How to make SQL a good query tool for you

SQL has been around for half a century, and today it is still used by many business communities. The main reason is that SQL data query language is easy to operate. When SQL is built on IBM, their goal is to create a language that recognizes English. For new users, once you've mastered the commands and meanings of each keyword, SQL becomes very easy to read.

SQL allows faster and easier access to data instead of creating a Python script or Excel spreadsheet. However, one advantage that is often overlooked is that once you know an iterative SQL, it becomes trivial to pick up the other iterations and it opens up a variety of data management techniques. Next, I'll analyze the benefits and reasons for each position in detail below: Product managers, data analysts, data scientists, and data engineers.

Product Manager

Product managers are primarily responsible for the success of their products and to understand the use of users and products in various fields. But it is often difficult to answer these questions in detail, and we often have to rely on data analysts to get these answers. While there are analytical tools such as Mixpanel and Google Analytics, these do not capture all the details of the product. Therefore, learning SQL will allow you to spend the least amount of money and get more details about the product.

Data Analyst

Many data analysts initially used Excel for data management and analysis. This is absolutely true, because Excel is more flexible than SQL iterative analysis. Excel is not good at scaling in the area of scalability. All Excel users experience scalability problems, and when they encounter large CSV files, Excel crashes easily. Then, when you use SQL, you don't have to worry about scalability issues, and you'll have the ability to analyze a larger set of data than before.

Data scientist

Data scientists spend 90% of their time cleaning up data and 10% of time analysis data. The biggest problem for data scientists is not the algorithm or lack of domain knowledge, but the need to quickly get clean data. New data scientists, especially those with computer science backgrounds, tend to use multiple scripting languages to capture and manipulate data. This approach is often more cumbersome, time-consuming, and brittle than using tools that are specifically for data access and manipulation. Learning SQL makes you a more self-reliant data scientist and allows you to broaden the range of accessible data sources and make it easier to iterate.

Data engineer

Data engineers are the backbone of every data pipeline. They collect, ingest, store, and process data in every data pipeline, such as architects, builders, and maintainers. Data engineers take on all the hard work, allowing others to access data safely and efficiently. For engineers, mastering SQL is necessary because relational and analytic databases and SQL interfaces will continue to be the most popular. MySQL, PostgreSQL, Redshift, BigQuery, DashDB, and hive all fall into this category. Unlike the other three groups, data engineers not only need to master write queries, they also need to know how to manage databases through SQL. Because data engineers are often seen as system experts, they also need to know how to optimize query performance.

How to make SQL a good query tool for you