Importance of SQL for Data Analytics

What is Data Analysis/Analytics?

Data Analytics is a technique focused on identifying raw data and drawing valuable insights that impact well over Business Growth and processes. In this New Era, to run a successful business, proper handling of your data is a must. good insights from data even improve the quality of customers and the help in spotting their behaviors and interests.

Thus, Data Analytics comprises tools, processes, and techniques which fetch raw data from various resources and then draw valuable insights leading to increased productivity. If we says in the practical sense, it is aimed to Improve customer Satisfaction, Elevate Performance, Speed up the Time Taken for Delivery, and much more. Well, Data Analytics software typically integrates Machine Learning and Deep Learning Algorithms that automates the analysis with very little efforts.

Data Analysis is the subset of Data Analytics. That means Data Analysis comprises some steps that professionals must carry out before a full-fledged analysis of the data. These steps include Examining, Cleaning, Transforming and Modeling data to be analysed at a later stage. Once this entire process of Data Analysis in done, Data Analytics Tools are used to draw insights and details.

What is SQL?

SQL (Structured Query Language) is a Database Management language. It has been around since the 1970s and is still the most popular way to access data in Databases today. SQL provides a number of operations that enable users to read, manipulate, and alter data. SQL is widely used by engineers in software development, but it is also widely used and utilized by Data Analysts, the reason is:

• Analysts don’t have to copy data into other programs because they can access enormous amounts of data immediately where it’s kept.
• Data Analysis done in SQL is easy to audit and reproduce when compared to spreadsheet methods. This eliminates the need for analysts to search for the cell containing the formula error.
• From a semantic standpoint, it is simple to grasp and learn.

SQL help to conduct aggregations similar to the Excel pivot table like: sums, counts, minimums and maximums, and so on. But over much bigger Datasets and no numerous Tables at once.

SQL Important for Data Analytics?

Structured Query Language (SQL), is a powerful Query Language preferably meant to store the data or fetch the data from Relational Database Management Systems (RDBMS). SQL consists of a set of actions, which when executed, assemble a set of data. Data Analysts rely on targeted techniques performed over the data to get valuable insights. The set of general actions include Select, Update, Delete, Add, Modify, Alter, and some clauses.

SQL stores the data in a particular Database type, and provides flexibility to access the data or update it easily. Data Analysts, before performing any analysis, need a dataset. They can create datasets, and perform actions like updating the data or deleting it, only if they know SQL. Even to handle Structured Data, Data Analysts need to learn SQL.

For professionals who might be looking for a testing environment to perform experiments on data, standard SQL Tools are often must-haves. Data Analysts also need SQL knowledge to understand data available in Relational Databases like Oracle, Microsoft SQL, and MySQL. It is essential to learn SQL for Data Preparation and Wrangling. For instance, if Analysts need to use Big Data Tools for analysis, then SQL is the language they must know.

Understanding the Importance of SQL for Data Analysts.

Data Analysts use SQL to manipulate the data, understand the data, and access, read and analyze the data before storing it in the database. Later, with the help of this analysis, we can derive constructive business strategies. To understand the relevance of expert SQL Data Analysts in an organization, it is important to understand in detail the reasons why SQL is significant in the process of Analytics.

• Easy to Learn and Apply: Data Analysts are constantly looking for prompt solutions that are not time-consuming and can be applied easily. SQL is easy to learn as compared to other Programming Languages. Many Programming Languages are complex and need strong memorization of steps to use them. SQL is simple to learn, and the easiest Programming Language to implement even for complex computations.
• Know your Dataset: For Data Analytics, the major base is the dataset in consideration. This means if the dataset contains NULL values or missing values then the insights drawn from it are bound to be erroneous. Data Analysts need to understand their dataset, which is made easier to traverse with SQL. The dataset contains several records, patterns, and Analysts can even select attributes that are related to the model they are considering.
• Integration: SQL has stronger impacts on Data Manipulation, Data Access, and Data Querying but then it lacks in creating visualizations. Data Visualization is essential in the entire process of Data Analytics. Thus, it is better to integrate SQL with popular Data Analytics Scripting Languages like Python and R. Also, the Client Apps securely connect with Database Engine via the in-built Libraries of SQL to get approval for working on datasets.
• The volume of Data: A dataset consists of thousands of records that are impossible to manage through old traditional methods. Thus, instead of using Spreadsheets to analyze the dataset, it is important for an Analyst to use SQL constructively to manage data. SQL is the best-recommended solution to deal with huge datasets and source optimal solutions.

SQL Data Analyst: Software Tools Used

The followings are the list of tools commonly used by data analysts:

• Microsoft Power BI
• Tableau
• MySQL
• Microsoft Excel
• Apache Spark
• Jupyter Notebook
• Python
• Metabase

Data analysis tools make analysts’ work more efficient and productive. Data analysts need to be familiar with the available data analysis tools to choose the right tool for their business task.

Summary

In the end, we conclude that SQL plays an important role in Data Analytics. As a matter of fact, the modern big data platforms are emulating SQL to process organized data that is generated alongside the unstructured one. In order to carry out data analytics with the data that is stored in relational databases like Oracle, Microsoft SQL, MySQL, we need SQL. SQL is also essential for carrying out data wrangling and preparation. Therefore, when dealing with various Big Data tools, you will make use of SQL.