|Application fee:||15.5 USD|
|Certification Body:||Aegis School of Data Science|
|Location:||On-campus (India, Mumbai, Pune, Bangalore)|
|Director:||Mr. Arpit Agrawal|
|Course fee:||232.5 USD|
|Total course fee:||274.35 USD|
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What is SQL?
SQL stands for Structured Query Language which is a computer language for storing, manipulating and retrieving data stored in relational database. SQL is the standard language for Relation Database System. All relational database management systems like MySQL, MS Access, SQL Server, Oracle, Sybase, Informix, DB2 and other database systems uses SQL as standard database language.
Also they are using different dialects, Such as:
MS SQL Server using T-SQL
Oracle using PL/SQL
MS Access version of SQL is called JET SQL (native format ), etc…
However, they all support at least the major commands (like SELECT, UPDATE, DELETE, INSERT, WHERE) to be compliant with the ANSI standard.
Why SQL for Data Analysis?
Since the term big data first appeared in our lexicon of IT and business technology it has been intrinsically linked to the no-SQL, or anything-but-SQL, movement. However, we are now seeing that SQL is experiencing a renaissance. The term “noSQL” has softened to a much more realistic approach "not-only-SQL" approach. And now there is an explosion of SQL-based implementations designed to support big data. Leveraging the Hadoop ecosystem, there is: Hive, Stinger, Impala, Shark, Presto and many more. Other NoSQL vendors such as Cassandra are also adopting flavors of SQL. Why is there a growing level of interest in the reemergence of SQL? Probably, a more pertinent question is: did SQL ever really go away? Proponents of SQL often cite the following explanations for the re-emergence of SQL for analysis:
However, despite the virtues of these explanations, they alone do not explain the recent proliferation of SQL implementations. Consider this: how often does the open-source community embrace a technology just because it is the corporate orthodoxy? The answer is: probably not ever. If the open-source community believed that there was a better language for basic data analysis, they would be implementing it. Instead, a huge range of emerging projects, as mentioned earlier, have SQL at their heart The simple conclusion is that SQL has emerged as the de facto language for big data because, frankly, it is technically superior. Here are key key reasons for this: