Hadoop vs SQL

Definition of Hadoop vs SQL

Hadoop vs SQL both are open-source software that is used to store data. We are using SQL to store data in a structured format, whereas Hadoop is used to store data in both structured as well as unstructured formats. An open-source framework is supported by Hadoop. In Hadoop sets of data are distributed among server clusters in Hadoop and feature parallel data processing. SQL is a domain-specific used in RDBMS.

Difference Between Hadoop vs SQL

Hadoop is used to store, analyze, retrieve, and extract patterns from data in a variety of formats, including XML, Text, JSON, and others. Only data kept in an RDBMS is stored, processed, retrieved, and mined for patterns using SQL. Hadoop supports structured as well as unstructured formats. Hadoop reads data several times but only writes data once for data updates.

Only structured data can be used using SQL, although, unlike Hadoop, data can be written and read more than once. Given that Hadoop was created for big data, it can often manage data amounts of up to a few terabytes or petabytes. Low amounts of data, often measured in gigabytes, are preferable for SQL. In distributed applications with dynamic schemas, HDFS maintains information in the form of key-value pairs, tables, etc. SQL only uses tables with specified schemas to store structured information in a tabular fashion.

What is Hadoop?

Hadoop is a piece of software that enables network components by resolving vast data problems and allowing users to manage massive amounts of data. Hadoop is a highly scalable and affordable technology that can store and handle structured, unstructured, and semi-structured data. Hadoop is the most widely used data processing framework.

Hadoop provides an architectural framework in addition to distributed processing and storage. To do this, it divides a document into several stores and locks them across a number of devices. Hadoop duplicates these stores onto the cluster in order to provide fault tolerance. After that, it does distribute processing by splitting a job into a number of smaller, independent jobs. Many different types of data are kept in the Hadoop architecture.

What is SQL?

In addition to processing data streams in relational data stream management systems, SQL language is specific to the domain that is used in computers to manage data in RDBMS. Simply put, SQL is a widely used database language for creating, and storing data in RDBMS like MySQL, Oracle, PostgreSQL, etc.

The most common tool for navigating and modifying data in SQL. Developers, DBA no longer utilize SQL Server as their standard DBMS solution. There is a vast ecosystem of various tools that collaborate to do extremely difficult data platform administration jobs. It serves as the standard language for business support systems, and tools to access and query a wide range of data sources. In actuality, SQL Server is considerably better than Hadoop.

Head to Head Comparison Between Hadoop vs SQL (Infographics)

Below are the top 15 differences between Hadoop and SQL:

Key Differences between Hadoop vs SQL

Let us look at the key differences between Hadoop and SQL:

• An open-source framework known as Hadoop processes data in parallel while distributing sets of data across computer/server clusters. To manage data in relational databases, SQL is a programming language with specialized domain knowledge.
• SQL writes data more than once, whereas Hadoop only writes data once. SQL is significantly easier to understand than Hadoop. However, both demand an understanding of programming.
• The utilization of Hadoop and SQL is free and open-source. Both, however, have significant setup and upkeep expenses. Hadoop manages data availability on several systems across numerous geo-locations since it utilizes the concepts of distributed and the principle of map-reduce.
• SQL cannot benefit from distributed computing since supporting databases are typically on-premises or in the cloud. Network connectivity is necessary for scaling in a Hadoop-based system. Hadoop offers affordable and adaptable horizontal scaling. In order to scale SQL, it was necessary to spend money and time configuring new SQL servers.
• Hadoop offers a method of analyzing big batches of data called OLAP. SQL is interactive and batch-oriented because it offers OLTP or real-time data processing.

Hadoop Requirement

We must install software like python, and java on our PC in order to run Hadoop. Hadoop must be run in the manner described below.

To utilize Hadoop, each node has to have the installed components on the following list. The required components will operate if the installation is done on a single node.

• Cluster manager: The cluster manager is dependent on the Hadoop distribution. For the port numbers for the Hadoop nodes, the installer used a restful API.
• YARN: This node manager service was responsible to manage all tasks related to data processing.
• Hive table: Used to store data.
• Zookeeper: It is used by BDD to manage the graph instances and keep them available.
• HDFS: The original data that was uploaded to HDFS will be stored in this database, which is HDFS/MapR.

SQL Requirement

To install the SQL server database we required minimum hardware requirements. SQL databases are running in all of the systems. SQL is a platform-independent language, which means that SQL data can be run on any operating system. Before SQL server 2019 it is platform dependent on windows after SQL server 2019 it is also platform-independent.

To install MySQL we required a .net framework. MySQL, PostgreSQL, and Oracle SQL databases are platform-independent. We are installing those databases in any of the operating systems.

Comparison Table of Hadoop vs SQL

The table below summarizes the comparisons between Hadoop vs SQL:

Hadoop	SQL
Hadoop uses modern technology.	SQL uses traditional technology.
The data size of Hadoop in petabytes.	The data size of SQL database in GB.
Hadoop contains operations of processing, storage, and extraction.	SQL contains the operation of processing and storage retrieval of data.
Hadoop is more fault-tolerant as compared with SQL.	SQL is less fault-tolerant as compared with Hadoop.
Hadoop stores data in tables, hash maps, and key-value pairs.	SQL stores data in tables.
Hadoop contains linear scaling.	SQL contains nonlinear scaling.
Hadoop contains OLAP transactions.	SQL contains OLTP transactions.
We are accessing batch-oriented data in Hadoop.	We are accessing real-time data in SQL.
Execution speed is fast as compared to SQL.	Execution speed is slow as compared to Hadoop.
Hadoop stores data in HDFS and access by using map reduction techniques.	SQL does not contain advanced techniques of optimization.
Hadoop contains the dynamic schema and processes all types of data.	SQL contains static schema.
In Hadoop, we write data and read it multiple times.	In SQL, we write and read data multiple times.
Hadoop contains low integrity.	SQL contains high integrity.
Hadoop contains the JDBC tool for interacting with SQL.	SQL can read and write data from Hadoop.
Hadoop uses commodity hardware.	SQL uses proprietary hardware.

Purpose of Hadoop

Large data collections are typically processed and stored using Hadoop, an open-source platform. In Hadoop, clustering is utilized to store data across several nodes. Thanks to Hadoop, the cluster server that we are using in scattered contexts can process and store data more easily.

We can run the Hadoop service thanks to the components for constructing applications that Hadoop offers. Through an API that is connected to the name node, we are storing the information that the application gathered in Hadoop in a number of different formats. Hadoop name mode keeps track of the locations of chunks and file directories that were replicated onto data nodes.

Purpose of SQL

RDBMS is the foundation of SQL and has been in the information processing lexicon for a while. RDBMS is a powerful database that manages massive data and efficiently manipulates it with SQL. SQL is a language that is typically used to manage, retrieve, and store a sizable quantity of data in a database.

Only data saved in RDBMS may be stored, and processed using SQL. Structured data can be used with SQL, although unlike Hadoop, many copies of the data can be written and accessed. Tables with specified schemas are used by SQL to store structured data in a tabular fashion. The core RDBMS characteristic of ACID is how SQL operates. SQL cannot benefit from distributed computing since supporting databases are typically on-premises or in the cloud. In order to scale SQL, it was necessary to spend money and time configuring new SQL servers.

Conclusion

HDFS maintains information in the form of key-value pairs, tables, etc. SQL only uses tables with specified schemas to store structured information in a tabular fashion. An open-source framework is supported by Hadoop. In Hadoop d sets of data are distributed among server clusters in Hadoop and feature parallel data processing. SQL is a domain-specific used in RDBMS.

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