Mike Divine's Learning Journal

     The three most important thinga about this course are the things we learned about Database planning and designing.  You need to understand what data you will be storing and how often it will be accessed, what type of queries will be used, etc.        Data integrity is key when you are having users access, update, and insert data on a regular basis.  Data validation and design are both important to keeping data integrity to make sure invalid data isn't being inserted.  Designing a system to make it as difficult as possible to make mistakes is important.      Scalability and performance are also very important because many databases handle large amounts of data and the more time it takes to find the data you need, the more money the company is losing.  Being able to grow as the need arises will set yourself up for the future rather than pushing the buck to the future.      Accessibility and usab...

      A data warehouse is a database that holds data to support business intelligence.  It usually holds data from operational databases, transactional databases and can even have outside sources that could give the business more insights into their industry.  The companies that use them can have deeper insights with seeing patterns and industry trends over time.      Having a copy of the data in a data warehouse is important so that the data can be structured to query data faster and so the load on the operational or transactional databases don't get overloaded with the analysis and report intensive queries.

Comparing MongoDB with MySQL, the first thing that strikes me is that MongoDB is not a relational database.  There are no keys that are strictly tied to other keys.  MongoDB stores data in documents using JSON.  MySQL uses rows and columns. MongoDB is easier to scale and can handle larger amounts of data more easily.  Using Mongo, you can store data with more flexibility allowing for more complex data storage.  Using MySQL, you would have to have tables and relations set up before data can be stored. MongoDB uses MQL, a query language based on JavaScript, while MySQL uses SQL which was developed to communicate with relational databases. I would choose Mongo if I needed to store large amounts of unstructured data.  Also if I needed flexibilty in changing data and high performance searching through mass amounts of data. I would choose MySQL if I needed a relational database with a well defined schema.  Also if I needed to keep data consistency and needed...

      It seems as though a slow index could be from multiple reasons.  Sometimes a query must actually search through all the rows in a table even using an index because the query is looking for too wide of a search.  Reading Slow Indexes in RDBMS at  https://use-the-index-luke.com/sql/anatomy/slow-indexes , it talks about the leaf node chain and how even though the database traverses the tree, it also has the follow the leaf node chain completely to make sure there are no missed values.  The article also talks about how fetching the table data can also take a lot of time if the data is spread out around different areas of the hard drive.  I would think that SSD would have much faster search and read speeds than HDD, so this may be a step forward in speed with using an SSD.