Hadoop vs SQL
In today’s world, Organisations rely on Big Data to fuel their operations, and Hadoop and SQL are widely used in the data industry for data management because they can efficiently handle enormous data volumes.
This description is intended to examine Hadoop and SQL, and distinguishes between the two by highlighting the Hadoop vs SQL differences.
What is Hadoop?
Hadoop is an open-source software framework for processing and storing data in clusters of commodity hardware for Big Data applications. It has a lot of storage for any type of data, a lot of processing power, and it can handle multiple activities or jobs at the same time thanks to parallel processing.
What is SQL?
Structured Query Language (SQL) has long been the standard tool for accessing and manipulating data in databases. It’s a massive ecosystem of different tools and services that operate together to handle extremely complex data platform administration duties. It is the language for accessing and querying a range of data sources for transactional and business support systems, as well as Business Intelligence applications.
Key Differences between Hadoop and SQL
|Architecture||An open-source framework is supported by Hadoop. Data sets are spread among computer/server clusters in Hadoop, which allows for simultaneous data processing.||It is based on domain-specific language, which is used in relational databases to conduct database management activities.|
|Operations||Hadoop is a platform for collecting, processing, retrieving, and extracting patterns from data in a variety of formats, including XML, Text, and JSON.||SQL is a programming language that is solely used to store, analyse, retrieve, and pattern mine data in relational databases.|
|Data Type||It can deal with both organised and unstructured data, publishes data once but reads it numerous times for data updates.||It is a structured data language that allows data to be written and read many times.|
|Data Structures Supported||Supports NoSQL data type structures, columnar data structures, and so on, which means you’ll have to offer codes for implementation and transaction rollback.||SQL is based on the core RDBMS features of Atomicity, Consistency, Isolation, and Durability (ACID).|
|Fault Tolerance||Extremely fault tolerance||High level of tolerance|
|Integrity||Low Integrity||High Integrity|
|Scaling||Scaling a Hadoop-based system necessitates the use of a network to connect computers. Horizontal scaling using Hadoop is both inexpensive and versatile.||Scaling SQL required purchasing and configuring new SQL servers, which was costly and time-consuming.|
|Data Processing||Offers Online Analytical Processing, which is large-scale batch data processing (OLAP).||Offers real-time data processing known as Online Transaction Processing (OLTP).|
|Execution Time||When millions of searches are run at the same time, statements are executed very quickly.||When millions of rows are processed, SQL syntax might be slow.|
|Interaction||Hadoop interacts with SQL systems using suitable Java Database Connectivity to send and receive data.||SQL systems are capable of reading and writing data to Hadoop systems.|
|Language Supported||The Java programming language is used to create the Hadoop framework.||SQL is a classic database language that is used to handle relational databases such as MySQL, Oracle, SQL Server, and others.|
|Use Case||When you need to manage large amounts of unstructured, structured, or semi-structured data.||Works effectively with little amounts of data and only handles structured data.|
See Also: MapReduce vs Spark
This article focused on the differences between Hadoop and SQL, demonstrating that they are both used for data management, but in different ways. Hadoop, a software framework for handling big data sets, can only write data once, whereas SQL, a programming language for data management in relational databases, may be written and read several times, simple to use but difficult to scale.