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:
FEATURE |
SMALL DATA |
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
Conclusion
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
