Structured Query Language or SQL continues to be one of the most sought after programming languages. Originally written in the 1970s by two IBM researchers, Raymond Boyce and Donald Chamberlin, it is used for managing databases. It is more in demand by employers than other popular programming languages like Python and Java. With companies’ ever-increasing need to capture, analyze, and leverage data, the continued demand for SQL skills is no surprise since SQL is one of the most versatile and powerful data querying languages for relational database management systems (RDBMS). As organizations continue to harness insights from manipulating data across relational and nonrelational databases, understanding SQL and Not Only SQL (NoSQL query languages are ever more critical. Professionals that have experience in using SQL to extract insights from relational databases will deliver much-needed value to businesses today as they become more data-driven.
Almost every role that involves strategic decision-making in today’s workforce, particularly at technology-centric companies, will require working with data stored in databases, and I am very excited to master this skill in this program.
In this Nanodegree program, I acquired the necessary skills to work with both relational and non-relational databases in today’s data-driven workplace through hands-on practice building, manipulating and analyzing every database and data related.
In particular, I learned how to :
- leverage the power of SQL to pull insights from relational databases.
- about the difference between relational and non-relational databases.
- differentiate the use of relational databases versus non-relational databases like MongoDB and Redis.
- execute core SQL commands to define, manipulate, aggregate, and join data and data tables.
- write advanced SQL queries (such as subqueries, window functions) to complete complex tasks.
- clean data, optimize SQL queries and enhance analysis performance.
- apply the results from queries to address business problems.
As part of the Management of Relational & Non-Relational Databases section, I learned how:
- to build normalized, consistent, and performant relational data models using SQL Database Definition Language (
DDL) and Database Manipulation Language (DML). - about the tradeoffs between relational databases and their non-relational counterparts,
- distinguish and justify the choice of relational databases vs. non-relational counterpart for different scenarios,
- learn about MongoDB and Redis to get an understanding of the differences in behaviors and requirements for non-relational databases.
- to apply all of this knowledge to build the supporting data structures for a social news aggregator website.
