Data storing and processing is nothing new; organizations have been doing it for a few decades to reap valuable insights. Compared to that, Big Data is a much more recently derived term. So, what exactly is the difference between Traditional Data and Big Data? The relational, structured data that companies store, process, and use is known as Traditional Data. The term ‘Big Data’ does not only refer to the volume of data but also its variety, accuracy, speed, and the methods organizations use to process the data.
There are a lot of ways to understand how is Big Data different from Traditional Data. You can learn more about Big data and Traditional Data by going for the Big Data for Beginners course.
Big Data vs Traditional Data Table
There are a few parameters based on which you can understand how is Big Data different from Traditional Data. The main difference between the two is less about the volume of data and more about the variety, velocity, and how the data is managed. Below are some of the differences between Traditional Databases vs big data:
Parameters | Big Data | Traditional Data |
Flexibility | Big data is more flexible and can include both structured and unstructured data. | Traditional Data is based on a static schema that can only work well with structured data. |
Real-time Analytics | Real-time data analytics is possible with Big Data. | Data analytics can be performed after the event in Traditional Data. |
Distributed Architecture | A distributed architecture is used in Big Data. | Traditional Data uses centralized architecture. |
Multitude of Sources | Variety of sources. | Limited sources. |
Enables Exploratory Analysis | An exploratory approach that can increase the scope and inspire users to explore new questions. | The traditional approach only answers existing questions. |
Data Size | Much larger in size, volume, and variety. | Comparatively smaller in size with less variety. |
Infrastructure Required to Manage Data | Big data management can often prove to be complicated and expensive. | Smaller and more cost-effective ways of managing data. |
Big Data vs Traditional Data
The difference between Big Data vs Traditional Data heavily relies on the tools, plans, processes, and objectives used within, which derive useful insights from the datasets. Let us now take a detailed look into how Big Data differs from Traditional relational databases.
Big Data vs Traditional Data: Flexibility
Traditional Data functions are based on a static relational database. By nature, it is more rigid and only allows structured data to fall effortlessly into the patterns. Big data, on the other hand, is more unstructured and uses new methods to store both unstructured and structured data. The dynamic schema allows Big Data to be more flexible and store more varied information.
Big Data vs Traditional Data: Real-Time Analytics
Traditional databases only allow analytics to take place after the event. This is a good approach as it allows less space for error. However, it also severely limits the scope of the data. But with Big Data, real-time analytics is now possible. In domains like transportation, manufacturing, medical, and safety, this feature proves to be monumental.
Big Data vs Traditional Data: Distributed Architecture
The Big Data vs Traditional Database is also different in its architecture. The Traditional database follows a centralized architecture, which can hinder its scope. Big data, on the other hand, is based on a distributed architecture. This feature makes it more scalable and also cost-effective. Features like cloud-based storage and open-source software make the storage of Big Data more economical.
Big Data vs Traditional Data: Multitudes of Sources
One of the main differences between Big Data vs Traditional Databases is the sources. Previously, organizations could gather data from limited sources, which also hindered their performance in the market. In the last few years, however, there has been an exponential increase in the sources of data and companies can gather different types of data from various sources now. It allows us to not only get a large volume of data but also retain more variety.
Big Data vs Traditional Data: Enables Exploratory Analysis
Traditionally Data analysis was limited to reaping insights on questions from customers that needed to be addressed or to understand customer demands in depth. The data reports were primarily generated based on these concerns. However, with the rise of Big Data, the scope of an iterative approach to data is now possible.
If we look at it from a business perspective, Traditional Data can offer detailed insights, customer surveys, and monthly reports. Whereas, Big Data can help organizations with preventive measures, market foresight, sentiment analysis, and much more, all based on data-driven, more accurate figures.
Big Data vs Traditional Data: Data Size
One of the main differences between Big Data and Traditional Databases is the size of the two. Usually, Traditional Data amounts are compiled in gigabytes and terabytes, which allows for it to be stored in one centralized server. In contrast, Big Data can be measured up in petabytes, zettabytes, exabytes, etc. Thus, it also requires more modern solutions for storage and analysis.
Big Data vs Traditional Data: Infrastructure Required to Manage Data
One of the significant differences between Traditional Data warehouses vs Big Data is management tools. Since traditional data is smaller in size and easier to manage, it can be stored and analyzed with traditional tools, which are more cost-effective. Big data, on the other hand, need more technologically advanced management methods, making it more expensive.
How are They Similar?
Even though they are vastly different in nature, there are a few similarities between Big Data and Traditional Data. Here are some examples of Big data and Traditional data similarities.
Security is important in both data types, whether it is Traditional Data or Big Data. With the rise in cybercrime, protecting all types of sensitive data from theft and unauthorized access is now more important than ever.
Even though the Traditional vs Big Data approach is quite different in terms of evaluation, data analysis is crucial to driving knowledge and insight from gathered data. Data visualization and other statistical tools are used to analyze Traditional Data, wherein more advanced technologies like machine learning are used to analyze Big Data.
The accuracy of data determines how reliable the data is. This means that under all circumstances, the quality of data is of utmost importance for organizations making informed decisions based on datasets.
The storage and management of data are crucial for organizations. Big Data usually requires more technologically advanced storage databases like cloud-based storage. Traditional Data, on the other hand, is stored in relational databases.
Both data types are extremely important for an organization to run smoothly, even when Traditional vs Big Data business approaches are quite different. Big Data can help open up new opportunities and drive informed decisions, wherein Traditional Data can derive patterns and trends from historical data.
What Should You Choose between Big Data and Traditional Data?
Now that we learned all the differences and similarities between Big Data vs Traditional Data, the main question still remains, which one should you choose between the two? The answer is not quite simple. Every organization needs both these types of data to perform well. Traditional Data offers insights into the past and thus can be used to correct mistakes, whereas Big Data can be used in multitudes of ways to predict the future and make sure your organization performs well.
At the end of the day, a lot of today’s data is still Traditional Data, and companies can't function without it. But with the rise of Big Data, more avenues are opening up for scaling. An organization needs to properly store, manage, and utilize both data types to stay ahead of the competition.
Conclusion
Popular trends claim that the rise of Big Data will result in the eradication of Traditional Data, but that is not true. Every sector and company has different needs when it comes to the type of data required. With that in mind, the key for organizations is to find the perfect balance between the two. That way, they can utilize the insights drawn from these datasets for future endeavors. If you want to learn more about big data and data analysis, then KnowledgeHut Big Data for Beginners course can be an excellent choice to get started.
