atoti-server-kafka

v0.9.15 safe
3.0
Low Risk

Resources to interact with Kafka streams

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, obfuscations, or credential harvesting attempts. The metadata risk slightly increases due to the maintainer's single package, but overall, there are no clear signs of a supply-chain attack.

  • Low network and shell risks
  • No obfuscation or credential harvesting attempts
  • Single package from maintainer
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 immediate signs of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no risk of secret or credential theft.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other suspicious elements were found.

📦 Package Quality Overall: Low (3.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ 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

  • Classifier: Typing :: Typed
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in atoti/atoti
  • Two distinct contributors found

🔬 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: activeviam.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository atoti/atoti appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "ActiveViam" 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 atoti-server-kafka 
Create a real-time data analytics dashboard that leverages the 'atoti-server-kafka' package to process and visualize data from Kafka streams. This project aims to demonstrate how to set up a Kafka consumer, process incoming data, and display it on a dynamic dashboard. Here’s a detailed plan for building this application:

1. **Set Up Your Environment**: Ensure you have Python installed along with the necessary packages such as 'atoti-server-kafka', 'pandas', 'plotly', and 'kafka-python'. Set up a virtual environment to manage your dependencies.
2. **Kafka Setup**: Install Apache Kafka locally or use a cloud-based service like Confluent Cloud. Configure a Kafka topic where your application will listen for messages.
3. **Consumer Implementation**: Use 'atoti-server-kafka' to create a Kafka consumer that subscribes to the specified topic. The consumer should continuously fetch new messages from the stream.
4. **Data Processing**: Implement a function within your application that processes the raw data received from Kafka. This could involve filtering, aggregating, or transforming the data into a format suitable for analysis.
5. **Integration with Plotly**: Utilize Plotly to create interactive visualizations of the processed data. These visualizations should update in real-time as new data arrives from the Kafka stream.
6. **Dashboard Development**: Develop a simple web interface using Flask or another lightweight framework to serve your Plotly charts. This dashboard should allow users to interact with the data through various filters and controls.
7. **Testing & Deployment**: Test your application thoroughly under different scenarios to ensure reliability. Consider deploying your application to a platform like Heroku or AWS to make it accessible online.

Some suggested features include:
- Real-time data updates every second.
- User-defined filters for narrowing down the data displayed on the dashboard.
- Historical data storage for trend analysis.
- Error handling mechanisms for robustness.

By following these steps, you'll create a powerful tool for real-time data monitoring and analysis, showcasing the capabilities of 'atoti-server-kafka' in handling streaming data.

💬 Discussion Feed

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