apache-airflow-providers-postgres

v6.7.0 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package is considered safe based on the low risk scores for network and shell risks, indicating no malicious activities or vulnerabilities in these areas.

  • Low network and shell execution risks
  • No evidence of obfuscation or credential theft
Per-check LLM notes
  • Network: No network calls detected, which is normal for a library focused on local operations like database connections.
  • Shell: No shell executions detected, consistent with a library that does not require or perform system-level commands.
  • Obfuscation: The observed pattern is a common technique for extending package paths and does not indicate malicious obfuscation.
  • Credentials: No suspicious patterns related to credential harvesting have been detected.
  • Metadata: The package shows some red flags such as missing author information and a single package associated with the author's account, but no clear signs of malicious intent 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-pos
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (5547 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
  • 28 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 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-postgres 
Create a small data pipeline application using Apache Airflow and the 'apache-airflow-providers-postgres' package. This application will automate the process of fetching data from a PostgreSQL database, performing some basic transformations on the data, and then storing the transformed data back into another PostgreSQL database. The application should also include a feature to schedule these tasks to run at regular intervals.

Step 1: Set up your environment by installing Apache Airflow and the 'apache-airflow-providers-postgres' package.

Step 2: Define a DAG (Directed Acyclic Graph) in Airflow that includes the following tasks:
- A task to connect to a source PostgreSQL database and fetch data from a specific table.
- A task to transform the fetched data (e.g., adding a new column with computed values).
- A task to store the transformed data into a target PostgreSQL database.

Step 3: Implement error handling and logging within each task to ensure robustness.

Step 4: Schedule the DAG to run daily at a specific time.

Suggested Features:
- Allow configuration of the source and target databases through environment variables or a configuration file.
- Add a task to send email notifications if any of the previous tasks fail.
- Include a task to log the execution details of each DAG run into a separate log table in the target database.

How to Utilize 'apache-airflow-providers-postgres':
- Use the 'PostgresHook' class from the package to interact with PostgreSQL databases. This includes executing SQL queries, fetching data, and inserting data.
- Configure connections to the PostgreSQL databases in Airflow's UI or via configuration files, which can then be accessed by the 'PostgresHook'.

This project will demonstrate how to leverage Apache Airflow and its PostgreSQL provider to create a simple yet effective data processing pipeline.

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