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Data Structures Week 4

Relational Database for our AA Data

This week, I continue to work with the data I scraped, parsed, and augmented in the previous three weeks to write this AA data to a relational database.


Planning my Relational Database

  1. I will plan on storing my AA data in normalized relations in order to more readily understand the unique locations that house AA meetings. This approach will avoid potential headaches that may arise from attempting to map overlapping location beacons with identical Lat-Lon coordinates, and will allow me the most flexibility. For example, a star schema will allow for the possibility that a meeting can occur at multiple locations and that one address can house multiple locations. Each unique meeting instance (which I'll call a schedule) will be attributed to a known meeting, location, and geoLocation.
  2. I will structure my data in a star schema. With this approach, each individual table will contain the least amount of data needed to convey critical information, optimizing storage efficiency and providing ease of understanding/visualizing data at higher levels of hierarchies without needing to duplicate this data.
  3. My general hierarchy will follow this outline, with each following item in the list constituting a more granular piece of data about a specific meeting that inherits characteristics from all proceeding data points:
  1. Geo Locations (Addresses and Lat-Lons)
  2. Locations (Building Names and/or Address Details)
  3. Meetings (Meeting Names and Meeting Details)
  4. Meeting Schedules (Day/Times, Meeting Types, Special Interest)