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Difference Between Small Data and Big Data

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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: 

FeatureSmall DataBig Data
VarietyData is typically structured and uniformData is often unstructured and heterogeneous
VeracityData is generally high quality and reliableData quality and reliability can vary widely
ProcessingData can often be processed on a single machine or in-memoryData requires distributed processing frameworks such as MapReduce or Spark
Analytics Traditional statistical techniques can be used to analyze dataAdvanced analytics techniques such as machine learning are often require
CollectionGenerally, it is obtained in an organized manner than is inserted into the databaseThe Big Data collection is done by using pipelines having queues like AWS Kinesis or Google Pub / Sub to balance high-speed data
VolumeData in the range of tens or hundreds of GigabytesSize of Data is more than Terabytes
Analysis AreasData marts(Analysts)Clusters(Data Scientists), Data marts(Analysts)
QualityContains less noise as data is less collected in a controlled mannerUsually, the quality of data is not guaranteed
ProcessingIt requires batch-oriented processing pipelinesIt has both batch and stream processing pipelines
VelocityA regulated and constant flow of data, data aggregation is slowData arrives at extremely high speeds, large volumes of data aggregation in a short time
StructureStructured 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)
ScalabilityThey are usually vertically scaledThey are mostly based on horizontally scaling architectures, which gives more versatility at a lower cost
Query Languageonly SequelPython, R, Java, Sequel
HardwareA single server is sufficientRequires more than one server
ValueBusiness Intelligence, analysis and reportingComplex data mining techniques for pattern finding, recommendation, prediction etc.
OptimizationData can be optimized manually(human powered)Requires machine learning techniques for data optimization
StorageStorage within enterprises, local servers etc.Usually requires distributed storage systems on cloud or in external file systems
PeopleData Analysts, Database Administrators and Data EngineersData Scientists, Data Analysts, Database Administrators and Data Engineers
SecuritySecurity 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.
NomenclatureDatabase, Data Warehouse, Data MartData Lake
InfrastructurePredictable resource allocation, mostly vertically scalable hardware.More agile infrastructure with horizontally scalable hardware
ApplicationsSmall-scale applications, such as personal or small business data managementLarge-scale applications, such as enterprise-level data management, internet of things (IoT), and social media analysis


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Last Updated : 04 Apr, 2023
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