astrocyte-ingestion-kafka

v0.15.0 suspicious
4.0
Medium Risk

Kafka IngestSource adapter for Astrocyte

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network calls, shell execution, and obfuscation. However, it lacks detailed maintainer information and shows signs of low effort, which raises concerns about its authenticity and purpose.

  • Lack of maintainer information
  • Signs of low effort in metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution detected, indicating the package does not execute system commands that could pose a risk.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows some low-effort signs and lacks important maintainer information, raising suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (4.4/10)

✦ High Test Suite 9.0

Test suite present — 1 test file(s) found

  • Test runner config found: pyproject.toml
  • 1 test file(s) detected (e.g. test_kafka_stream_source.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (977 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

  • 3 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with astrocyte-ingestion-kafka 
Create a real-time data streaming application using Python that leverages the 'astrocyte-ingestion-kafka' package to ingest data from a Kafka topic into a database. Your application should be capable of handling high-volume data streams efficiently and provide insights through a simple REST API endpoint.

Step-by-Step Guide:
1. Set up a local Kafka cluster or use a cloud-based service like Confluent Cloud.
2. Define a Kafka topic where your data will be published.
3. Use 'astrocyte-ingestion-kafka' to create a data ingestion pipeline that subscribes to the Kafka topic and processes incoming messages.
4. Integrate a database system (such as PostgreSQL or MongoDB) to store ingested data.
5. Develop a simple REST API using Flask or FastAPI to query and display data stored in the database.
6. Implement basic error handling and logging mechanisms to ensure robustness and maintainability of the application.
7. Add optional features such as data transformation during ingestion (e.g., converting JSON to CSV), data validation before storage, and periodic data archiving.

Features:
- Real-time data ingestion from Kafka topics
- Data storage in a relational or NoSQL database
- REST API endpoints for querying and displaying data
- Error handling and logging
- Optional: data transformation and validation during ingestion
- Optional: periodic data archiving

Utilizing 'astrocyte-ingestion-kafka':
- Configure the package to connect to your Kafka cluster
- Use the provided adapter classes to define how data is extracted from Kafka messages
- Implement a callback mechanism within 'astrocyte-ingestion-kafka' to handle each message as it arrives, ensuring efficient processing and storage of data.

💬 Discussion Feed

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