Feature image Hadoop Vs SQL

Hadoop vs SQL:
A Complete Comparative Guide

By Brian Laleye · October 14, 2022 · 9 min read

Hadoop is a framework of software components, while SQL is a programming language.

For big data tasks, both tools have pros and cons.

Hadoop handles larger data sets but only writes data once. SQL is easier to use but more difficult to scale.

In this comparative guide, you will get a detailed explanation of Hadoop Vs SQL.

Hadoop vs SQL: A Quick Overview

The essential comparisons between Hadoop and SQL to know are:

  • Architecture

Hadoop is defined as an open-source framework that works to distribute data among different servers. Along with this, it processes data parallelly.

At the same time, SQL is a domain-specific programming language that helps handle lots of data in relational databases.

  • Skill Level

In comparison with SQL, Hadoop is much harder to learn. But both skills require a decent knowledge of code.

  • Pricing

Hadoop and SQL are both open-source that can be used free of cost. But the setup for both is different, as well as maintenance costs.

  • Reviews

Hadoop gains a 4.3/5 customer rating on the website G2.com. But SQL is not a product; it’s a programming language. Thus it has no rating on G2.

  • Data

Hadoop writes data a single time, while SQL writes it many times. However, both read data multiple times.

To grow business, big data needs to be handled carefully in organizations. But most of the teams in organizations fail to take such complexities in data management.

However, Hadoop and SQL best handle big data most efficiently. 

In this article, we will compare Hadoop vs SQL based on various factors, customer reviews, and features.

Comparison of Features: Hadoop Vs SQL

Comparative Tour: Level 1

Hadoop

SQL

User Rating on G2.com

4.3/5

Pricing

No Cost

No Cost

Language

Java

SQL

Schema Structure

Dynamic

Static

Scaling

Linear

Non Linear

Skill

Advanced

Intermediate

Comparative Tour: Level 2

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 extremely fault tolerant

SQL has good fault tolerance

Storage

Stores data in the form of key-value pairs, tables, hash maps, etc in distributed systems

Stores structured data in tabular format with fixed schema in the cloud

Scaling

Linear

Non Linear

Providers

Cloudera, Horton work, AWS, etc. provide 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 the 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 processes through 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 proprietary hardware

Training

Learning Hadoop for entry-level as well as a seasoned profession is moderately hard

Learning SQL is easy for even entry-level professionals

Understanding Hadoop

Hadoop is an excellent ecosystem of open-source working tools that handle data sets in a distributed manner and solve multiple data management issues.

Four components make Hadoop, Yarn, libraries, and Hadoop Distributed File System(HDFS), which runs on the shelf hardware.

Hadoop has an excellent feature of handling various data sets, making it the first choice among organizations looking to generate insights and valuable data extracted from multiple sources. The tool is best for taking large amounts of data.

The most successful organizations using Hadoop technology are IBM, Amazon Web Services, Hadapt, Pivotal Software, etc.

Understanding SQL

SQL stands for Structured Query Language and is one of the popular open-source domain-specifying programming languages that help efficiently manage and process data in RDBMS (Relational Database Management System) like MySQL, SQL Server, and Oracle, etc. SQL is a declarative language developed by Oracle to generate analytical queries.
A domain-specific language used in computing, structured query language processes data streams in relational data stream management systems in addition to handling data management in relational database management systems.

Simply put, SQL is a standard database language used to create, store, and extract data from relational databases like MySQL, Oracle, SQL Server, and others.

Is Hadoop Better than SQL?

Hadoop performs better than SQL when compared in terms of speed and the capacity to process organized, semi-structured, and unstructured data equally well.

However, Hadoop is not a replacement for SQL; rather, its application relies on specific needs.

Does Hadoop Use SQL?

SQL-on-Hadoop is a class of analytical application tools that combine established SQL-style querying with newer Hadoop data framework elements.

By supporting familiar SQL queries, SQL-on-Hadoop lets a wider group of enterprise developers and business analysts work with Hadoop on commodity computing clusters.

One of the earliest efforts to combine SQL and Hadoop resulted in the Hive data warehouse.

Other tools that help support SQL-on-Hadoop include BigSQL, Drill, Hadapt, Hawq, H-SQL, Impala, JethroData, Polybase, Presto, Shark (Hive on Spark), Spark, Splice Machine, Stinger, and Tez (Hive on Tez).

Hadoop and SQL: Main Differences

In the first shot, we can say that the most significant difference between Hadoop and SQL is how these separate tools handle data.

SQL has limitations in that they can take only limited data sets like relational data and faces lots of struggles with complex sets that come.

Hadoop is excellent for handling such large sets of data as well as unstructured data.

Some of the other differences are listed below:

  • Hadoop is linearly scalable whereas SQL is non-linear.
  • Hadoop integrates slowly, while SQL integrates fast.
  • Hadoop writes once, while SQL writes many times.
  • Hadoop possesses a dynamic schema structure, while SQL has a static one.
  • Hadoop-batch processing, SQL-Doesn’t support.
  • Hadoop is difficult to learn but easy to scale, while SQL is the opposite. In Hadoop, data nodes can easily be added.

The choice of tool mostly depends on the data sets you are looking for management.

If you want to work on extensive data collection, choose Hadoop. Whereas if you don’t want to bother yourself with the complexities of advanced data management, you can select SQL.

Hadoop and SQL: Training and Support

Let’s dive into some trainings and supports provided by these two, one being framework and the other being a programming language:

Hadoop

  •   Mailing Lists
  •   Documentation
  •   Community
  •   Apache Software Foundation(Its parts)

SQL

  • There is no official training for SQL, but multiple modules and training lectures are available over the internet.

Hadoop and SQL: Pricing

Taking in account, SQL and Hadop as open-source platforms are considerably less expensive than a proprietary solution.

In an enterprise setting, open source solutions are often far more affordable for equal or better capacity, and they also provide businesses the flexibility to start small and scale.

Hadoop

Hadoop is an open-source platform that is entirely free of cost.

However, there are various costs of Hadoop clusters that perform different parallel tasks on the given data sets.

If we talk about the cost of each group depends on its disk capabilities, with a total node cost of around $1,000-2,000 per TB.

SQL

SQL is also an open-source platform that is entirely free of cost.

But this is only for primary use. Setting up extra SQL features costs more. For example, RDMS uses SQL languages that involve cost in its setup.

Its price can reach thousands of dollars annually if taken in operation properly.

Conclusion

In this article, we have covered the significant critical differences between Hadoop and SQL.

Both these tools help in managing the data but in a unique way.

Hadoop is a framework, while SQL is a programming language. Both these tools have their pros and cons.

Hadoop can handle large sets of data but can write data only once. However, SQL is easy to work with but challenging in terms of scale.

However, which tools best suit you depends on what type of company, what kind of data to handle, your investment, etc.

At RestApp, we’re building a Data Activation Platform for modern data teams.

We have designed our next-gen data modeling editor to be intuitive and easy to use.

If you’re interested in starting with connecting all your favorite tools, check out the RestApp website or try it for free with a sample dataset.

Discover the next-gen end-to-end data pipeline platform with our built-in No Code SQL, Python and NoSQL functions. Data modeling has never been easier and safer thanks to the No Code revolution, so you can simply create your data pipelines with drag-and-drop functions and stop wasting your time by coding what can now be done in minutes! 

Play Video about Analytics Engineers - Data Pipeline Feature - #1

Discover Data modeling without code with our 14-day free trial!

Category

Share

Subscribe to our newsletter

Brian Laleye
Brian Laleye
Brian is the co-founder of RestApp. He is a technology evangelist and passionate about innovation. He has an extensive experience focusing on modern data stack.

Related articles

Build better data pipelines

With RestApp, be your team’s data hero by activating insights from raw data sources.