apache-airflow-providers-ydb

v2.5.2 safe
4.0
Medium Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package has minimal risks associated with network and shell operations. The obfuscation and metadata risks are low, and there's no evidence of credential harvesting or malicious intent.

  • Low network and shell execution risks
  • No signs of malicious activities
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external communication for its functionality.
  • Shell: No shell executions detected, indicating no direct command-line interface manipulations.
  • Obfuscation: The observed pattern is likely a standard method for extending module search paths and not indicative of malicious activity.
  • Credentials: No suspicious patterns related to credential harvesting were found.
  • Metadata: The package shows some red flags such as a missing author name and a non-HTTPS external link, but there are no clear signs of malicious intent or typosquatting.

📦 Package Quality Overall: Medium (7.8/10)

✦ High Test Suite 9.0

Test suite present — 18 test file(s) found

  • Test runner config found: conftest.py
  • 18 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-ydb
  • 1 documentation file(s) (e.g. conf.py)
  • Detailed PyPI description (3693 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-ydb 
Develop a small data pipeline management application using Apache Airflow and the 'apache-airflow-providers-ydb' package. This application will serve as a bridge between your local development environment and YDB (Yandex DataSphere), allowing you to automate the process of ingesting, processing, and exporting data from various sources to YDB.

### Objective:
Create a fully-functional mini-application that leverages Apache Airflow to manage workflows involving YDB operations. Your application should include at least two distinct DAGs (Directed Acyclic Graphs):
1. A DAG for ingesting data from a CSV file stored in S3 into YDB.
2. Another DAG for performing simple data transformations on the imported data within YDB and exporting it back to S3.

### Features:
- **CSV Ingestion**: Implement a task to download a CSV file from an S3 bucket and load its contents into YDB.
- **Data Transformation**: Use YDB SQL queries to perform basic data manipulations such as filtering or aggregating the data.
- **Export to S3**: Write a task to export transformed data back to an S3 bucket.
- **Error Handling**: Ensure that your application gracefully handles errors, logging them appropriately and retrying failed tasks if necessary.
- **Scheduling**: Set up scheduling so that the ingestion and transformation processes run daily.

### How 'apache-airflow-providers-ydb' is Utilized:
- **Operator Usage**: Utilize operators provided by the 'apache-airflow-providers-ydb' package to interact with YDB directly from your DAGs. For example, use the `YdbToS3Operator` for exporting data and `S3ToYdbOperator` for importing data.
- **Connection Configuration**: Configure Airflow connections for YDB and S3 to establish secure communication channels.
- **Task Dependencies**: Define dependencies between tasks to ensure that data is processed in the correct order.

### Deliverables:
- Source code for the application, including all DAG definitions.
- Documentation detailing setup instructions and configuration requirements.
- A brief demonstration showing the application in action, including screenshots or videos of the Airflow UI and S3/YDB interactions.

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