VITAL ROLE OF SQL IN DATA ANALYTICS

INTRODUCTION

We have heard a lot about data science course technology. Along with this, those people

who are interested in grabbing knowledge about data science technology must

have heard that there are many skills that a data scientist should have. The most

important one is SQL. Before getting yourself ready for the SQL, you should know

what SQL is, why it is important in the data science technology and why

companies demand SQL skill for data scientist.

WHAT IS SQL?

SQL stands for the standard query language. Generally, the SQL language is used

in all the relational databases. SQL language is used to manipulate the data which

is stored in the database. Moreover, the SQL is the standard for all relational

databases which are using SQL as their key API. SQL is a non-procedural

language. The commands of the SQL will tell what is performed, but they will not

tell how to do. There are two types of query languages which are procedural and

non-procedural. SQL is one of the non-procedural query languages.

WHY SQL IS IMPORTANT IN DATA ANALYTICS

We know that data science technology is the process of extracting and processing

data. Obviously, we need to have the data in the first hand for extracting and

processing. Where do we get this data from? We get the data from different

sources which include sequential files, weblogs, relational databases, etc. A

relational database is a section where SQL comes into light. If we want to extract

data which is stored in the relational databases, then we need SQL. Most of the

companies store their data analytics course on the NoSQL or SQL database. Many database

platforms became available after the standard query language. The SQL became a

standard for all such platforms and many database systems.

Platforms like Spark, Hadoop, etc. are using SQL for maintaining and structuring

their data in the relational database systems. Along with this, Apache Drill and

Impala provide an interactive capability for the queries. On the other hand,

Hadoop provides many amazing features for batch SQL.

Moreover, Apache Spark uses in-memory system for accelerating the processing

of the queries. Spark is a platform for storing and processing the data which is

launched by the Apache foundation. Spark is said to be more useful as compared

to the Hadoop since it is launched to overcome the shortcomings of the Hadoop.

Apart from this, the in-memory system indicates that Spark retrieves the data

from the hard drive and load it into the buffer memory. Then, the Spark access

the data from the buffer memory instead of the hard drive where it considerably

reduces the time for accessing the data.

Here are the points which tell why a data scientist should have a command on the

SQL.

 A data scientist should know SQL for handling the structured data. We

know very well that the structured data is stored in relational databases.

 Big platforms such as Hadoop, Spark, etc. are being used for data science

technology. These platforms work on the SQL so that a data analytics courses hyderabad

should know SQL for working on such platforms.

 SQL works as a standard tool.

Those students who are interested in grabbing knowledge in data science

technology can go for on data analytics courses hyderabad.