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Scenario
An SQLite database contains e-commerce data that needs to be exported to Parquet format for analytics processing. The export must preserve table relationships and provide metadata about the conversion.
Task
Write a Python script at /home/interview/export_to_parquet.py that connects to /home/interview/ecommerce.db, extracts all tables, converts each to Parquet format with Snappy compression, and creates a manifest file. Save outputs to /home/interview/parquet_export/ directory.
Note: pandas, pyarrow, and sqlite3 are already installed.
Database Schema
| Table | Columns | Foreign Keys |
|---|---|---|
| customers | id, name, email, created_at | - |
| products | id, name, price, category, stock | - |
| orders | id, customer_id, product_id, order_date, total_amount, status | customer_id → customers.id product_id → products.id |
Manifest Requirements
Create manifest.json containing: export timestamp, compression type, and for each table: table name, file name, row count, file size, and foreign key relationships.
Example Manifest
{
"export_timestamp": "2026-02-13T10:30:00",
"compression": "snappy",
"tables": [
{
"table_name": "orders",
"file_name": "orders.parquet",
"row_count": 200,
"file_size_bytes": 23456,
"foreign_keys": [
{"column": "customer_id", "references_table": "customers", "references_column": "id"}
]
}
]
}
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