SQL for Data Analysis is a powerful programming language that helps data analysts interact with data stored in Relational databases. By using SQL several companies have built their proprietary tools to fetch information from databases quickly.
This data-driven approach has enabled the industry to channel its growth by analyzing meaningful information to make critical business decisions. Data Analysis has played a vital role in identifying trends and patterns as organizations forecast their business goals by extracting their historical data in databases. And,
This article provides you with a comprehensive overview of the importance of SQL for Data Analysis. It also explains the types of Data Analysis and the potential of SQL queries with databases, along with the limitations it possesses.
Table of Contents
What is Data Analysis?
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Data Analysis helps organizations improve their product and services to enhance customer satisfaction. The process of Data Analysis involves collecting and organizing big data to extract useful information, as it helps in making critical decisions to prosper in business.
With the advent of technology, Data Analysis discovers key parameters, predicts patterns and trends, which leads to business productivity. It also adds value to business processes, as it assists in understanding the meaning behind numbers and figures by presenting an in-depth analysis.
Types of Data Analysis
Data Analysis requires systematically evaluating parameters through various processes. To summarize, Data Analysis can be of four different types:
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1. Statistical Analysis
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Statistical analysis helps in describing and comparing data to uncover trends and patterns. Statistical analysis includes descriptive and inferential analysis to identify measures of central tendency and decipher inferences to get key performance indicators.
2. Diagnostic Analysis
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The Diagnostic analysis aims to identify the root cause of a business problem, as it involves finding positive and negative impacts of decisions taken previously. With Diagnostic analysis, organizations find the ground reality of divergence from statistical analysis.
3. Predictive Analysis
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Predictive analysis assists in having a prior understanding of events likely to occur by estimating trends from historical data. For instance, if a company wants to identify potential risk in business initiatives, Predictive analysis catalyzes the organization to align with the desired business goals.
4. Prescriptive Analysis
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The Prescriptive analysis combines insights from the above three analyses to formulate a plan of action for the organization. With Prescriptive analysis, organizations can address the possible issues with ML models that are trained with prior knowledge of strategies.
Different tools Used to Perform Data Analysis
The primary purpose of Data Analysis is to gather information, process, and derive insights to come with possible business solutions. Data Analysis with software tools have helped speed up this process, and few powerful tools are as below:
1. Python: It is the most widely used general-purpose programming language bundled with huge libraries like pandas, NumPy, and several others, helping in Data Analysis.
2. R: It is the most prominent programming language for Data Analysis that has dominated the market with powerful statistical tools.
3. SAS: Primarily used by big IT companies, Statistical Analysis System (SAS) has helped in performing complex Statistical analysis and generating Reports.
4. SQL: It is the standard programming language used to communicate with Relational databases. SQL for Data Analysis is designed to facilitate the retrieval of specific information through simple queries from a database.
What is SQL?
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In 1970, Raymond FF. Boyce and Donald D. Chamberline at IBM developed a quasi-Relational Database Management System (DBMS) called SEQUEL (Sequential English Query Language). However, the first commercial implementation of SQL was introduced in 1979 by Relational Software for VAX computers.
SQL consists of five basic commands to control structure, perform manipulation for transactions, and Data Analytics. There are many versions and frameworks of SQL, and the most commonly used is MySQL workbench. It is an Open-source tool facilitating an integrated development environment and is widely used for data warehousing, logging, and inventory management.
SQL stores data in a table format, consisting of rows representing a number of records and columns corresponding to various features. All back-end data storage and analysis processes use SQL queries comprising three phases — parsing, binding, and optimization. SQL queries use a simple set of English words to interact with databases compared to other programming languages.
SQL is one of the primary database management languages used by almost every organization to fetch data and develop custom business models. It has helped manage data precisely and deliver optimum results. With improvisation in technologies, storage, and IT solutions, SQL is providing data storage, processing, retrieval, and analysis to support finding insights from complex data.
Benefits of SQL for Data Analysis
- SQL for Data Analysis is easy to understand and learn, thereby making it a user-friendly language.
- SQL for Data Analysis is efficient at fast query processing and helps in retrieving big data from multiple databases efficiently.
- SQL for Data Analysis supports exceptional handling as it provides standard documentation to users.
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Understanding SQL for Data Analysis
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1. SQL for Data Analysis: SQL Queries
SQL queries can be classified into five parts as they perform specific roles to execute queries on any RDBMS system, and they are:
a) Data Definition Language (DDL)
DDL commands include create, alter, drop, rename and truncate, dealing with the structure of the databases. It operates on database objects like views, tables, indexes, and triggers.
b) Data Manipulation Language (DML)
DML commands include insert, update, and delete operations to modify data in existing databases.
c) Data Query Language (DQL)
This command includes a select operation to retrieve data matching criteria specified by the user. To condense data efficiently, DQL commands also involve nested queries.
d) Data Control Language (DCL)
This command is used by data administrators to grant and revoke permission to access data in the organization’s database.
e) Transaction Control Language (TCL)
TCL commands help in managing transactions in databases to commit or roll back a current transaction. TCL command is used to commit a DML operation, and it has the ability to club multiple commands in a single operation.
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2. SQL for Data Analysis: SQL Joins
The SQL join clause is used to combine different tables in databases, where JOIN is made using a Primary and Foreign key. There are four major joins which include inner, left, right, and full join used in combination with the ‘from’ clause.
A Primary key is a column in a table that acts as a unique identifier in both tables, whereas a Foreign key specifies a link to the Primary key in another table. For instance, it is more likely for customer-id as a column in the sales and customer detail table, making it the Primary key. The choice of SQL joins depends on the analysis to be performed.
3. SQL for Data Analysis: SQL Aggregations
The sole purpose of Data Analysis is to get meaningful information, and a process of combining multiple entities can be performed by SQL aggregation query. Aggregation is a deterministic function, and it involves calculation for a set of values giving a single entity.
Data Analysis with the aggregation function untangles insights from data, as it operates on multiple rows, giving us the significant quantity from columns in the table. SQL comes with some standard functions like count, sum, min, max, and avg operation.
These functions are often used in conjunction with ‘groupby’, ‘orderby,’ and ‘having clauses to evaluate specific columns.
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4. SQL for Data Analysis: SQL Views and Stored Procedures
SQL views are virtual tables whose content is obtained from an existing table, and it optimizes the database to provide an additional level of security by restricting users from fetching complete information from the database. Views represent a subset creating a window on data and are used to get rid of basic queries.
Stored procedures are created to process one or more DML operations on a database and are also capable of taking user input to perform a group of SQL commands. Data Analysis often requires a repeated process to make reports, and stored procedures serve as a boon to overcome this problem.
Limitations of SQL for Data Analysis
- SQL lacks a user interface making it complex while dealing with huge databases.
- With SQL, you cannot perform complex statistical analysis, which is vital for any Data Analysis task.
- SQL requires data in a row-column format where schemas represent data types of columns; hence it fails to process unstructured data.
Conclusion
This article talks about the importance of SQL for Data Analysis. It gives a brief overview of SQL and the way it facilitates the analysis of data in business processes.
SQL is accompanied in every data-driven sector, protruding the significance of big data computation. Considering the power of SQL in extensive database processes, Apache Hive designed SQL as the front end to integrate with Hadoop to process and analyze petabytes of data.
As developers and analysts require to access data from databases, SQL has become a regular part of any data-driven organization.
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Amit Kulkarni specializes in freelance writing within the data industry, by creating informative and engaging content on data science by using his problem-solving and analytical thinking ability.