Generally speaking databases are organized sets of data that are contained in or accessed through a computer. Relational databases, which are digital databases based on the relational model of data, use SQL for querying and maintaining the database. Structured Query Language (SQL) is a domain-specific language used in programming, which was started about 50 years ago in the 1970’s. It is designed for managing data or for stream processing in a relational data steam management system. [2] This is the standard language when it comes to relational database management systems. Database updates or information retrieval use SQL statements. The SQL commands that are used in conjunction with other database extensions are “Select”, “Insert”, “Update”, “Delete”, “Create”, and “Drop”. [1] These basis SQL commands are able to achieve almost all the needs of a database.
There is also something called NoSQL, which is another type of database management system, but instead are non-relational databases.These databases are used for large volumes of data that would cause the response time of a relational database to slow down. NoSQL databases are specialized systems and still have their limitations. They are better suited towards sites that deal with large amounts of data such as Google or Amazon. That being said, it is important to learn about SQL databases because they still have more advantages than NoSQL databases. SQL databases have a secure storage and management model, they allow for control of the view of data, they allow for developers the add to the existing database, and they have better security models for their data storage.[3] In addition to the advantages over NoSQL, it is important for data scientists to learn SQL because it is easy to learn and understand, it integrates well with other coding languages, it has the ability to deal with huge data sets, and it is a highly marketable programming language.[4] Overall, SQL is one of the starting languages used to create relational databases, it has the ability for communication between relational databases, and if you learn SQL you will have an entry to more science data jobs.
Sources:
[1] SQLCourse. “What Is SQL?” SQLCourse, 20 Aug. 2000, www.sqlcourse.com/intro.html.
[2] “SQL.” Wikipedia, Wikimedia Foundation, 4 Feb. 2020, en.wikipedia.org/wiki/SQL.
[3] “What Is Database? What Is SQL?” Guru99, www.guru99.com/introduction-to-database-sql.html.
[4] Onyango, Francis. “5 Reasons Every Aspiring Data Scientist Must Learn SQL.” Medium, Analytics Vidhya, 1 July 2019, medium.com/analytics-vidhya/5-reasons-every-aspiring-data-scientist-must-learn-sql-2bab007a8d76.
Comments:
Dane,
Everything you mentioned in your post is true. Minimal technology is taken into the field, but with the incorporation of even the most simple form of database, the overall construction process could be improved from timing, to cost, to communication, all of which play an important role in a project.
Douha,
It was interesting to learn more in depth what a relational database is, as I researched SQL and they go hand in hand together. I understood that when broken down it is essentially rows and columns working together. The class registration was a good example of this type of database and how we use them more than we are aware of.
Pritesh,
From your post and others my understanding of a object oriented database is one that is similar to a relational database, but is presented in a different form that may be easier to visualize. Because I am unfamiliar with databases in general, i’m not sure what other “real-world entities” could be presented as an object, but I am interested to find out more.
4 comments:
Mika,
I enjoyed hearing about the differences between SQL and NoSQL database management systems, and why SQL might be the preferred system. I think a lot of times, people think that the more complex of the options will be the most suitable, but, as you mentioned, SQLs have more advantages than NoSQL, such as its integration with other coding languages like you mentioned.
You gave a very thorough explanation of the differences and key uses of SQL and NoSQL. I particularly liked how you discussed the advantages of learning SQL to excel in data science jobs. As someone, who has completed a Data Science internship I agree with you. I used SQL-like technology almost everyday throughout the duration of my coop. I was able to use SQL-like syntax when using pyspark to pull data from NoSQL databases as well. Learning pyspark had a very small learning curve once I knew SQL.
Hi Mika,
I genuinely agree with the advantages of SQL over NoSQL, and perhaps this is just because I work with SQL much more frequently so I am more familiar with this language but I see that the benefits for AEC work most often can be derived from a relational model. Especially since using SQL technology, you can extract NoSQL based information out of databases to use. It is a more robust language, and very easy to learn.
Mika,
I have never heard of SQL or NoSQL before, so I appreciate the concise way that you broke the two down. From my understanding, the only benefit to using NoSQL is that it is better at handling large data sizes. Is there ever a time where you would use NoSQL for a basic database, or is it simply more beneficial to stick with SQL because it is easier to learn and apply? It seems like not only is SQL really valuable in data management, but also as a general skill that can be applied to various fields.
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