Hadoop vs SQL: The Comparative Guide Which is better for Data Management

In today’s global economy, we have seen the emergence of Big Data in the digital technology industry, and as such, companies ranging from startups to large corporations are setting aside resources on how to harness insights and gain key strategies from the data they produce as well as from the ones available from other companies. Trying to analyze such data will require the use of certain tools and specific skills to fully grasp the opportunities on offer from the data.

This write-up is aimed at looking at Hadoop and SQL, it will differentiate between both of them by highlighting Hadoop vs SQL differences to enable you to choose either of them when presented with certain challenges that need to be solved as they are best suited for specific scenarios.

Organizations today rely on Big Data to power their business and Hadoop and SQL are both popularly used for data management available in the data industry as they can be used to handle large data sets efficiently.

Hadoop vs SQL

In this section, you will be introduced to the differences between Hadoop and SQL and the unique ways they manage data to allow you to decide on which tool to use for specific operations.

1. Architecture

Hadoop: Hadoop supports an open-source framework. In Hadoop data sets are distributed across computer/server clusters with parallel data processing features.

SQL: SQL stands for Structured Query Language. It is based on domain-specific language, used to handle database management operations in relational databases.

2. Operations

Hadoop: Hadoop is used for storing, processing, retrieving, and pattern extraction from data across a wide range of formats like XML, Text, JSON, etc.

SQL: SQL is used to store, process, retrieve, and pattern mine data stored in a relational database only.

3. Data Type/ Data update

Hadoop: Hadoop handles both structured and unstructured data formats. For data update, Hadoop writes data once but reads data multiple times.

SQL: SQL works only for structured data but unlike Hadoop, data can be written and read multiple times.

4. Data Volume Processed

Hadoop: Hadoop is developed for Big Data hence, it usually handles data volumes up to Terabytes and Petabytes.

SQL: SQL works better on low volumes of data, usually in Gigabytes.

5. Data Storage

Hadoop: Hadoop stores data in the form of key-value pairs, hash, maps, tables, etc in distributed systems with dynamic schemas.

SQL: SQL stores structured data in a tabular format using tables only with fixed schemas.

6. Schema Structure

Hadoop: Hadoop supports dynamic schema structure.

SQL: SQL supports static schema structure.

7. Data Structures Supported

Hadoop: Hadoop supports NoSQL data type structures, columnar data structures, etc. meaning you will have to provide codes for implementation or for rolling back during a transaction.

SQL: SQL works on the property of Atomicity, Consistency, Isolation, and Durability (ACID) which is fundamental to RDBMS.

8. Fault Tolerance

Hadoop: Hadoop is highly fault-tolerant.

SQL: SQL has good fault tolerance.

9. Availablity

Hadoop: As Hadoop uses the notion of distributed computing and the principle of map-reduce therefore it handles data availability on multiple systems across multiple geo-locations.

SQL: SQL supporting databases are usually available on-prises or on the cloud, therefore it can’t utilize the benefits of distributed computing.

10. Integrity

Hadoop: Hadoop has low integrity.

SQL: SQL has high integrity.

11. Scaling

Hadoop: Scaling in Hadoop based system requires connecting computers over the network. Horizontal Scaling with Hadoop is cheap and flexible.

SQL: Scaling in SQL required purchasing additional SQL servers and configuration which is expensive and time-consuming.

12. Data Processing

Hadoop: Hadoop supports large-scale batch data processing known as Online Analytical Processing (OLAP).

SQL: SQL supports real-time data processing known as Online Transaction Processing (OLTP) thereby making it interactive and batch-oriented.

13. Execution Time

Hadoop: Statements in Hadoop are executed very quickly even when millions of queries are executed at once.

SQL: SQL syntax can be slow when executed in millions of rows.

14. Interaction

Hadoop: Hadoop uses appropriate Java Database Connectivity (JDBC) to interact with SQL systems to transfer and receive data between them.

SQL: SQL systems can read and write data to Hadoop systems.

15. Support for ML and AI 

Hadoop: Hadoop supports advanced machine learning and artificial intelligence techniques.

SQL: SQL’s support for ML and AI is limited compared to Hadoop.

16. Skill Level

Hadoop: Hadoop requires an advanced skill level for you to be proficient in using it and trying to learn Hadoop as a beginner can be moderately difficult as it requires certain kinds of skill sets.

SQL: The SQL skill level required to use it is intermediate as it can be learned easily for beginners and entry-level professionals.

17. Language Supported

Hadoop: Hadoop framework is built with Java programming language.

SQL: SQL is a traditional database language used to perform database management operations on relational databases such as MySQL, Oracle, SQL Server, etc.

18. Use Case

Hadoop: When you need to manage unstructured data, structured data, or semi-structured data in huge volume, Hadoop is a good fit.

SQL: SQL performs well in a moderate volume of data and it supports structured data only.

19. Hardware Configuration

Hadoop: In Hadoop, commodity hardware installation is required on the server.

SQL: With SQL supported system, propriety hardware installation is required.

20. Pricing

Hadoop: Hadoop is a free open-source framework.

SQL: SQL supporting systems are mostly licensed.

Hadoop vs SQL Summary

Parameter	Hadoop	SQL
Architecture	Hadoop supports an open-source framework. In Hadoop data sets are distributed across computer/server clusters with parallel data processing features.	SQL stands for Structured Query Language. It is based on domain-specific language, used to handle database management operations in relational databases.
Operations	Hadoop is used for storing, processing, retrieving, and pattern extraction from data across a wide range of formats like XML, Text, JSON, etc.	SQL is used to store, process, retrieve, and pattern mine data stored in a relational database only.
Data Type/ Data update	Hadoop handles both structured and unstructured data formats. For data update, Hadoop writes data once but reads data multiple times.	SQL works only for structured data but unlike Hadoop, data can be written and read multiple times.
Data Volume Processed	Hadoop is developed for Big Data hence, it usually handles data volumes up to Terabytes and Petabytes.	SQL works better on low volumes of data, usually in Gigabytes.
Data Storage	Hadoop stores data in the form of key-value pairs, hash, maps, tables, etc in distributed systems with dynamic schemas.	SQL stores structured data in a tabular format using tables only with fixed schemas.
Schema Structure	Hadoop supports dynamic schema structure.	SQL supports static schema structure.
Data Structures Supported	Hadoop supports NoSQL data type structures, columnar data structures, etc. meaning you will have to provide codes for implementation or for rolling back during a transaction.	SQL works on the property of Atomicity, Consistency, Isolation, and Durability (ACID) which is fundamental to RDBMS.
Fault Tolerance	Hadoop is highly fault-tolerant.	SQL has good fault tolerance.
Availability	As Hadoop uses the notion of distributed computing and the principle of map-reduce therefore it handles data availability on multiple systems across multiple geo-locations.	SQL supporting databases are usually available on-prises or on the cloud, therefore it can’t utilize the benefits of distributed computing.
Integrity	Hadoop has low integrity.	SQL has high integrity.
Scaling	Scaling in Hadoop based system requires connecting computers over the network. Horizontal Scaling with Hadoop is cheap and flexible.	Scaling in SQL required purchasing additional SQL servers and configuration which is expensive and time-consuming.
Data Processing	Hadoop supports large-scale batch data processing known as Online Analytical Processing (OLAP).	SQL supports real-time data processing known as Online Transaction Processing (OLTP) thereby making it interactive and batch-oriented.
Execution Time	Statements in Hadoop are executed very quickly even when millions of queries are executed at once.	SQL syntax can be slow when executed in millions of rows.
Interaction	Hadoop uses appropriate Java Database Connectivity (JDBC) to interact with SQL systems to transfer and receive data between them.	SQL systems can read and write data to Hadoop systems.
Support for ML and AI	Hadoop supports advanced machine learning and artificial intelligence techniques.	SQL’s support for ML and AI is limited compared to Hadoop.
Skill Level	Hadoop requires an advanced skill level for you to be proficient in using it and trying to learn Hadoop as a beginner can be moderately difficult as it requires certain kinds of skill sets.	The SQL skill level required to use it is intermediate as it can be learned easily for beginners and entry-level professionals.
Language Supported	Hadoop framework is built with Java programming language.	SQL is a traditional database language used to perform database management operations on relational databases such as MySQL, Oracle, SQL Server, etc.
Use Case	When you need to manage unstructured data, structured data, or semi-structured data in huge volume, Hadoop is a good fit.	SQL performs well in a moderate volume of data and it supports structured data only.
Hardware Configuration	In Hadoop, commodity hardware installation is required on the server.	With SQL supported system, propriety hardware installation is required.
Pricing	Hadoop is a free open-source framework.	SQL supporting systems are mostly licensed.

Conclusion

This article primarily looked at the difference between Hadoop and SQL, it showed that they are both used in the management of data but carry this out in different ways. Hadoop which is a framework of software components handles larger data sets and can only write data once whereas, SQL, a programming language that is used for data management in RDBMS, can be written and read multiple times, it is easy to use but difficult to scale.

The differences between the two do not negate the other as one can not say Hadoop is better than SQL or vice versa, rather, your preference, in the end, will depend on what type of data you want to handle or the kind of operation your enterprise is into as well as the cost implications of using either of them.

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