Flavio's Blog

  Flavio Cervantes June 26, 2025   Journal Entry – WK. I This week I learned the history of operating systems and how they have developed over the years. The main 3 OSs are MacOS, Windows, and Linux. We went from batch style processing, to multi-threaded style processing, this enhances the efficiency and usability of computers. All components on a PC are critical for the OS to function, but he main components that directly affect the OS is the CPU, RAM, and Storage. With the CPU, it is able to determine which process has priority as far as resources, it also supports virtualization of CPU, which allows a single physical CPU into smaller CPUs, hence applications are able to share the resources and run simultaneously. I also learned that memory is simply an array of bytes; by sharing resources it allows various applications to have their own segmented share of memory. Regarding OS’s, I learned the core necessities of it; which is the monuments or file systems, memory, ...

Flavio Cervantes June 19, 2025 Briefly summarize the what you consider to be the three (3) most important things you learned in this course.      One of the most valuable knowledge areas that I gained via CST 363-30-2253 is "Database Architecture", I learned the importance of understanding what each database is designed for and the benefits of using it for that purpose in order to use built-in libraries or code to efficiently. For example, if one needs to run analytic scripts again data, then a Data Warehouse design is best for this. MySQL is best designed for processing queries and connection handling. I learned the difference between replicated data and distributed data; this is important because depending on the application one can design the database to have an offsite replication or distribute the data stored in different physical locations.      Another important concept that I learned in this class is "DB Design", I was able to learn the various appr...

Flavio Cervantes CST363-30_2253 Compare MongoDB with MySQL.  MongoDB utilizes flexible JSON style layout, while SQL utilizes strict uniformity with tables of rows and columns, otherwise known as Schemas.  The flexibility of MongoDB is utilized for big data usages such as storing log files, dynamic applications that can involve many changes of data needing to be stored with inserts or updates. Examples of these would be custom mobile applications, log files for different types of apps., or a data science analytic app. On the other hand, SQL is best for apps. such as banking, financial, or warehouse inventory where integrity, standardization databases are needed. With mySQL one achieves the ability to use complex queries and join tables together, it also focuses on extensively structured relationships to ensure reliable queries .  What are some similarities?  The similarities are that the...

 Flavio Cervantes June 09 - 2025 CST 363-2253 In week 06 of CST 363, I learned that API stands for application programming interfaces; this is utilized for POST and GET commands that can communicate a applications such as python with a database called Python psycop2 or Java DB porgraaming which is known as Java JDBC. I also learned that one can utilize SQL queries within their java or python code, for example one can call a getPerformThisAction( select * from DB WHERE  value = 'XYZ'); within their java class.  A very interesting itesm that  i laerned this week was how to make a local database, a java application host a local web site based application. This was very neat to learn about the controller classes, the back/front end HTML files, and how to set the approriate commands to enable them to communicate with each other. For example, in lab 19 we are tasked to create classes that will either create a patient or prescription. We must ensure all requirements are met...

 Flavio Cervantes 06/03/2025        If indexes are supposed to speed up performance of query, what does the author mean by a slow index?     The author means by a slow index is when the incorrectly configured   indexes are used for a certain database, hence it causes inefficiency within the lookup. He informs us slow indexes are mistakenly diagnosed and administrators perform indexes rebuild as the "fix all" solution, but this is the incorrect method of fixing slow indexes.  According to Markus Winand, he explains that an index lookup requires  a.  tree traversal  b.  following the leaf node chain  c . fetch table data - if any of these are configured poorly, then that will be a culprit for performance degradation. (eg: an inefficiently written "where" clause can cause a slow query)        Reference:  https://use-the-index-luke.com/sql/anatomy/slow-indexes