Welcome to the Schema Change Detector Utility, a Python package designed for data engineers who navigate the dynamic and evolving landscape of database schemas. In the data engineering world, schema changes can significantly impact data integration, reporting, and analysis processes. This utility provides a seamless way to detect schema changes, enabling data professionals to make informed decisions and adapt their workflows accordingly.

• Schema Storage: Read and store the schema of any table in a SQLite database for future reference.
• Change Detection: Compare the current table schema against the stored version to detect any changes.
• Integration Friendly: Designed to be easily integrated into data load activities, ensuring you're always working with the most up-to-date schema information.

Before you can use the Schema Change Detector Utility, you need to install it. The package is distributed as a wheel file, making it easy to install using pip.

pip install ddl_change_detect-0.1.0-py3-none-any.whl

To get started with the Schema Change Detector Utility, follow these steps:

1. Initialize the Database Before using the utility, initialize the database to store the schema information.

python -m ddl_change_detect.db_init 2. Read and Store Schema Use the store_schema function to read a table's schema and store it in the database.

from ddl_change_detect import store_schema

create_statement = conn.execute("""SHOW CREATE TABLE {table_name}""") table_name = 'table_name' sqlite_db_path = 'db/schema_store.db'

store_schema(create_statement, table_name, sqlite_db_path)

3. Detect Schema Changes To detect changes in the schema, use the compare_schema function.

from ddl_change_detect import compare_schema connection_string='path to schema_store.db in python package/or anywhere' schema_diff = compare_schema(connection_string, table_name, sqlite_db_path)

if schema_diff: print("Schema differences detected:") print(schema_diff) else: print("No schema differences detected.")

In scenarios where schema changes are frequent, it's crucial to perform data profiling only on tables that have changed. This utility can be integrated into your data pipeline to automatically trigger profiling jobs for tables with detected schema changes.

Example:

if schema_diff: # Logic to trigger data profiling for table_name print(f"Triggering data profiling for {table_name} due to schema changes.")

When loading data into a data warehouse or data lake, schema changes can affect the loading process. Use this utility to detect schema changes and adjust your loading strategy accordingly.

if schema_diff: # Adjust data loading process for table_name print(f"Adjusting data loading process for {table_name} due to schema changes.")

The Schema Change Detector Utility empowers data engineers to manage and respond to schema changes proactively. By integrating this utility into your data pipelines, you can ensure that your data processing and analysis workflows remain efficient and accurate, even as your data sources evolve.