Data Science is the study and analysis of data. In order to analyze the data, we need to extract it from the database. This is where SQL comes into the picture. Relational Database Management is an important part of Data Science.
While many modern industries have geared their product management with NoSQL, SQL remains the ideal choice for many CRM, business intelligence tools and in office operations.
Many database platforms are modelled after SQL. This is because it has become a standard for many database systems. As a matter of fact, modern big data systems like Hadoop, Spark make use of SQL for maintaining relational database systems and processing structured data.
While Hadoop provides features for batch SQL, Impala and Apache Drill provide interactive query capabilities.
On the other hand, Apache Spark uses the powerful in-memory SQL system to accelerate the processing of queries.
Furthermore, in order to become a data scientist, knowledge of SQL is a must. Many interview questions of Data Science start with SQL queries. Therefore, SQL is essential for Data Science. Therefore, from the above description, we conclude that:
- A Data Scientist needs SQL in order to handle structured data. This structured data is stored in relational databases. Therefore, in order to query these databases, a data scientist must have a sound knowledge of SQL.
- As a matter of fact, Big Data Platforms like Hadoop provides an extension for querying SQL commands for manipulating data through HiveQL.
- In order to experiment with data through the creation of test environments, data scientists make use of SQL as their standard tool.
- 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.