apache-airflow-providers-apache-iceberg

v2.0.2 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risks across all categories, with no clear indicators of malicious activity. It's likely a legitimate package with minor metadata issues.

  • Minor network and metadata risks noted.
  • No shell execution, obfuscation, or credential harvesting detected.
Per-check LLM notes
  • Network: The observed network call pattern is likely related to authenticating or fetching tokens, which could be part of the legitimate functionality if the package interacts with remote services.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The observed pattern is likely a standard import mechanism and not malicious obfuscation.
  • Credentials: No suspicious patterns indicating credential harvesting were found.
  • Metadata: The package has some minor issues with maintainer history and a non-secure link, but no clear signs of malicious intent.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 10 test file(s) found

  • Test runner config found: conftest.py
  • 10 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-apa
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3538 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
  • 8 type-annotated function signatures (partial)
✦ 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 1.5

Found 1 network call pattern(s)

  • } response = requests.post(f"{base_url}/{TOKENS_ENDPOINT}", data=data, timeout=30)
⚠ 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-apache-iceberg 
Develop a data orchestration tool using Apache Airflow that leverages the 'apache-airflow-providers-apache-iceberg' package to manage and optimize data workflows involving Iceberg tables. Your goal is to create a fully functional mini-application that demonstrates the integration of Airflow with Iceberg, showcasing its capabilities in managing complex data pipelines. Here’s a step-by-step guide on what your application should accomplish:

1. **Setup Environment**: Begin by setting up a development environment where you have installed both Apache Airflow and the 'apache-airflow-providers-apache-iceberg' package. Ensure that you have access to an Iceberg-enabled storage system.
2. **Define Data Workflows**: Design a series of data workflows that include tasks such as extracting data from various sources, transforming it according to specific business rules, and loading it into Iceberg tables. Use the 'apache-airflow-providers-apache-iceberg' package to define operators that interact with Iceberg tables directly.
3. **Implement Task Dependencies**: Create task dependencies within Airflow to ensure that data transformation and loading occur in the correct sequence. This could involve tasks like 'ExtractDataFromSource', 'TransformData', and 'LoadIntoIcebergTable'.
4. **Error Handling and Logging**: Implement robust error handling mechanisms and logging within your workflows to monitor the execution of each task and handle any issues that arise during the data pipeline process.
5. **Optimization Techniques**: Utilize the capabilities of the 'apache-airflow-providers-apache-iceberg' package to optimize data workflows. For example, you might implement strategies for incremental data loads or leverage Iceberg's metadata capabilities to improve query performance.
6. **Testing and Validation**: Finally, write tests to validate that your workflows execute correctly and produce the expected results. Test different scenarios to ensure reliability and robustness.

Suggested Features:
- Integration with multiple data sources (e.g., databases, APIs)
- Dynamic data transformation based on user-defined functions
- Support for incremental data loads into Iceberg tables
- Automated cleanup of old data versions in Iceberg tables
- Detailed logging and alerting for workflow failures

The 'apache-airflow-providers-apache-iceberg' package is utilized throughout this project to facilitate seamless interaction with Iceberg tables, allowing for efficient data management and optimization within the Airflow framework.

💬 Discussion Feed

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