It is a single accompanying Python script illustrating how you might validate your CSV data by loading it into an in-memory SQLite database and performing the various checks (File Integrity, Schema-Level, Key Constraints, Logic Validations, Data Quality, etc.). The script is somewhat verbose for clarity. In practice, you'd tailor it to exactly match your real CSV paths, column names, constraints, and business rules.

You can run the script from the command line, providing paths to your CSV files. It handles:

1. Parsing CSV (verifies row length).
2. Creating SQLite tables with constraints.
3. Loading CSV data into those tables.
4. Running validation queries (duplicate primary keys, foreign key mismatches, domain checks, etc.).
5. Reporting results and exiting with success/failure.

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How The Script Works

1. Command-Line Arguments

   • --spatial, --linguistic, --temporal, --sources specify paths to each CSV.

2. CSV Format Check

   • check_csv_format(...) ensures each row has the expected number of columns. If there’s a mismatch, the script raises errors and stops.

3. In-Memory SQLite

   • We open sqlite3.connect(":memory:") so everything runs in RAM. When the script ends, the database disappears (ephemeral).

4. Table Creation

   • create_tables(conn) sets up the schema. You’ll see placeholders for constraints like foreign keys. SQLite requires PRAGMA foreign_keys = ON; if you want foreign key enforcement. You can add that right after connecting, e.g.:

     conn.execute("PRAGMA foreign_keys = ON;")
     

   • Note that some constraints (e.g., composite foreign keys or advanced checks) may require custom triggers in SQLite.

5. Loading CSVs

   • load_csv_into_table(...) uses a generic approach to read each CSV and insert rows. By default, it skips the header row (with next(reader, None)). Adjust if your CSVs don’t have headers.

6. Validation Tests

   • run_tests(conn) executes various queries that reflect your earlier mention of needed checks:
   • Schema-Level & Key Constraints
     • Duplicate primary key checks in Spatial, Linguistic, etc.
     • Foreign key checks (e.g., Linguistic.SPATID must exist in Spatial).
   • Logic-Based Validations
     • Temporal data must obey rules about EVENT, OBJID, OBJNAME, START < END, etc.
   • Data Quality
     • Checking coordinate ranges in Spatial.
     • Allowed language codes.
     • Allowed EVENT values.

7. Error Handling

   • The script accumulates all errors, then prints them at the end and exits with status code 1 if there are any. A zero exit code means success.

8. Execution

   • Example usage:

     python validate_csvs.py \
     --spatial /path/to/Spatial.csv \
     --linguistic /path/to/Linguistic.csv \
     --temporal /path/to/Temporal.csv \
     --sources /path/to/Sources.csv
     

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Customizing the Script

• Columns & Orders: Make sure the lists in load_csv_into_table precisely match your CSV layout and skip-headers logic.
• Constraints: If you need advanced constraints (e.g., composite foreign keys, or advanced check constraints), you can add them when creating tables. SQLite is a bit limited on multi-column foreign keys but can do them with the right syntax or triggers.
• Extra Validation: Add or remove queries in run_tests for your domain-specific logic.
• Performance: If your CSVs are large, you may want to optimize with executemany() or PRAGMA tweaks. For moderate data sizes, the above approach is fine.