge amount of complex data sets. semi-structured
Big data is characterized by the three Vs: volume, velocity, and variety. Volume refers to the vast amount of data that is generated and collected; velocity refers to the speed at which data is generated and must be processed; and variety refers to the many different types and formats of data that must be analyzed, including structured, semi-structured, and unstructured data.
Due to the size and complexity of big data sets, traditional data management tools and techniques are often inadequate for processing and analyzing the data. Big data technologies, such as Hadoop, Spark, and NoSQL databases, have emerged to help organizations store, manage, and analyze large volumes of data.
The main differences between traditional data and big data as follows:
- Volume: Traditional data typically refers to small to medium-sized datasets that can be easily stored and analyzed using traditional data processing technologies. In contrast, big data refers to extremely large datasets that cannot be easily managed or processed using traditional technologies.
- Variety: Traditional data is typically structured, meaning it is organized in a predefined manner such as tables, columns, and rows. Big data, on the other hand, can be structured, unstructured, or semi-structured, meaning it may contain text, images, videos, or other types of data.
- Velocity: Traditional data is usually static and updated on a periodic basis. In contrast, big data is constantly changing and updated in real-time or near real-time.
- Complexity: Traditional data is relatively simple to manage and analyze. Big data, on the other hand, is complex and requires specialized tools and techniques to manage, process, and analyze.
- Value: Traditional data typically has a lower potential value than big data because it is limited in scope and size. Big data, on the other hand, can provide valuable insights into customer behavior, market trends, and other business-critical information.
Some similarities between them, including:
- Data Quality: The quality of data is essential in both traditional and big data environments. Accurate and reliable data is necessary for making informed business decisions.
- Data Analysis: Both traditional and big data require some form of analysis to derive insights and knowledge from the data. Traditional data analysis methods typically involve statistical techniques and visualizations, while big data analysis may require machine learning and other advanced techniques.
- Data Storage: In both traditional and big data environments, data needs to be stored and managed effectively. Traditional data is typically stored in relational databases, while big data may require specialized technologies such as Hadoop, NoSQL, or cloud-based storage systems.
- Data Security: Data security is a critical consideration in both traditional and big data environments. Protecting sensitive information from unauthorized access, theft, or misuse is essential in both contexts.
- Business Value: Both traditional and big data can provide significant value to organizations. Traditional data can provide insights into historical trends and patterns, while big data can uncover new opportunities and help organizations make more informed decisions.
The difference between Traditional data and Big data are as follows:
| Traditional Data | Big Data |
|---|---|
| Traditional data is generated in enterprise level. | Big data is generated outside the enterprise level. |
| Its volume ranges from Gigabytes to Terabytes. | Its volume ranges from Petabytes to Zettabytes or Exabytes. |
| Traditional database system deals with structured data. | Big data system deals with structured, semi-structured,database, and unstructured data. |
| Traditional data is generated per hour or per day or more. | But big data is generated more frequently mainly per seconds. |
| Traditional data source is centralized and it is managed in centralized form. | Big data source is distributed and it is managed in distributed form. |
| Data integration is very easy. | Data integration is very difficult. |
| Normal system configuration is capable to process traditional data. | High system configuration is required to process big data. |
| The size of the data is very small. | The size is more than the traditional data size. |
| Traditional data base tools are required to perform any data base operation. | Special kind of data base tools are required to perform any databaseschema-based operation. |
| Normal functions can manipulate data. | Special kind of functions can manipulate data. |
| Its data model is strict schema based and it is static. | Its data model is a flat schema based and it is dynamic. |
| Traditional data is stable and inter relationship. | Big data is not stable and unknown relationship. |
| Traditional data is in manageable volume. | Big data is in huge volume which becomes unmanageable. |
| It is easy to manage and manipulate the data. | It is difficult to manage and manipulate the data. |
| Its data sources includes ERP transaction data, CRM transaction data, financial data, organizational data, web transaction data etc. | Its data sources includes social media, device data, sensor data, video, images, audio etc. |
Conclusion :
The key differences between traditional data and big data are related to the volume, variety, velocity, complexity, and potential value of the data. Traditional data is typically small in size, structured, and static, while big data is large, complex, and constantly changing. As a result, big data requires specialized tools and techniques to manage and analyze effectively.