Efficient data loader

[Anish9901]

Fixes #3423

This PR reduces the time required to load the "Movie Collection" dataset for local as well as remote DBs.

Technical details

Problem: We previously relied on a single SQL dump of Movie Collection schema to load the entire dataset into the DB. This was fine when the DB was present along with the django service but was really slow in the case of a remote DB which ultimately resulted in server timeout with a 502 response.

Solution: This PR solves the aforementioned problem by breaking the large SQL dump into parts and extracting all the data to be loaded in multiple .csv.

• movie_collection_tables.sql: Contains SQL queries for setting up tables for the dataset.
• movies_csv/: Contains all the data required to be loaded into the tables in separate .csvs.
• movie_collection_fks.sql: Contains SQL queries for setting up PKs and FKs on the tables.

We first execute movie_collection_tables.sql then we load all the data in the tables using SQL COPY instead of INSERT and then finally we execute movie_collection_fks.sql to setup PKs and FKs.

Performance (GCP):

DB	Before	After	Improvement
Local	25s	9s	64%
Remote	186s ~(3.1mins)	12s	93.5%

Checklist

• My pull request has a descriptive title (not a vague title like Update index.md).
• My pull request targets the develop branch of the repository
• My commit messages follow best practices.
• My code follows the established code style of the repository.
• I added tests for the changes I made (if applicable).
• I added or updated documentation (if applicable).
• I tried running the project locally and verified that there are no
  visible errors.

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[mathemancer]

Great job; looks good to me. I tried loading everything in this branch and found now problems.