As we move into 2022, businesses will continue to look for ways to become more data-driven. This means that knowing how to use Structured Query Language, commonly called SQL or 'Sequel', will remain an essential skill for data scientists, analysts, and developers. In this post, we'll provide an overview of the access language and take a look at some of the most important SQL trends for 2022. So if youre looking to expand your skills to SQL to become a more complete data professional in 2022, then read on!
This overview is designed for those with some technical knowledge but no specific, formal SQL training. After reading this overview, you should be more familiar with the state of SQL today, plus where the language and development community are going in 2022.
IBM researchers Raymond Boyce and Donald Chamberlin created the SQL programming language in the 1970s. Then, following Todd's seminal publication of "A Relational Model of Data for Large Shared Databases," SEQUEL, the programming language, was born.
Todd's idea was that all data in a database should be represented as relations. Based on this hypothesis, Boyce and Chamberlin came up with SQL. According to author Malcolm Coxall in "Oracle Quick Guides (Cornelio Books 2013)," The original SQL version was intended to modify and retrieve data from IBM's first relational database known as "System R."
A few years later, the SQL language became available to developers at-large with Oracle V2, a proprietary variant of the SQL language and commercially released in 1979. Other major database software companies soon followed and developed commercial relational databases using SQL access, such as Ingres, Informix, and Sybase.
It took another 17+ years, in January 1997, for the first open-source database project, PostgreSQL to be released. It was initially named POSTGRES, referring to its origins as a successor to the Ingres database developed at the University of California, Berkeley, and commercialized in the 80s. Then, in 1996, the project was renamed PostgreSQL to reflect its SQL support.
The SQL language has since been designated the preferred language for relational database communication by American National Standard Institute (ANSI) and the International Standards Organization (ISO). While vendors modify the language to suit their needs, most SQL applications are based on the ANSI-approved version.
Easy to Learn and Use
SQL is praised for its simplicity by using declarative statements like SELECT, in contrast to other programming languages that demand a high-level conceptual knowledge and memorization of the steps required to complete an activity.
It's easier to comprehend than other languages' complex syntax thanks to English words that are simple to understand compared to memorizing strings of numbers and letters in foreign languages.
SQL is a great language to start with if you're new to programming and data science. The small syntax allows you to query data quickly and extract insights from it.
Understanding your dataset
Learning SQL will give you a solid understanding of relational databases and enable you to master the foundations of data science.
SQL will assist you in researching your data thoroughly, visualizing it, and knowing how your data is structured.
It will allow you to discover missing values, identify anomalies, NULLS, and your data format.
SQL will let you play with your data, get thoroughly acquainted with it, and learn how the values are distributed and organized due to slicing, filtering, aggregations, and sorting.
Integrates with scripting languages
As a data scientist, you must be careful in representing your data to comprehensible it to your team or organization. You must carefully display your data in such a manner that others readily understand it.
Other programming languages like R and Python work well with SQL. You may simply integrate a code package as a stored procedure to easily mix SQL and Python to do your task comfortably.
Also, specialized connection libraries for SQL such as SQLite and MySQLdb can be quite beneficial in connecting a client app to your database engine, allowing you to interact with your data.
Manages huge volumes of data
Today, most data analytics deals with massive amounts of data stored in relational databases. Working with such quantities of data necessitates using higher-level solutions to manage it rather than simple spreadsheets. As the amount of datasets grows, using spreadsheets becomes unreasonable. Data in the relational model and managed by SQL is the best solution for dealing with vast volumes of data.
When working with relational databases, you don't have to be concerned about pools of data in SQL. Instead, it can query and give useful insights from the data.
Get Started Learning SQL
Github is a great place to find a free, open-source database to install, practice, and learn SQL. There are many open-source SQL database management systems available. For example, SQLite is a popular free option and uses less system resources than a full implementation of Oracle, for example.
Once you have installed an open-source SQL database, choose a SQL tutorial that suits your needs. You can find some online courses on platforms like Udemy or Coursera. In addition, you can watch free videos on YouTube. Another option is to buy a book about SQL programming. It will be easier if the book is written for your particular open source SQL database management system. Here is a SQLite book on Amazon.
Then, try to write queries on your own. It is not easy at first, but you will get better with practice. Do not hesitate to ask questions on GitHub discussion forums, Slack channels, or Reddit if you have any doubts. Open-source communities are known for being generous in sharing knowledge and 'paying it forward.
Here is an overview of frequently used SQL commands and features.
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Data Definition Language (DDL): It contains commands which define the data. The commands are:
create: It is used to create a table.
drop: It is used to delete the table, including all the attributes.
alter: It is a reserve word that modifies the table's structure.
rename: A table name can be changed using the reserver 'rename' -
Data Manipulation Language (DML): It contains commands to manipulate the data. The commands are:
select: get data from a database table
insert: This command is generally used after the create command to insert a set of values into the tables.
delete: A command used to delete particular tuples or rows or cardinality from the table.
update: It updates the tuples in a table.
Triggers: Triggers are actions performed when certain conditions are met on the data.
A trigger contains three parts:
event The change in the database that activates the trigger is an event.
condition A query or test run when the trigger is activated.
action A procedure executed when the trigger is activated and the condition met is true. -
Client-server execution and remote database access: Separates the data (server) from the application (client). A database will generally have one or more servers, which provide the storage space, manage the data, create backups, etc. The clients are software programs running on a user's computer.
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Security and authentication: Rather than allowing the user to access every aspect of the database, SQL allows users to perform operations on specific tables such as retrieving information about certain fields, sorting it into different orders, updating specific records, or deleting them altogether. This limits data access to specifically authorized tables rather than the whole database.
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Embedded SQL: SQL provides the feature of embedding host languages such as C, COBOL, Java for query from their language at runtime.
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Transaction Control Language: Transactions are a critical element of DBMS, and to control the transactions, TCL is used with commands like commit, rollback, and savepoint.
commit: Saves the database at any point whenever the database is consistent.
rollback: Rollback/undo to the previous point of the transaction.
savepoint: Goes back to the previous transaction without going back to the entire transaction. -
Advanced SQL: The current features include OOP ones like recursive queries, decision-supporting queries, and query supporting areas like data mining, spatial data, and XML(Xtensible Markup Language).
Agnostic Queries
SQL agnostic query tools are becoming more popular due to their flexibility when working with data in multiple formats across multiple databases. ShardingSphere's open-source SQL Parser database plug-in is an excellent example of this trend.
It allows users to work with data regardless of the underlying open-source SQL database. In addition, you can use this tool to parse SQL queries, allowing developers to easily analyze and modify their code. ShardingSphere's SQL Parser is designed with any open-source SQL database in mind, making it a versatile tool for developers.
It can be used to quickly parse complex SQL queries, making it easier to understand and modify the code. Additionally, the tool can generate execution plans for queries, helping developers optimize their queries for performance.
Open-Source SQL
Open-source versions of SQL and SQL databases are continuing to outpace industry giants Microsoft and Oracle for the new class of cloud-based, data-powered apps being built. This is due, in part, to the open-source community development model, which can quickly adapt and improve products using the collective 'wisdom of the crowd'. Additionally, open-source solutions are often more affordable than their proprietary counterparts.
For example, the open-source database PostgreSQL has seen significant growth in recent years. According to the latest figures from DB-Engines, PostgreSQL is now the fourth most popular database in the world, after MySQL, Microsoft SQL Server, and Oracle Database. This growth is largely because PostgreSQL is open-source, mature, and freely available for download. But it's also just as powerful and feature-rich as a commercial Oracle database.
Democratization of Data Access
SQL is a powerful language that can help you collaborate and write reports. While it can be intimidating for beginners, more non-technical users are starting to learn SQL to get the most out of their data.
For example, product managers and analysts use SQL to analyze their data. They might still lean on Excel spreadsheets or Google Docs, but they know it's time to learn SQL if they find themselves creating more complicated queries against ever-larget datasets.
SQL is open source, which means it's available for anyone to use and customize. This makes it a popular choice for analysts and developers who want to build their reports and applications.
SQL Collaboration
Open source tools like DBeaver are increasing in popularity for collaborative SQL development. These tools make it easy to share your work with other developers and make changes and corrections collaboratively. This can save time and improve the quality of your SQL code.
DBeaver is a free, open-source tool available for Windows, Mac, and Linux. It supports all of the most popular open-source databases, including MySQL, PostgreSQL, Oracle, Amazon Redshift, H2, DerbyDB, and more.
Collaborative SQL development is an efficient way to work with other developers. Working on open-source tools like DBeaver can save time for programmers by allowing them to share code and corrections easily. Additionally, open-source tools are frequently updated with new features, making it easier for developers to keep up with the latest trends. For these reasons, open-source collaborative SQL development is likely to increase in popularity in the years to come.
As the world of technology continues to change, so do programming languages. SQL is a universal language that can be used by programmers and non-programmers alike. It's easy for people without computer science backgrounds to get started with coding because it does not require any previous experience or knowledge of other programming languages like JavaScript or Python. This article has shown you why there are plenty of reasons to learn how to code in this popular language--and what steps you should take next!