atoti-client-storage-azure

v0.9.15 suspicious
4.0
Medium Risk

Code to load data from Azure Blob cloud storage

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows no direct signs of malicious activity, but the lack of documentation and the author's single package presence raise concerns about its legitimacy and purpose.

  • Lack of package description
  • Author has only one published package
Per-check LLM notes
  • Network: No network calls detected, which is not necessarily suspicious for an Azure storage client package but should be confirmed with package documentation or source code.
  • Shell: No shell execution patterns detected, aligning with expectations for a library focused on Azure storage operations.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating secure handling of sensitive information.
  • Metadata: The author 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 (4.2/10)

○ Low Test Suite 1.0

No test suite detected

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

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.activeviam.com/products/atoti/python-sdk/0.9.15
○ 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-client-storage-azure 
Create a mini-application named 'AzureDataLoader' that leverages the 'atoti-client-storage-azure' package to demonstrate how to efficiently load datasets stored in Azure Blob Storage into a local Python environment. This application will serve as a versatile tool for data scientists and analysts who need to work with large datasets hosted on Azure Blob Storage but prefer to analyze them locally using Python libraries such as Pandas or Dask.

#### Project Overview:
- **Application Name:** AzureDataLoader
- **Primary Functionality:** Efficiently download and load datasets from Azure Blob Storage into a local Python environment.
- **Target Audience:** Data Scientists, Analysts, and Engineers working with large datasets hosted on Azure Blob Storage.
- **Key Features:**
  - Authentication via Azure credentials.
  - Support for multiple file formats (CSV, Parquet, etc.).
  - Progress tracking during download.
  - Error handling for network issues and file corruption.
  - Option to directly load the downloaded dataset into a Pandas DataFrame or Dask DataFrame.
  - User-friendly command-line interface for easy interaction.

#### Utilization of 'atoti-client-storage-azure':
- Use the 'atoti-client-storage-azure' package to establish a connection to the Azure Blob Storage account where your datasets are stored.
- Implement functions within the 'AzureDataLoader' app that utilize the package's capabilities to authenticate, navigate through directories, and download specific files or entire folders.
- Ensure the application supports various file formats by integrating file-specific parsers after downloading.

#### Step-by-Step Guide:
1. **Setup:** Install the required packages ('atoti-client-storage-azure', Pandas, Dask) and set up the Azure credentials.
2. **Connection:** Establish a secure connection to the Azure Blob Storage using the 'atoti-client-storage-azure' package.
3. **Navigation:** Allow users to browse and select the desired file(s) or folder(s) from the Azure Blob Storage.
4. **Download & Load:** Download the selected file(s), track the progress, and handle errors gracefully. Once downloaded, parse the file(s) into a DataFrame using Pandas or Dask based on user preference.
5. **Interactive Interface:** Develop a simple command-line interface for users to interact with the application easily, including options to specify file paths, choose between different file formats, and decide on the loading method.
6. **Documentation:** Provide comprehensive documentation detailing setup, usage, and troubleshooting tips.

This project aims to streamline the process of accessing and analyzing data stored in Azure Blob Storage, making it more accessible and efficient for data professionals.

💬 Discussion Feed

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