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Difference Between Hadoop and SQL Performance
  • Last Updated : 30 Apr, 2020

Hadoop: Hadoop is an open-source software framework written in Java for storing data and processing large datasets ranging in size from gigabytes to petabytes. Hadoop is a distributed file system that can store and process a massive amount of data clusters across computers. Hadoop from being open source is compatible with all the platforms since it is Java-based. Hadoop has two core layers namely, Processing/Computation layer (MapReduce) and Storage layer (Hadoop Distributed File System). Hadoop runs code across a cluster of computers and performs offline batch processing for huge data sets across the cluster of commodity servers. However, Hadoop is not a replacement for SQL rather their use depends on individual requirements. When compared in terms of performance, Hadoop outshines SQL due to its increased speed and ability to process structured, semi-structured and unstructured data with the same efficiency.

SQL Performance: Structured Query Language (SQL) is a standard language to manipulate, retrieve and store a data in a database. Relational databases use SQL as a standard to maintain and manipulate data. SQL commands such as “Select”, “Insert”, “Update”, “Delete”, “Create”, and “Drop” can be used to store, update or retrieve data from a database. Some common relational database management systems that use SQL are Oracle, Microsoft SQL Server, Sybase, Access, Ingres, etc. However, with an increasing amount of data (or Big Data), it became difficult to store such a huge amount of data using relational databases. worked well for structured schema but as for Big Data, it did not have in a fixed schema, rather it was semi-structured data. RDBMS The 3 V’s of Big Data: Volume, variety, and velocity were the primary reason that led to the advent of NoSQL databases. As from the name it was quite evident that SQL could no longer serve the purpose of data manipulation for NoSQL databases. Hadoop has an edge over SQL in this context.

Below is a table of differences between Hadoop and SQL Performance:

FeatureHadoopSQL Performance
StructureNo fixed schemaFixed Schema
Data FormatStructured, semi-structured or unstructured dataStructured data
Data VolumeHadoop works exceptionally well on both low and high volume of dataSQL works better on low volume of data
Data processingHadoop supports large-scale offline batch processing known as OLAPSQL supports Real-time data processing known as OLTP
SpeedFasterSlower
ThroughputHigher throughputLower throughput
LatencyHadoop cannot fetch a particular record from the data set very quickly hence it has low latencySQL can fetch a particular record from the data set very quickly hence it has high latency
ScalabilityHorizontal scalability which means more machines can be added in the network for parallel processingVertical scalability which means more hardware or CPU is added to existing machine
Data StorageData can be stored in the form of tables, key-value pairs etcData can be stored in the form of tables only.
IntegrityLow integrityHigh integrity
Data varietyHadoop deals with Big data and supports variety of dataSQL does not support variety of data
UpdatesHadoop is designed with the concept of write once read many. Hence data updates are practically not possibleSQL is write once, read and update many. Hence data updates are very easily done
ACID PropertiesIt does not fully comply with ACID propertiesIt fully complies with ACID properties
LicenseHadoop is free open source softwareSQL is licensed
ExampleMongoDB, HBase etcOracle, Microsoft SQL Server etc
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