Are you a Data Analyst? If yes, then do you know there is a universal language accepted that is one of the most important prerequisites for data analysis?
If you haven’t guessed the answer already, yes, we are talking about SQL. The Data Definition Language (DDL) is essential for analyzing and retrieving accurate data from databases.
With nearly 11 million job opportunities being generated by 2026, the Data industry is growing day by day. With its growth, your chance to grow your career into one of the most in-demand industries also unfolds 2X. SQL Data Analytics plays a key role in becoming a data analyst. Therefore, in this blog, we will learn about SQL and why it is a necessity for every data analyst. By the end of the blog, you will have answers to how SQL can change your current position in the industry.
What exactly is SQL?
Structured Query Language, also known as SQL, was introduced to the tech world in the 1970’s. Developed by IBM researchers Raymond Boyce and Donald Chamberlin, this language is used prominently in various industries, such as data analytics, e-commerce, HR, data sciences, government services, and education.
After the invention of the language, SQL was adopted by ANSI (American National Standards Institute) in 1986. Following the next year (1987), being standardized by ISO (International Organization for Standardization).
We live in a world where data is produced every minute of the day. Of course, everything that is created in the process as a whole is not fit for purposeful usage. This is where SQL comes in. With its easy syntax, this language is the most approached solution for analyzing and producing useful components from the raw data. SQL provides brushed-up data from the records and is efficient in querying and managing the data.
However, these functions in an organization have become a necessity in the current digital times. Being able to analyze and interpret data is no longer an option. There are several ways SQL queries for extracting sub-sets from the database that help in cleaning and standardizing the data. To learn more, let’s dive into how SQL works in the data analytics industry and the five major reasons a person pursuing this position should learn SQL intently.
Reason 1: SQL-foundational language for Data Analysts
The core importance of analyzing raw data is to find patterns for efficient decision-making. As a data analyst, your primary goal is to cleanse the data and contribute to making an informed decision that empowers the current position of the company. Extracting meaningful information from the data helps an organization to know its consumers better and produce results that serve consumer needs. So, how exactly does SQL in data analytics contribute? Let’s find out!
SQL Functions
SQL functions are designed to provide subsets of larger data while keeping specific conditions in the loop. It helps in calculating data and performing transformation by combining different data and producing efficient results. Some of the common key functions are:
- SELECT (used for data selection)
- UPDATE (used for changing information)
- INSERT INTO (used for adding data)
- WHERE (used for filtering information)
- JOIN (used for combining data)
- DELETE (used for removing data)
SQL Databases
The core reason for using SQL data analytics is to filter information into useful subsets of data. SQL has the ability to refine the existing information and simplify the solution to define a pattern. It has the ability to work with versatile data sources like data warehouses, relational data and data lakes. Thus, it also allows you to read and understand different types of data from various sources. Some of them are named in the image given below.
Considering the versatility of the platform, SQL provides a wide range of calculations and aggregations on the data. This platform helps in binding information with speed that delays insights and increases accuracy.
Reason 2: Incorporate Analytical Tools for Fresh Data
Today’s digital world requires you to know the dynamics of changing data while making efficient decisions. Real-time and fresh data play a vital role in fulfilling the demand. SQL is one of the tools that seamlessly integrates powerful analytics tools. Tools like Power BI, Tableau and Google Data Studio. These applications ensure that the details of data are brought forward and help data analysts visualize data.
From creating interactive dashboards to finding needed data through visualization, these platforms with SQL lead the way to refining and optimizing data. For example, SQL server analysis is a live connection that can be connected to Power BI. This is exactly like connecting to an Azure Cloud Database. Keeping the consistent need for real-time data in mind, it is important to understand that this kind of data contributes heavily to creating dynamic reports and the most current data. This type of integration for getting fresh data is a valuable source to companies dealing in industries like E-commerce, where they interact with consumers on a daily basis. These companies need quick information on the response and feedback to upgrade the quality of service and products.
Reason 3: Efficient Data Extraction
In the information age, it is necessary to shorten the data to be precise. This procedure enables data analyst to use needed data as a solution to provide better performance and upgrade their decision-making.
Given that the primary use of SQL language is to help manipulate databases and retrieve information, data extraction is the most basic step for data analysts. Efficient data extraction with the help of SQL leads to analysts highlighting the pattern and reporting the concerns to hire-ups for the right decisions.
SQL data analytics allows one to pick up necessary data from the source databases and retrieve important queries in the process. SQL is used for cleaning, transforming, filtering, and sorting, and it gives a guide to finding solutions and implementing them in the future.
Additionally, unlike other platforms, SQL allows millions of rows to be queried quickly, unlike struggling with massive spreadsheets. This is useful in terms of dealing with bigger projects with huge bandwidths that require accurate data in a given timeline. With SQL, data extraction becomes easy and does not become a cumbersome task, streamlining the process prominently.
Reason 4: Security
SQL Server security has various levels, such as physical, operating system, and application. SQL has several built-in security features, including encryptions, which are measures to protect databases and refined data. Having knowledge of handling SQL queries to build authentication permissions for users is necessary.
SQL helps data analyst to protect data at various levels and manage information accordingly. Queries in this language allow one to encrypt information and control its access within the team or consumers on multiple levels. This is a must feature for any type of industry as companies need their data to be saved from any type of malware or manipulation threats.
Reason 5: Higher Earning Potential
SQL is a powerful language that helps data analyst to perform data manipulation and aggregation. Its accuracy in interacting with large amounts of data while presenting powerful results helps data analysts perform complex queries and identify patterns.
Being one of the universal languages for bringing out insights, SQL in data analytics is a must. Below are a few more reasons for a data analyst to be fluent in SQL:
- Helps manage vast amounts of data in less time.
- A variety of tools are integrated with the language so one can carry out the required results in different setups.
- High levels of interactivity and accessibility while seamlessly working with different types of data from multiple sources.
- Easy to comprehend and implement.
- Large files are easy to figure out, unlike spreadsheets. It just needs one to know the right query.
- Design and retrieve data from the database while entering customized queries.
- Monitor and control access to data.
These benefits not only highlight the need to learn this language for data analysis but also showcase the potential it adds to one’s position. The average salary a data analyst earns in 2024 on an average year is $1,09,938 USD.
Apart from this, SQL also opens doors for new opportunities and roles like business analyst and data engineer.
Conclusion
In today’s time, companies are constantly shifting their operations and every possible regular task online. Cloud computing is common, and every department deals with a high amount of data on a daily basis. To become an efficient data analyst who is able to summarize and bring out the needed results from chunks of information, SQL is a non-negotiable language in which to be fluent.
Specializing in data analytics, SQL will help you perform complex analyses and contribute more effectively to finding solutions and making the required fruitful decisions. This one addition can further translate into higher job positions and ultimately increased earning potential.
Now that you are brimming with information about why data analytics with SQL is such a core part of becoming an outstanding professional in the IT industry, we at Microtek Learning have experts to teach you!
We provide Microsoft SQL Certification. Our certified trainers are experienced in simplifying the concepts and helping you at each step to understand and practically implement the language. If you are interested in taking a step ahead and upskilling your journey, talk to us here!
