arcane-bigquery-storage

v1.3.1 safe
3.0
Low Risk

A package to use the BigQuery Storage Write API

🤖 AI Analysis

Final verdict: SAFE

The package shows low risk across all categories with no network calls, shell executions, obfuscations, or credential harvesting. The only notable concern is the maintainer's limited history with the platform.

  • Low risk in network, shell, obfuscation, and credential handling.
  • Maintainer has only one package, potentially indicating a new or less active account.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communication.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • 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 conclusive evidence of malintent.

📦 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 (1912 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

  • 5 type-annotated function signatures (partial)
○ 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

No suspicious network call patterns found

✓ 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: wearcane.com

✓ 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 "Arcane" 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 arcane-bigquery-storage 
Create a real-time data ingestion tool using Python that leverages the 'arcane-bigquery-storage' package to stream data into Google BigQuery. This tool will serve as a bridge between various data sources and BigQuery, allowing users to ingest data seamlessly and efficiently. Here’s a step-by-step guide on how to build this tool:

1. **Setup**: Begin by setting up your development environment with Python and installing necessary packages including 'arcane-bigquery-storage', 'google-cloud-bigquery', and 'pandas'. Ensure you have access to Google Cloud Platform and BigQuery.
2. **Data Source Integration**: Design the tool to support multiple data sources such as CSV files, real-time streaming from IoT devices, or even web scraping from public APIs. Each source should be configured via a simple UI where users can specify connection details and data formats.
3. **Data Transformation**: Implement functionality within the tool to transform incoming data into a format suitable for BigQuery. This includes handling data types, cleaning data, and structuring it according to BigQuery schema requirements.
4. **Real-Time Streaming**: Utilize 'arcane-bigquery-storage' to stream transformed data into BigQuery in real-time. Focus on optimizing performance and ensuring minimal latency between data collection and storage.
5. **Error Handling & Logging**: Develop robust error handling mechanisms to manage issues such as network failures or incorrect data formats. Logs should be maintained for troubleshooting and auditing purposes.
6. **Monitoring & Alerts**: Integrate monitoring tools to track the health of the data ingestion process. Set up alerts for critical issues like high latency or failed data uploads.
7. **Security**: Ensure all connections to data sources and BigQuery are secure, utilizing OAuth2 authentication methods provided by Google Cloud SDK.
8. **User Interface**: Create a user-friendly interface for managing the tool. Features should include starting/stopping data ingestion processes, viewing logs, and configuring data sources.
9. **Documentation**: Provide comprehensive documentation on how to install, configure, and operate the tool, along with examples and best practices.

The 'arcane-bigquery-storage' package is crucial for this project as it enables efficient and scalable data streaming into BigQuery. By leveraging its capabilities, we aim to create a versatile tool that simplifies the process of ingesting large volumes of data into BigQuery, making it easier for businesses and developers to harness the power of big data analytics.

💬 Discussion Feed

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