Difference Between Hadoop and SQL Performance

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:

Feature	Hadoop	SQL Performance
Structure	No fixed schema	Fixed Schema
Data Format	Structured, semi-structured or unstructured data	Structured data
Data Volume	Hadoop works exceptionally well on both low and high volume of data	SQL works better on low volume of data
Data processing	Hadoop supports large-scale offline batch processing known as OLAP	SQL supports Real-time data processing known as OLTP
Speed	Faster	Slower
Throughput	Higher throughput	Lower throughput
Latency	Hadoop cannot fetch a particular record from the data set very quickly hence it has low latency	SQL can fetch a particular record from the data set very quickly hence it has high latency
Scalability	Horizontal scalability which means more machines can be added in the network for parallel processing	Vertical scalability which means more hardware or CPU is added to existing machine
Data Storage	Data can be stored in the form of tables, key-value pairs etc	Data can be stored in the form of tables only.
Integrity	Low integrity	High integrity
Data variety	Hadoop deals with Big data and supports variety of data	SQL does not support variety of data
Updates	Hadoop is designed with the concept of write once read many. Hence data updates are practically not possible	SQL is write once, read and update many. Hence data updates are very easily done
ACID Properties	It does not fully comply with ACID properties	It fully complies with ACID properties
License	Hadoop is free open source software	SQL is licensed
Example	MongoDB, HBase etc	Oracle, Microsoft SQL Server etc