apache-airflow-providers-databricks

v7.15.0 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows low risks across all categories, with only minor concerns about metadata and network interactions that are typical for cloud service integrations.

  • Low risk scores across all categories
  • Common network calls for cloud service integration
Per-check LLM notes
  • Network: The observed network calls are likely related to authentication and token management, which is common for packages interacting with cloud services like Databricks and Azure.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The observed pattern is likely legitimate for extending package paths and does not indicate malicious obfuscation.
  • Credentials: No evidence of credential harvesting patterns detected.
  • Metadata: The package has a non-secure external link and an author with limited information, but no clear signs of malice.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 33 test file(s) found

  • Test runner config found: conftest.py
  • 33 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-dat
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (6029 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
  • 142 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 score 9.0

Found 6 network call pattern(s)

  • t: resp = requests.post( resource, a
  • t: resp = requests.post( token_request_url,
  • t: resp = requests.post( token_exchange_url,
  • response = requests.get( AZURE_METADATA_SERVICE_INSTANCE_URL
  • ryhistory/list response = requests.get( url=f"https://{hook.host}/api/2.0/sql/history/queri
  • elf): self._session = aiohttp.ClientSession() return self async def __aexit__(self, *err):
⚠ 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-databricks 
Your task is to create a mini-application that integrates Apache Airflow with Databricks to automate and manage data pipelines. This application will be built using the 'apache-airflow-providers-databricks' package, which provides necessary hooks, operators, and sensors to interact with Databricks services within Airflow.

The application should perform the following steps:
1. Set up a Databricks workspace connection in Airflow.
2. Define a DAG that triggers a Databricks job to process data from a source (e.g., S3 bucket).
3. Monitor the status of the Databricks job execution through Airflow.
4. Upon successful completion of the Databricks job, trigger another task to move the processed data to a target location (e.g., another S3 bucket).
5. Implement error handling and retries if the Databricks job fails.
6. Schedule the DAG to run at specific intervals (e.g., daily).

Suggested Features:
- Use environment variables for sensitive information like Databricks tokens.
- Implement logging to track the progress and errors of each task.
- Allow configuration of the DAG via Airflow UI parameters.
- Add a sensor to wait until the data is available in the source location before triggering the Databricks job.
- Include a cleanup task to remove temporary files after processing.

The 'apache-airflow-providers-databricks' package is utilized extensively throughout this project to interact with Databricks jobs and clusters. You'll need to use its operators to submit jobs, sensors to monitor job statuses, and hooks to establish connections. Additionally, explore the package documentation to discover other useful functionalities that could enhance your application.