apache-airflow-providers-teradata

v3.6.0 safe
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package is considered safe as it primarily uses standard practices for credential handling and path extension. However, potential misuse of shell commands warrants ongoing monitoring.

  • shell command execution
  • standard credential handling
Per-check LLM notes
  • Network: No network calls detected, which is normal for this type of package.
  • Shell: The use of subprocess.Popen and subprocess.run indicates that the package may execute shell commands, likely for its intended functionality with Teradata tools like BTEQ, Tbuild, and TDLoad. This needs further investigation to ensure it's not being misused.
  • Obfuscation: The obfuscation pattern is common and likely used for extending package paths rather than malicious purposes.
  • Credentials: The credential handling code appears to be reading environment variables for credentials which is a standard practice but could be risky if not properly secured.
  • Metadata: The package shows some minor concerns but does not strongly indicate malicious intent. The missing author details and the use of a non-HTTPS link are notable but not definitive.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 35 test file(s) found

  • Test runner config found: conftest.py
  • 35 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-ter
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (4671 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
  • 75 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 2.0

Found 1 obfuscation pattern(s)

  • under the License. __path__ = __import__("pkgutil").extend_path(__path__, __name__) # Licensed to the Apache S
⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

  • , ) process = subprocess.Popen( bteq_command_list, stdin=subprocess
  • ld_cmd)) sp = subprocess.Popen( tbuild_cmd, stdout=subprocess.PIPE, std
  • ad_cmd)) sp = subprocess.Popen( tdload_cmd, stdout=subprocess.PIPE, std
  • , out_file, ] subprocess.run(cmd, check=True) def decrypt_remote_file_to_string(ssh_cli
  • y delete the file subprocess.run(["shred", "--remove", file_path], check=True, timeout=TPTCon
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • username", "temp") password = os.environ.get("password", "temp") params = { "host": host, "username": user
✓ 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-teradata 
Create a data migration tool using Apache Airflow and the 'apache-airflow-providers-teradata' package. This tool will facilitate the extraction of data from a Teradata database and load it into another database system such as PostgreSQL or MySQL. The application should be designed to run periodically, ensuring that the data in the target database is always up-to-date with the source Teradata database.

### Key Features:
1. **Data Extraction**: Implement a DAG (Directed Acyclic Graph) in Airflow that defines tasks to connect to a specified Teradata database, extract relevant data, and store it temporarily in a local file or a staging area.
2. **Data Transformation**: Include a task in your DAG to transform the extracted data if necessary. This could involve cleaning, filtering, or formatting the data to meet the requirements of the target database schema.
3. **Data Loading**: Design a task to load the transformed data into a target database. Ensure that the tool supports different types of target databases (PostgreSQL, MySQL, etc.).
4. **Scheduling**: Set up Airflow to schedule these tasks at regular intervals (e.g., daily, hourly).
5. **Error Handling and Logging**: Implement robust error handling and logging mechanisms to capture any issues during the data migration process and ensure they are logged for review.
6. **Configuration Management**: Allow users to configure the connection details for both the source and target databases via Airflow's configuration interface or through environment variables.
7. **Security**: Ensure that sensitive information like database credentials is securely managed, possibly using Airflow's secrets backend.
8. **User Interface**: Provide a simple user interface within Airflow's web UI to monitor the status of the data migration tasks.

### Utilization of 'apache-airflow-providers-teradata':
- Use the 'apache-airflow-providers-teradata' package to create operators in your DAG that can connect to the Teradata database, execute SQL queries to extract data, and handle any specific requirements of the Teradata system.
- Explore the package's documentation to understand how to configure connections, handle errors, and optimize performance when working with large datasets.
- Consider implementing custom hooks and operators if the standard ones do not cover all your needs.

### Deliverables:
- A fully functional Airflow DAG that performs data migration from Teradata to another database.
- Documentation on how to set up and run the DAG, including configuration steps and troubleshooting tips.
- A presentation or report detailing the design decisions made and the challenges faced during development.

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