apache-airflow-providers-apache-impala

v1.9.2 safe
3.0
Low Risk

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

🤖 AI Analysis

Final verdict: SAFE

The package is considered safe as it does not exhibit any significant risks such as network or shell execution vulnerabilities. While there are minor irregularities noted, they do not strongly indicate malicious intent.

  • No network calls detected
  • No shell execution patterns
  • Some obfuscation but likely benign
Per-check LLM notes
  • Network: No network calls detected, which is normal for a library focused on providing connectivity to Apache Impala.
  • Shell: No shell execution patterns detected, consistent with the expected behavior of a library.
  • Obfuscation: The observed pattern is likely a standard practice for extending package paths and not indicative of malicious obfuscation.
  • Credentials: No patterns indicative of credential harvesting were found.
  • Metadata: The package shows some irregularities but lacks clear indicators of being malicious.

📦 Package Quality Overall: Medium (7.4/10)

✦ High Test Suite 9.0

Test suite present — 11 test file(s) found

  • Test runner config found: conftest.py
  • 11 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 (4000 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-impala 
Create a data processing pipeline using Apache Airflow that leverages the 'apache-airflow-providers-apache-impala' package to interact with Impala, a popular SQL engine for Hadoop. Your goal is to develop a fully functional mini-application that demonstrates the integration of Airflow with Impala for executing complex data queries and handling large datasets efficiently. Here’s a detailed breakdown of what your application should achieve:

1. **Setup Environment**: Begin by setting up a local environment where you have Apache Airflow installed along with the 'apache-airflow-providers-apache-impala' package. Ensure that Impala is also available for testing purposes.

2. **Define Data Sources**: Define one or more data sources that can be queried using Impala. These could be existing datasets stored in HDFS or other storage systems supported by Impala.

3. **Design DAGs**: Design Directed Acyclic Graphs (DAGs) within Airflow to represent your data processing workflows. Each DAG should include tasks that utilize the 'apache-airflow-providers-apache-impala' package to execute specific SQL queries on your defined data sources.

4. **Query Execution**: Implement tasks within these DAGs that use the Impala provider hooks to connect to Impala and execute predefined SQL queries. These queries should perform operations such as filtering, aggregating, and joining data from different sources.

5. **Data Transformation and Analysis**: Include tasks that transform or analyze the retrieved data. For example, calculate statistics like averages or sums, or create new derived datasets.

6. **Output Results**: Finally, design tasks that output the results of your queries and analyses to a destination of your choice, such as writing them back to HDFS, saving them as CSV files, or sending them to a database.

7. **Scheduling and Monitoring**: Set up scheduling for your DAGs so they run at regular intervals or based on specific triggers. Additionally, implement monitoring capabilities to track the status and performance of your pipelines.

**Features to Consider**:
- Support for dynamic parameterization of queries to enable flexibility in data retrieval and analysis.
- Integration with logging services to capture and review query execution logs.
- Implementation of retries and error handling mechanisms for robustness.
- User-friendly UI elements to visualize the workflow and monitor progress.

Your final submission should include the complete codebase for your application, documentation detailing setup instructions and usage, and a brief demonstration of how the 'apache-airflow-providers-apache-impala' package is utilized within the context of your data processing pipeline.

💬 Discussion Feed

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