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.
Table of Contents
What is Hadoop?
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Hadoop is an open-source software framework that is used in the processing and storage of data for Big Data applications in clusters of computer servers built from commodity hardware. It provides massive storage for any kind of data, an enormous processing power, and can take concurrent tasks or jobs by using parallel processing.
Hadoop is made up of four components namely: Hadoop Distributed File System (HDFS) which allows data to be stored in an easily accessible format across a large number of clusters, MapReduce which is used to process data by mapping them into a suitable format for analysis, Yet Another Resource Negotiator (YARN) that is responsible for managing computing resources in clusters and running the analysis, and finally the Library that contains Hadoop libraries and utilities needed by other Hadoop modules.
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Hadoop is used to support advanced analytics initiatives, which include predictive analytics, data mining, and machine learning hence, it is used by Big Data corporations such as IBM, Microsoft, Cloudera, Pivotal Software, and Hadapt, Amazon Web Services, etc. For more information about Hadoop, visit here.
Understanding the Key Features of Hadoop
Let’s look at some of Hadoop’s key features:
- Hadoop can process structured or unstructured data and can store a huge volume of data quickly and efficiently.
- Hadoop has enormous computing power as a result of its computing model that makes use of multiple computing nodes.
- Data stored in Hadoop is fault-tolerant. In case a node becomes faulty, the computing is distributed to other functional nodes, and data is backed up automatically by the active operating nodes to ensure functionality.
- Scalability is also amazing in Hadoop. Even when your data begins to grow at a very high rate, Hadoop lets you seamlessly add more nodes to your system.
- Hadoops’ Open-source framework is free of cost which makes it widely accepted globally.
What is SQL?
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Structured Query Language (SQL) is an Open-source Domain-specific programming language used in computing to handle data management and processing data streams in Relational Database Management Systems (RDBMS).
SQL was initially developed at IBM in the early 1970s and by 1979 Oracle developed a commercial implementation of SQL as a declarative language for analytical queries such as creating, storing, and extracting data from RDBMS. Examples of SQL-based databases include Oracle Server, SQL Server, MySQL, etc.
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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.
Hadoop vs SQL: 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.
Hadoop vs SQL Comparison: 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.
Hadoop vs SQL Comparison: 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.
Hadoop vs SQL Comparison: 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.
Hadoop vs SQL Comparison: 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.
Hadoop vs SQL: Schema Structure
Hadoop: Hadoop supports dynamic schema structure.
SQL: SQL supports static schema structure.
Hadoop vs SQL Comparison: 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.
Hadoop vs SQL Comparison: Fault Tolerance
Hadoop: Hadoop is highly fault-tolerant.
SQL: SQL has good fault tolerance.
Hadoop vs SQL Comparison: 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.
Hadoop vs SQL Comparison: Integrity
Hadoop: Hadoop has low integrity.
SQL: SQL has high integrity.
Hadoop vs SQL Comparison: 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.
Hadoop Vs SQL Comparison: 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.
Hadoop vs SQL Comparison: 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.
Hadoop vs SQL Comparison: 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.
Hadoop vs SQL Comparison: 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.
Hadoop vs SQL Comparison: 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.
Hadoop vs SQL Comparison: 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.
Hadoop vs SQL Comparison: 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.
Hadoop vs SQL Comparison: Hardware Configuration
Hadoop: In Hadoop, commodity hardware installation is required on the server.
SQL: With SQL supported system, propriety hardware installation is required.
Hadoop vs SQL: 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.
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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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