Difference Between Hadoop and SQL

Hadoop and SQL are used for data management but varies in the type of data handled and also are handled in a different way. Hadoop is an ecosystem of big data which is used for storing data, processing them, and mining the data patterns.

SQL is basically a type of query language which has similar functions to Hadoop.

Hadoop vs SQL

The main difference between Hadoop and SQL is that Hadoop is an open-source type Framework while SQL is particularly domain-specific. It can handle data only in relational databases.

Hadoop vs SQL

Hadoop is available in the market alike a product and thus has a rating of 4.3/ 5 on G2.com which is a software review website. It is free to use but additional requirements are required which comes with a price and also some maintenance charge is required.

It is an open-source tool. SQL is also an open-source but domain-specific query language.

It can process and manage data on a relational database Management system. Since it is not sold in the market like a product and is a language, it has no such rating.

The language is used for analytical queries. It is only capable of handling limited types of data sets.

Similar to Hadoop, SQL is also free but has some additional charges and a maintenance cost.

Comparison Table Between Hadoop and SQL

Parameters of ComparisonHadoopSQL
Full Name The full name is Apache Hadoop. The full name is Structured Query Language.
Type of scalingHadoop works with linear scaling.SQL is non-linear.
Number of times it can writeHadoop can write one single time.SQL can write multiple times.
NatureIt is dynamic in nature.It is static in nature.
Difficulty LevelHadoop is complex and difficult to learn compared to SQL.SQL is easier to learn compared to Hadoop. 
Rating on G2.comThe rating of Hadoop is 4.3/5.No rating is given for SQL since it is a query language and not sold in the market as a product.
IntegrityHadoop is under low integrity.SQL is under high integrity.
Batch processingHadoop supports batch processing.SQL does not support batch processing.

What is Hadoop?

Apache Hadoop commonly known as Hadoop is an open-source type of software that is used to solve huge loads of data management problems by using a network of multiple computers.

By using the MapReduce programming model, the software framework processes large amounts of data.

The Hadoop is designed in such a way, assuming that hardware failures can occur very commonly and The framework should thus handle it automatically.

Hadoop divides the file into Large chunks and then it is distributed across the nodes in a cluster. Then the packaged code is transferred into nodes for parallel data processing.

Thus dataset is processed faster and in a more efficient manner. The base of the Hadoop framework is composed of the following modules:-

  • Hadoop Common
  • Hadoop Distributed File System ( HDFS)
  • Hadoop Yarn
  • Hadoop MapReduce
  • Hadoop Ozone

The term Hadoop is used for Both the modules that are base module and submodule. Hadoop was a paper on Google File System that was published in the year 2003.

The co-founders of Hadoop are Doug Cutting and Mike Cafarella. Owen O’ Malley in the year 2006, was added to the Hadoop Project and Was released for the first time in April 2006.

Dhruba Borthakur created the very first design document for Hadoop Distributed File System in 2007.

What is SQL?

Structured Query Language or SQL as the short name runs is a language that is domain-specific used mainly in programming and also the management of data. It can handle data only in Relational Database or RDBMS.

SQL is an expert in structured data handling. SQL comes with two main advantages.

One is that it can handle a large quantity of data with one single command and the other is that it can eliminate the need for specification of how a record is to be reached with or without the presence of an index.

The language is originally based upon relational algebra. Data definition, data access control, data manipulation, and data query are included under SQL.

It was one of the very first languages to use the relational model of Edgar F.Codd. SQL was first developed by Donald D. Chamberlin and Raymond F. Boyce at IBM in the earlier 1970s.

It was earlier known as SEQUEL or Structured English Query Language. SQL can define mainly three kinds of data:-

  • Predefined data type
  • Constructed data type 
  • User-defined data type

The language is divided into several language elements:-

  • Clauses
  • Expressions
  • Predicates
  • Queries 
  • Statements

SQL is found to deviate in various ways from the foundation laid theoretically. 

Main Differences Between Hadoop and SQL

  1. Hadoop does linear scaling while SQL is a non-linear programming language.
  2. Hadoop falls under low integrity while SQL falls under High Integrity.
  3. Hadoop is dynamic while SQL is static in nature.
  4. Hadoop is capable of writing only once, but SQL is capable of writing multiple times.
  5. Hadoop is much more complex and harder than SQL. 
  6. Batch processing is supported by Hadoop but not SQL. 
  7. Hadoop works with large quantities of data while SQL mainly works with small quantities of data.
Difference Between Hadoop and SQL

Conclusion

Both Hadoop and SQL do the work of data management but in two different ways. Hadoop is a framework or compilation of software components.

On the other hand, SQL is a type of programming language. The correct and appropriate tool for a company’s data depends on the type of data the company is handling and also the time and money invested in the training.

If the company needs to handle large quantities of data, then Hadoop is definitely the best option, but It is quite complex since it is an advanced data management tool. So In case one needs to avoid the complexities, he or she can opt for SQL. 

References

  1. https://dl.acm.org/doi/abs/10.14778/2732977.2733002
  2. https://ieeexplore.ieee.org/abstract/document/7840751/
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