apache-airflow-providers-oracle

v4.6.0 safe
3.0
Low Risk

Provider package apache-airflow-providers-oracle for Apache Airflow

🤖 AI Analysis

Final verdict: SAFE

The package is deemed safe based on the low scores across all risk categories, with only minor issues noted in metadata.

  • Low network and shell risk
  • Minor obfuscation and metadata concerns
  • No credential risk detected
Per-check LLM notes
  • Network: No network calls detected, which is normal for a library focused on Oracle integration without real-time database operations.
  • Shell: No shell execution patterns detected, aligning with expectations for a standard library package.
  • Obfuscation: The observed pattern is a common method for extending module search paths and is not indicative of malicious obfuscation.
  • Credentials: No suspicious patterns related to credential harvesting were detected.
  • Metadata: The package has some minor issues but no clear signs of malicious intent or typosquatting.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 16 test file(s) found

  • Test runner config found: conftest.py
  • 16 test file(s) detected (e.g. conftest.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://airflow.apache.org/docs/apache-airflow-providers-ora
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (4970 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 18 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 46 unique contributor(s) across 100 commits in apache/airflow
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # Licensed to the Apache S
  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # # Licensed to the Apache
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: airflow.apache.org>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
✓ Git Repository History

Repository apache/airflow appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with apache-airflow-providers-oracle 
Create a data migration tool using Apache Airflow and the 'apache-airflow-providers-oracle' package. This tool will automate the process of migrating data from an Oracle database to another database system (e.g., PostgreSQL). Your task is to design and implement a workflow that extracts data from multiple tables in an Oracle database, transforms it as needed, and then loads it into a PostgreSQL database. Here’s a step-by-step guide on how to approach this project:

1. **Set Up Environment**: First, ensure you have Python installed along with Apache Airflow and the 'apache-airflow-providers-oracle' package. Additionally, install any other necessary packages such as psycopg2 for PostgreSQL interaction.
2. **Define DAGs**: Use Apache Airflow to define Directed Acyclic Graphs (DAGs) that represent the workflow of your data migration process. Each DAG should include tasks for connecting to the Oracle database, extracting data, transforming it if required, and loading it into the PostgreSQL database.
3. **Oracle Connection Task**: Utilize the 'apache-airflow-providers-oracle' package to establish a connection with the Oracle database. Ensure you handle credentials securely and efficiently.
4. **Data Extraction**: Write tasks within your DAG that extract data from specific tables in the Oracle database. Consider implementing error handling and logging to track the extraction process.
5. **Transformation Logic**: Depending on the requirements, include tasks that transform the extracted data before loading it into the PostgreSQL database. For instance, this could involve cleaning data, converting data types, or performing calculations.
6. **PostgreSQL Connection Task**: After transformation, establish a connection to the PostgreSQL database using psycopg2 or another suitable library.
7. **Data Loading**: Implement tasks to load the transformed data into the PostgreSQL database. Make sure to manage transactions properly to ensure data integrity.
8. **Scheduling and Monitoring**: Configure your DAGs to run at scheduled intervals. Use Airflow’s web interface to monitor the status of your tasks and debug any issues that arise.
9. **Documentation and Testing**: Document your workflow and test each part of your DAG thoroughly to ensure reliability and accuracy.

Optional Features:
- Implement retry logic for failed tasks.
- Add support for incremental data migration rather than full migrations.
- Integrate logging and alerting mechanisms to notify stakeholders of any issues.
- Include a feature to compare data between the Oracle and PostgreSQL databases post-migration for validation.

This project will not only demonstrate your proficiency with Apache Airflow and the 'apache-airflow-providers-oracle' package but also showcase your ability to design and implement robust data migration workflows.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!