Top 21 Difference Between Small Data and Big Data| Small data vs Big data


Daily huge amount of  data is generated. Online media has increased its impact to the next level. But do we imagine what is the property of the data whether it is small or big? We harness data daily. Moreover we don’t know even from where this type of data is originated. You might have heard about the term big data these days and it is also very much relevant to today’s scenario. But do you guys have ever think about the journey how this big data got big from actually a small data? In this article, we will discuss difference between Small data and big data.

So read on the full article to know the exact difference between small data and big data.

What is Small Data?

Small data is that data which is acquired from small datasets. It can be anything ranging from a small excel file to a simple notepad file.

So the question is What is the benefit of small data?

It helps in making relevant decisions. Moreover, it can influence the current decision as well. In simple terms we can say the data which is deployed for usual tasks and that is quite concise in nature as well as it has accessible structure is defined as a small data.

What is Big data?

Big Data as is clear from the name is large chunks of structured and unstructured data. The amount of data is so huge, we can’t even imagine what quantity is daily stored.
It also assists in taking the business decisions. This data focuses  on 5’Vs mainly volume, veracity, viscosity, variety, and value.

Also Read- Big Data Vs Data warehouse | Differences between big data and data warehouse

Lets read out the major differences between Small Data and Big Data:




Technology used

Small data makes the use of traditional technology Big data is vast so it can not be extracted by vague methods, so it deploys new and modern technology
Accessibility It is small in size hence it is easily accessible Some specific tools are needed to access this much amount of the data
Volume It has a lesser volume ranging from GB to few TB It incurs more volume that is more than Terabytes
Collection Generally, it is obtained in an organized manner than is inserted into the database The Big Data collection is done by using pipelines having queues like AWS Kinesis or Google Pub / Sub to balance high-speed data
Velocity Its velocity of generation is slow It is quite fast
Analysis Areas Data marts(Analysts) Clusters(Data Scientists), Data marts(Analysts)
Quality Contains less noise as data is less collected in a controlled manner Usually, the quality of data is not guaranteed
Query Language SQL is used Python, R, Java, SQL
Database SQL NoSQL
Processing It requires batch-oriented processing pipelines It has both batch and stream processing pipelines
Scalability Small data is  vertically scaled They are mostly based on horizontally scaling architectures. It allows  more versatility at a lower cost
Velocity A regulated and constant flow of data, data aggregation is slow Data arrives at extremely high speeds, large volumes of data aggregation in a short time
Structure Structured data in tabular format with fixed schema(Relational) The variety of data set including tabular data, text, audio, images, video, logs, JSON, etc.(Non-Relational)
Infrastructure Predictable resource allocation, mostly vertically scalable hardware. More agile infrastructure with horizontally scalable hardware
Value Business Intelligence, analysis and reporting Complex data mining techniques for pattern finding, recommendation, prediction, etc.
Hardware A single server is sufficient Requires more than one server
Optimization Data can be optimized manually(human-powered) Requires machine learning techniques for data optimization
Storage Storage within enterprises, local servers, etc. Usually requires distributed storage systems on cloud or in external file systems
People Data Analysts, Database Administrators and Data Engineers Data Scientists, Data Analysts, Database Administrators, and Data Engineers
Security The main practices of security are user privileges, data encryption, hashing, etc. Best security practices include data encryption, cluster network isolation, strong access control protocols, etc.
Nomenclature Database, Data Warehouse, Data Mart

Data Lake

Also Read- What is Big data: Advantages and Disadvantages of Big data


I hope this article works for you. In this article, we have represented the difference between Small data and big data. If you are having any doubt, ask me freely in the comment box


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