altertable-lakehouse

v0.3.0 suspicious
4.0
Medium Risk

Official Python SDK for Altertable Lakehouse API

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package appears to be a legitimate SDK for interacting with an external service, but the maintainer's limited history and the network calls raise some concerns.

  • The maintainer has only one package, suggesting they may be new or less active.
  • The package makes network calls, which, while potentially legitimate, increases the risk of supply-chain attacks.
Per-check LLM notes
  • Network: The network call pattern suggests the package is making HTTP requests, which could be legitimate if it's designed to interact with an external service.
  • Shell: No shell execution patterns were detected, indicating there is no immediate risk from executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, which might indicate a new or less active account, raising some suspicion but not enough to conclusively determine malice.

📦 Package Quality Overall: Low (2.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (2205 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 15 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • fix}" self._client = httpx.Client( base_url=self.base_url, timeout=sel
✓ Code Obfuscation

No obfuscation patterns detected

✓ 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: altertable.ai

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Altertable" 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 altertable-lakehouse 
Develop a data management mini-application called 'DataLakeSync' using the 'altertable-lakehouse' Python package. This application will serve as a bridge between local data storage systems and the cloud-based AlterTable Lakehouse, enabling users to efficiently manage their data by syncing it from various sources to the lakehouse and vice versa. The primary goal of DataLakeSync is to simplify the process of data migration, transformation, and analysis, making it accessible to both technical and non-technical users.

### Features:
1. **Data Synchronization**: Users should be able to specify local directories or files containing CSV, JSON, or Parquet datasets. The application will then sync these datasets with the AlterTable Lakehouse.
2. **Schema Management**: Allow users to define schemas for their datasets before syncing them to ensure data integrity and structure consistency in the lakehouse.
3. **Transformation Rules**: Implement a feature where users can apply simple transformations (e.g., renaming columns, adding calculated fields) on the data before it is synced to the lakehouse.
4. **Version Control**: Keep track of different versions of datasets within the lakehouse to allow users to revert to previous states if necessary.
5. **Security & Access Control**: Integrate basic security measures like user authentication and authorization to control who has access to which datasets within the lakehouse.
6. **User Interface**: Develop a simple web interface using Flask or Django to make the application more user-friendly, allowing users to interact with their data without needing to run commands in a terminal.
7. **Monitoring & Alerts**: Implement monitoring to track the status of sync operations and send alerts via email or SMS in case of errors or failures.

### Utilizing 'altertable-lakehouse':
- Use the 'altertable-lakehouse' package to connect to the lakehouse and perform CRUD operations on datasets.
- Leverage its capabilities for schema definition, data ingestion, and querying to implement the synchronization logic.
- Explore any additional features provided by the package that could enhance the functionality of your application, such as support for advanced data types or integration with other services.

💬 Discussion Feed

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