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SQL for Data Analytics - The Ultimate Guide

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SQL is one of the most widely used and adaptable programming languages, combining a surprisingly simple learning curve with a complicated depth that allows users to develop advanced data analytics tools and dashboards.

SQL has been modified into a variety of proprietary tools, each with its own purpose and niche market, to easily construct and interact with databases, including the popular MySQL, Microsoft Access, and PostgreSQL.

Table of Contents:

What is SQL?

SQL is a standard language used to store, manipulate, and retrieve data from databases. It has been available since the 1970s and is now the most frequent way to access data in databases.

SQL provides a number of operations that enable users to read, manipulate, and alter data. Though software engineers often use SQL, it is also popular with data analysts for several reasons:

  • It’s simple to grasp and learn in terms of semantics.
  • Analysts do not need to copy data into other programs because they can access enormous amounts of data immediately where it is kept.
  • SQL data analysis is easier to audit and duplicate than spreadsheet technologies.

SQL is ideal for doing aggregations similar to those found in an Excel pivot table—sums, counts, minimums, and maximums, and so on—but on considerably bigger datasets and over numerous tables at once.

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SQL for Data Analytics

SQL for Data Analytics: SQL Queries

SQL queries are divided into five categories based on their functions in executing queries on any RDBMS system, and they are as follows:

SQL Queries

  • Data Definition Language (DDL ) – DDL commands that deal with database structure include create, alter, drop, rename, and truncate. It works with database objects such as views, tables, indexes, and triggers.
  • Data Manipulation Language ( DML ) – Insert, update, and remove DML commands are used to edit data in existing databases.
  • Data Query Language ( DQL ) – This command comprises a select operation that retrieves data that matches the user-specified criteria. DQL commands use layered queries to efficiently condense data.
  • Data Control Language (DCL ) – Data administrators use this command to grant and revoke authorization to access data in the organization’s database.
  • Transaction Control Language ( TCL ) – TCL commands aid in database transaction management by allowing you to commit or roll back a current transaction. The TCL command is used to submit a DML operation, and it can combine numerous commands into a single operation.

SQL for Data Analytics: SQL Joins

SQL for Data Analytics SQL Joins

The SQL join clause is used to join various database tables, where JOIN is performed using a primary and foreign key. The four major joins are inner, left, right, and full, which are used in conjunction with the ‘from’ clause.

A Primary key is a table column that serves as a unique identifier in both tables, whereas a Foreign key indicates a link to a Primary key in another table. For example, it is more likely that the customer-id will be a column in the sales and customer information table, making it the primary key. The SQL joins used are determined by the analysis that will be performed.

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SQL for Data Analytics: SQL Aggregations

The main goal of Data Analytics is to obtain useful information, and SQL aggregation queries can conduct the process of integrating many entities. A deterministic function known as aggregation involves calculating a set of variables to produce a single entity.

As it functions on numerous rows, Data Analytics with the aggregation function untangles insights from data, providing us with a significant quantity from columns in the table. SQL includes standard functions such as count, sum, min, max, and average.

These functions are frequently used in conjunction with the ‘groupby,’ ‘orderby,’ and ‘having clauses to evaluate certain columns.

SQL for Data Analytics: SQL views and Stored Procedures

SQL views are virtual tables whose content is derived from an existing table, and it optimizes the database to give an additional level of security by preventing users from retrieving complete information from the database. Views are used to eliminate fundamental searches by representing a subset and creating a window on data.

Stored procedures are written to perform one or more DML actions on a database, as well as to accept user input and execute a set of SQL instructions. Data analysis frequently necessitates a repetitive process in order to generate reports, and stored processes can assist with this issue.

Benefits of SQL for Data Analytics

  • SQL for Data Analytics is simple to grasp and master, making it a user-friendly language.
  • It is effective in processing queries quickly and efficiently retrieving large amounts of data from numerous databases.
  • It allows for exceptional handling by providing users with standard documentation.
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This article discusses the significance of SQL in data analysis. SQL is present in every data-driven industry, emphasizing the necessity of large data computation.

Given the potential of SQL in large database processes, Apache Hive built SQL as the front end to interface with Hadoop to process and analyze petabytes of data.

SQL has become an essential aspect of any data-driven organization since developers and analysts need to access data from databases.

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About the Author

Principal Data Scientist

Meet Akash, a Principal Data Scientist who worked as a Supply Chain professional with expertise in demand planning, inventory management, and network optimization. With a master’s degree from IIT Kanpur, his areas of interest include machine learning and operations research.