Why SQL is important in Data Science?

Many veterans of this discipline would argue that SQL is essential to the perpetuation of data as a commodity in the digital age. But what factors make it so important today?

Databases aren’t new; it’s only that the Big Data era has injected a sense of newness and urgency into the world of databases. Basically, there are three common types of database: hierarchical, network, and relational. A relational database is independent of its applications — the database structure can be modified without impacting any connected applications. In a relational database, you can define complex relationships between tables, and you can access the relations directly.

While it may be over four decades old, SQL (structured query language) remains relevant in the 21st century thanks to a number of key advantages that it offers over the alternatives.

First and foremost it is accessible to almost anyone since it relies not on some obtuse skillset or arcane coding strings but instead harnesses declarative statements to create queries. As a result, anyone who wants to begin a career in data science should be able to learn about the ins and outs of SQL fairly quickly, which is good news because of its cornerstone status in this profession.

Features of SQL

Scalability

At the simplest level it is possible to catalogue and analyse data using spreadsheet software, although this is only really relevant in cases where the volumes of information being managed are minimal. In the context of the rapid rise of big data, it makes far more sense to let SQL bear the burden, since it is far better equipped to handle very large datasets without crumbling under the pressure.

Transferable skills

From a career perspective, any data scientist who learns to bend SQL to their will can reliably expect to find employment across a broad selection of industries, since this language is used to orchestrate databases in everything from healthcare to finance and beyond.

Versatility

The aforementioned accessibility and scalability of SQL make it even more of an asset to data science at a time when there are so many potential operating ecosystems that it might be expected to occupy.

While in the past it could reliably be expected that databases would be running on local hardware, the age of cloud computing, remote data centres, hybrid setups and superfast connectivity has changed the game significantly.

reasons we should go for SQL

1. It’s Becoming a Standard to Use SQL in Data Science
   SQL proficiency is a basic requirement for many data science jobs, including data analyst, business intelligence developer, programmer analyst, database administrator, and database developer. You’ll need SQL to communicate with the database and work with the data. Many technical interviews for these jobs test SQL skills in some way, usually in the whiteboard test (i.e. where you solve a problem by writing code on a whiteboard).
2. SQL Integrates with Scripting Languages
   Is SQL important in data science? Sometimes it will give you all the insights you need. But you may want to take it further. Maybe you want to summarize the data in a particular way and then create a nice data visualization for your web application. Or maybe you want to use the query result as one of the inputs for the next step in some code you’re writing. Or maybe you have a working script package and you want to integrate it into the SQL environment.
   Luckily, you can convert the result set into an XML or JSON format and use it for subsequent data consumption. Depending on the version of SQL you use, specialized connection libraries (such as SQLite and MySQLdb) allow you to CONNECT A CLIENT APP TO YOUR DATABASE. You can even integrate your code package as a stored procedure. This makes exploratory data analysis, algorithm building and tuning, and model evaluation and deployment a lot easier.
3. SQL is Declarative
   Machine learning involves self-learning algorithms — algorithms that can adjust their performance without having the process hard-coded in a set of logical rules. In other words, machine learning lets you specify your objective without specifying how it is done. SQL works in a similar way.
   SQL is nonprocedural and designed specifically for accessing data. The primary difference between SQL and conventional programming languages (R, Python, Java, etc.) is that SQL statements specify WHAT data operations should be performed rather than HOW to perform them. When you write Python script, the Python interpreter reads your program line by line and carries out the instructions in each line. If you’ve ever written any code, you know how long that takes!
   In contrast, SQL’s concise set of commands save time and reduce the amount of programming required to perform complex queries. Instead of directing a compiler along each step of the way, you simply tell it what you want it to do.
4. SQL Prepares You for NoSQL
   How important is SQL for data science? If you’re planning a serious data career, there’s one more reason to start with this language. Big Data’s velocity and volume have made NoSQL databases more popular. NoSQL is prized for its scalability and flexibility, but because it has evolved so quickly there is currently no standard engine or interface. Tackle SQL first, and learning NoSQL will be a lot easier. Once you have a solid SQL foundation, you’ll appreciate the limitations as well as the advantages of NoSQL (i.e. NoSQL uses flexible document objects rather than SQL’s predetermined, fixed tabular schema).