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Difference Between Small Data and Big Data

Last Updated : 04 Apr, 2023
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Small Data: It can be defined as small datasets that are capable of impacting decisions in the present. Anything that is currently ongoing and whose data can be accumulated in an Excel file. Small Data is also helpful in making decisions, but does not aim to impact the business to a great extent, rather for a short span of Small data can be described as small datasets that are capable of having an influence on current decisions. Almost everything currently in progress and the data of which can be acquired in an Excel file. Small data is also useful in decision-making but is not intended to have a large impact on business, rather for a short period of time. 
In nutshell, data that is simple enough to be used for human understanding in such a volume and structure that makes it accessible, concise, and workable is known as small data. 

Big Data: It can be represented as large chunks of structured and unstructured data. The amount of data stored is immense. It is therefore important for analysts to thoroughly dig the whole thing into making it relevant and useful to make proper business decisions. 
In short, datasets that are really huge and complex that conventional data processing techniques can not manage them are known as big data. 


Below is a table of differences between Small Data and Big Data: 

Feature Small Data Big Data
Variety Data is typically structured and uniform Data is often unstructured and heterogeneous
Veracity Data is generally high quality and reliable Data quality and reliability can vary widely
Processing Data can often be processed on a single machine or in-memory Data requires distributed processing frameworks such as MapReduce or Spark
Technology Traditional Modern
Analytics  Traditional statistical techniques can be used to analyze data Advanced analytics techniques such as machine learning are often require
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
Volume Data in the range of tens or hundreds of Gigabytes Size of Data is more than Terabytes
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
Processing It requires batch-oriented processing pipelines It has both batch and stream processing pipelines
Database SQL NoSQL
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) Numerous variety of data set including tabular data, text, audio, images, video, logs, JSON etc.(Non Relational)
Scalability They are usually vertically scaled They are mostly based on horizontally scaling architectures, which gives more versatility at a lower cost
Query Language only Sequel Python, R, Java, Sequel
Hardware A single server is sufficient Requires more than one server
Value Business Intelligence, analysis and reporting Complex data mining techniques for pattern finding, recommendation, prediction etc.
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 Security practices for Small Data include user privileges, data encryption, hashing, etc. Securing Big Data systems are much more complicated. Best security practices include data encryption, cluster network isolation, strong access control protocols etc.
Nomenclature Database, Data Warehouse, Data Mart Data Lake
Infrastructure Predictable resource allocation, mostly vertically scalable hardware. More agile infrastructure with horizontally scalable hardware
Applications Small-scale applications, such as personal or small business data management Large-scale applications, such as enterprise-level data management, internet of things (IoT), and social media analysis


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