apache-airflow-providers-apache-cassandra

v3.9.4 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package is deemed safe with low risk scores across all categories except metadata, where some concerns exist but do not strongly indicate malicious activity.

  • Low network and shell risk
  • Minimal obfuscation risk
  • No credential risk detected
  • Metadata has minor issues but lacks clear malicious indicators
Per-check LLM notes
  • Network: No network calls detected, which is normal for this type of package.
  • Shell: No shell execution patterns detected, aligning with the expected behavior for this package.
  • Obfuscation: The observed pattern is likely for path manipulation and not malicious obfuscation.
  • Credentials: No credential harvesting patterns detected.
  • Metadata: The package shows some red flags such as missing author information and a single package associated with the maintainer's account, but there are no clear signs of typosquatting or malicious intent.

📦 Package Quality Overall: Medium (7.4/10)

✦ 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-apa
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3951 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
✦ 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-apache-cassandra 
Your task is to develop a data orchestration tool using Apache Airflow that integrates with Apache Cassandra to manage data workflows involving database operations. This tool will be particularly useful for teams dealing with large datasets where data needs to be regularly ingested, transformed, and analyzed. Here’s a detailed plan for your project:

1. **Project Overview**: Create a mini-application that automates the process of data ingestion from various sources into Apache Cassandra and subsequent data processing tasks such as transformation and analysis.
2. **Core Features**:
   - **Data Ingestion**: Design DAGs (Directed Acyclic Graphs) that periodically fetch data from different sources like CSV files, APIs, or other databases and insert it into a Cassandra cluster.
   - **Data Transformation**: Implement tasks within the DAGs that perform basic transformations on the data before storing it in Cassandra. For example, cleaning up null values, formatting dates, etc.
   - **Data Analysis**: Use Apache Airflow to schedule periodic tasks that run analytical queries on the data stored in Cassandra and generate reports or visualizations.
3. **Utilizing 'apache-airflow-providers-apache-cassandra' Package**:
   - **Connecting to Cassandra**: Utilize the package to establish a connection between Airflow and your Cassandra cluster. Ensure you handle connection parameters securely.
   - **Executing Queries**: Leverage the package’s capabilities to execute CQL (Cassandra Query Language) statements directly from Airflow tasks for both reading and writing data.
4. **Development Steps**:
   - Set up a local development environment with Apache Airflow and Apache Cassandra.
   - Install the 'apache-airflow-providers-apache-cassandra' package.
   - Write Python scripts that define your DAGs and tasks, including data ingestion, transformation, and analysis processes.
   - Test each component of your workflow to ensure they work seamlessly together.
5. **Advanced Features (Optional)**:
   - Implement error handling and retries for failed tasks.
   - Integrate logging to monitor the execution of your DAGs.
   - Provide a user-friendly interface for managing and scheduling your DAGs through Airflow’s web UI.
6. **Deliverables**:
   - A fully functional data orchestration tool that integrates Apache Airflow with Apache Cassandra.
   - Documentation detailing the setup process, configuration options, and usage instructions.
   - Sample DAGs demonstrating each core feature of your application.

💬 Discussion Feed

Leave a comment

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