Managing Data Relationships in DynamoDB

We have been tasked with creating a DynamoDB table that models data about Target stores in the United States. We will need to examine the data in the context of the access patterns listed below. We will then create a DynamoDB table with the necessary partition and sort keys, as well as secondary indexes to facilitate our access patterns. We will need to modify the provided load.py script to make any needed data transformations or mutations. We will then load the data into our table utilizing load.py. And finally, we will modify queries.py to test our access patterns against our DynamoDB table.

Access Patterns

Given ID, return all attributes for store (ID: 1957).

Return all stores that do not observe daylight savings time; return just state and store ID.

Given a phone area code, return all stores with formatted addresses and phone numbers (Area Code: 206).

Return all attributes for all stores with Starbucks and CVS.

Given state, return all attributes for all stores (State: WA).

Given state and city, return all attributes for all stores (State: WA; City: Seattle).

Given state city and zip, return all attributes for all stores (State: WA; City: Seattle; Zip: 98125).

Expected Returns

Query ID returned 1 items with an average of 48 attributes

Query DaylightSavingsNo returned 54 items with an average of 3 attributes

Query PhoneArea returned 4 items with an average of 5 attributes

Query StarbucksCVS returned 816 items with an average of 47 attributes

Query State returned 37 items with an average of 47 attributes

Query StateCity returned 3 items with an average of 48 attributes

Query StateCityZip returned 1 items with an average of 48 attributes

Resources

• Scripts at lab starting state:
  • load.py
  • queries.py
• Code used in the solution videos for this lab can be found here.
• Data used in this lab:
  • target.csv
  • target_abridged.csv
  • target.json