apache-airflow-providers-tableau

v5.4.1 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all categories, with only minor issues noted in metadata. There are no indications of malicious activities or supply-chain attacks.

  • No network or shell execution risks detected.
  • Minor metadata issues but no signs of malicious intent.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package is expected to communicate with external services like Tableau.
  • Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
  • Obfuscation: The observed pattern is likely a standard method for extending module search paths and not indicative of malicious obfuscation.
  • Credentials: No suspicious patterns indicating credential harvesting were found.
  • Metadata: The package has some minor issues but no clear signs of malice or typosquatting.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 12 test file(s) found

  • Test runner config found: conftest.py
  • 12 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-tab
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3493 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
  • 11 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

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-tableau 
Your task is to develop a mini-application using Apache Airflow along with the 'apache-airflow-providers-tableau' package. This application will automate the process of extracting data from Tableau Server, performing basic ETL (Extract, Transform, Load) operations, and then loading the transformed data into a PostgreSQL database. The application should be designed to run periodically as a scheduled task.

### Core Features:
1. **Data Extraction**: Implement an Airflow DAG (Directed Acyclic Graph) that extracts metadata and data from a specified Tableau Server site. Use the 'apache-airflow-providers-tableau' package to authenticate and retrieve data via its API.
2. **ETL Operations**: Once extracted, perform simple ETL operations such as filtering out unnecessary fields, renaming columns, and converting date formats.
3. **Data Loading**: After processing, load the transformed data into a PostgreSQL database. Ensure that the schema is appropriately created if it does not exist, and data is inserted or updated accordingly.
4. **Scheduling**: Configure the DAG to run on a daily basis, but also allow for manual triggering through the Airflow UI.
5. **Logging and Monitoring**: Integrate logging within your DAG tasks to monitor execution and troubleshoot any issues that arise during the ETL process.

### Additional Features (Optional):
- **Error Handling**: Implement error handling mechanisms to manage exceptions during data extraction and loading processes.
- **Parameterization**: Allow users to specify different Tableau Server sites, PostgreSQL databases, and ETL configurations via Airflow variables or environment variables.
- **Notifications**: Set up notifications to alert stakeholders when the ETL job completes successfully or encounters errors.

### Utilizing 'apache-airflow-providers-tableau':
This package provides hooks and operators to interact with Tableau Server's APIs directly from Airflow. You'll use these to authenticate with Tableau, extract metadata about workbooks and datasets, and download actual data from Tableau Server. The package simplifies the interaction with Tableau's REST API, making it easier to integrate Tableau data sources into your Airflow workflows.

### Deliverables:
1. A fully functional Airflow DAG that performs the above tasks.
2. Documentation on how to set up the environment, including required dependencies and configuration steps.
3. Instructions on deploying and running the DAG in a local Airflow instance.

💬 Discussion Feed

Leave a comment

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