atoti-server-directquery-databricks

v0.9.15 suspicious
4.0
Medium Risk

Resources to use DirectQuery on Databricks

⚠ Tarball exceeded 25 MB — source code analysis was limited to package metadata only.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows minimal risk in terms of network, shell, and obfuscation activities, but the metadata risk score is elevated due to the maintainer having only one package, which warrants further investigation.

  • Maintainer has only one package
  • No description provided for the package
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package is expected to perform external communications.
  • Shell: No shell executions detected, indicating no immediate risk from command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, indicating secure handling of sensitive information.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account, raising some suspicion but not conclusive evidence of malice.

📦 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-directquery-databricks 
Create a data analysis tool that leverages the 'atoti-server-directquery-databricks' package to perform real-time analytics on large datasets stored in Azure Databricks. This tool will allow users to interactively query and visualize data without needing to load it entirely into memory, thus optimizing performance and resource usage.

Step 1: Set up the environment
- Install the required packages including 'atoti-server-directquery-databricks', pandas, and any visualization library like matplotlib or seaborn.
- Configure your Databricks cluster to ensure it has the necessary libraries installed and is accessible via the 'atoti-server-directquery-databricks' package.

Step 2: Data Ingestion
- Design a function to connect to the Databricks SQL endpoint using 'atoti-server-directquery-databricks'.
- Implement a method to execute SQL queries directly on the Databricks cluster and fetch results.

Step 3: Interactive Querying
- Develop a user-friendly interface where users can input SQL queries.
- Utilize 'atoti-server-directquery-databricks' to run these queries against the Databricks dataset.
- Display the results back to the user in a tabular format.

Step 4: Visualization
- Integrate a feature that allows users to select columns from the queried result set to generate plots.
- Use matplotlib or seaborn to create visual representations of the data such as bar charts, line graphs, etc.

Step 5: Advanced Features
- Add support for saving queries and their results for future reference.
- Implement a feature to compare different sets of queries side by side visually.
- Include documentation and examples to help other developers integrate 'atoti-server-directquery-databricks' into their own projects.

By following these steps, you'll have built a powerful yet easy-to-use tool that demonstrates the capabilities of 'atoti-server-directquery-databricks' for performing real-time data analysis on large datasets hosted in Databricks.

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