Is Azure Storage File Datalake Safe?

Azure Storage File Datalake — Nerq Trust Score 79.2/100 (B+ grade). Score based on 2 independent trust signals. Last analyzed: 2026-04-12

Azure Storage File Datalake is a Python package with a Nerq Trust Score of 79.2/100 (B+), based on 3 independent data dimensions. Last analyzed: 2026-04-12 Security: 90/100. Popularity: 90/100. Data sourced from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. Last updated: 2026-04-12. Machine-readable data (JSON).

Is Azure Storage File Datalake safe?

Trust Score Breakdown — Azure Storage File Datalake has a Nerq Trust Score of 79.2/100 (B+). Measured across 2 independent trust signals (as of 2026-04-12).

Security Analysis → Azure Storage File Datalake Privacy Report →

What is Azure Storage File Datalake's trust score?

Azure Storage File Datalake has a Nerq Trust Score of 79.2/100, earning a B+ grade. This score is based on 2 independently measured dimensions including security, maintenance, and community adoption.

Security
90
Popularity
90

What are the key security findings for Azure Storage File Datalake?

Azure Storage File Datalake's strongest signal is security at 90/100. No known vulnerabilities have been detected.

Security score: 90/100 (strong)
Popularity: 90/100 — community adoption

What is Azure Storage File Datalake and who maintains it?

AuthorMicrosoft Corporation
CategoryPython Packages
SourceN/A

Azure Storage File Datalake Across Platforms

Same developer/company in other registries:

Azure Storage File Datalake
46/100 · ai_tool
azure-storage-blob
68/100 · gems
azure-storage-common
68/100 · gems
Microsoft PowerPoint
67/100 · android
Microsoft Excel: Spreadsheets
67/100 · android

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Safety Guide: Azure Storage File Datalake

What is Azure Storage File Datalake?

Azure Storage File Datalake is a Python package — Microsoft Azure File DataLake Storage Client Library for Python.

How to Verify Safety

Run pip audit or safety check. Review on PyPI for download stats.

You can also check the trust score via API: GET /v1/preflight?target=azure-storage-file-datalake

Key Safety Concerns for Python package

When evaluating any Python package, watch for: dependency vulnerabilities, malicious uploads, maintenance status.

Measured Signals

Azure Storage File Datalake has a Nerq Trust Score of 79/100 (B+). This score is a composite of automated measurements of security, maintenance, community, and quality signals.

Key Takeaways

Detailed Score Analysis

DimensionScore
Security90/100
Maintenance100/100
Popularity90/100
Quality65/100
Community35/100

Based on 5 dimensions. Data from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard.

What data does Azure Storage File Datalake collect?

Azure Storage File Datalake is a Python package maintained by Microsoft Corporation. It receives approximately 8,465,235 weekly downloads. Licensed under MIT License.

As a development package, Azure Storage File Datalake does not directly collect end-user personal data. However, applications built with it may collect data depending on implementation. Privacy score: 80/100.

Review the package's dependencies for potential supply chain risks. Run your package manager's audit command regularly.

Full analysis: Azure Storage File Datalake Privacy Report · Privacy review

Is Azure Storage File Datalake secure?

Security score: 90/100. Azure Storage File Datalake has 0 known vulnerabilities (CVEs) in the National Vulnerability Database. This is a clean record.

Licensed under MIT License, allowing code inspection. Open-source packages allow independent security review of the source code.

Run your package manager's audit command (`npm audit`, `pip audit`, `cargo audit`) to check for known vulnerabilities in your dependency tree.

Full analysis: Azure Storage File Datalake Security Report

Azure Storage File Datalake Across Platforms

Same developer/company in other registries:

Azure Storage File Datalake (ai_tool, 46/100)azure-storage-blob (gems, 68/100)azure-storage-common (gems, 68/100)Microsoft PowerPoint (android, 67/100)

How we calculated this score

Azure Storage File Datalake's trust score of 79.2/100 (B+) is computed from PyPI registry, GitHub repository, NVD, OSV.dev, and OpenSSF Scorecard. The score reflects 5 independent dimensions: security (90/100), maintenance (100/100), popularity (90/100), quality (65/100), community (35/100). Each dimension is weighted equally to produce the composite trust score.

Nerq analyzes over 7.5 million entities across 26 registries using the same methodology, enabling direct cross-entity comparison. Scores are updated continuously as new data becomes available.

Signals last measured on April 12, 2026. Data version: 1.0.

Full methodology documentation · Machine-readable data (JSON API)

Frequently Asked Questions

Is Azure Storage File Datalake Safe?
azure-storage-file-datalake with a Nerq Trust Score of 79.2/100 (B+). Strongest signal: security (90/100). Score based on Security (90/100), Popularity (90/100).
What is Azure Storage File Datalake's trust score?
azure-storage-file-datalake: 79.2/100 (B+). Score based on Security (90/100), Popularity (90/100). Scores update as new data becomes available. API: GET nerq.ai/v1/preflight?target=azure-storage-file-datalake
What are safer alternatives to Azure Storage File Datalake?
In the Python Packages category, more Python packages are being analyzed — check back soon. azure-storage-file-datalake scores 79.2/100.
Does Azure Storage File Datalake have known vulnerabilities?
Nerq checks Azure Storage File Datalake against NVD, OSV.dev, and registry-specific vulnerability databases. Current security score: 90/100. Run your package manager's audit command for the latest findings.
Is Azure Storage File Datalake actively maintained?
Azure Storage File Datalake maintenance score: N/A. Check the repository for recent commit activity and issue responsiveness.
API: /v1/preflight Trust Badge API Docs

See Also

Disclaimer: Nerq trust scores are automated measurements based on publicly available signals. They are not endorsements, verdicts, or guarantees of suitability. Always evaluate the signals against your own requirements.

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