Difference Between Hadoop and SQL

Hadoop: It is a framework that stores Big Data in distributed systems and then processes it parallelly. Four main components of Hadoop are Hadoop Distributed File System(HDFS), Yarn, MapReduce, and libraries. It involves not only large data but a mixture of structured, semi-structured, and unstructured information. Amazon, IBM, Microsoft, Cloudera, ScienceSoft, Pivotal, Hortonworks are some of the companies using Hadoop technology. SQL: Structured Query Language is a domain-specific language used in computing and to handle data management in relational database management systems, it also processes data streams in relational data stream management systems. In nutshell, SQL is a standard Database language that is used for creating, storing and extracting data from relational databases such as MySQL, Oracle, SQL Server, etc. Below is a table of differences between Hadoop and SQL:

Feature	Hadoop	SQL
Technology	Modern	Traditional
Volume	Usually in PetaBytes	Usually in GigaBytes
Operations	Storage, processing, retrieval and pattern extraction from data	Storage, processing, retrieval and pattern mining of data
Fault Tolerance	Hadoop is highly fault tolerant	SQL has good fault tolerance
Storage	Stores data in the form of key-value pairs, tables, hash map etc in distributed systems.	Stores structured data in tabular format with fixed schema in cloud
Scaling	Linear	Non linear
Providers	Cloudera, Horton work, AWS etc. provides Hadoop systems.	Well-known industry leaders in SQL systems are Microsoft, SAP, Oracle etc.
Data Access	Batch oriented data access	Interactive and batch oriented data access
Cost	It is open source and systems can be cost effectively scaled	It is licensed and costs a fortune to buy a SQL server, moreover if system runs out of storage additional charges also emerge
Time	Statements are executed very quickly	SQL syntax is slow when executed in millions of rows
Optimization	It stores data in HDFS and process though Map Reduce with huge optimization techniques.	It does not have any advanced optimization techniques
Structure	Dynamic schema, capable of storing and processing log data, real-time data, images, videos, sensor data etc.(both structured and unstructured)	Static Schema, capable of storing data(fixed schema) in tabular format only(structured)
Data Update	Write data once, read data multiple times	Read and Write data multiple times
Integrity	Low	High
Interaction	Hadoop uses JDBC(Java Database Connectivity) to communicate with SQL systems to send and receive data	SQL systems can read and write data to Hadoop systems
Hardware	Uses commodity hardware	Uses propriety hardware
Training	Learning Hadoop for entry-level as well as seasoned profession is moderately hard	Learning SQL is easy for even entry-level professionals