awswrangler

v3.16.1 safe
3.0
Low Risk

Pandas on AWS.

🤖 AI Analysis

Final verdict: SAFE

The package is generally safe with a low risk score. While there are some unusual patterns such as shell execution and credential handling, these do not strongly indicate malicious intent given the package's purpose.

  • Unusual shell execution
  • Potential risk in handling credentials
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package is designed to interact with external services.
  • Shell: The shell execution pattern is unusual and may indicate potential risk if commands executed are not part of the package's intended functionality.
  • Obfuscation: The observed patterns involve standard Base64 decoding which is commonly used for handling encrypted or binary data in a text format, not indicative of malicious obfuscation.
  • Credentials: The code retrieves environment variables that could contain sensitive information such as AWS credentials and configurations. This practice should be handled with caution to prevent accidental exposure of secrets.
  • Metadata: The maintainer has only one package, but no other suspicious activities were flagged.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://aws-sdk-pandas.readthedocs.io/
  • Detailed PyPI description (14357 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 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 528 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 16 unique contributor(s) across 100 commits in aws/aws-sdk-pandas
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • se["SecretString"] return base64.b64decode(response["SecretBinary"]) def get_secret_json(name: str, b
  • glue_query: str = json.loads(base64.b64decode(glue_base64_query))["originalSql"] return f"""CREATE
  • nt_kms.decrypt(CiphertextBlob=base64.b64decode(res["ConnectionProperties"]["ENCRYPTED_PASSWORD"]))[
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • for command in COMMANDS: subprocess.run(command.split(" "), timeout=6.0, check=True) print("done!")
⚠ Credential Harvesting score 10.0

Found 6 credential access pattern(s)

  • { "max_attempts": int(os.getenv("AWS_MAX_ATTEMPTS", "5")), } mode = os.getenv("AWS_RETRY_
  • PTS", "5")), } mode = os.getenv("AWS_RETRY_MODE") if mode: retries_config["mode"] = m
  • NTAINER_RUNTIME_DOCKER_MOUNTS=/etc/passwd:/etc/passwd:ro " f"--conf spark.yarn.appMasterE
  • IME_DOCKER_MOUNTS=/etc/passwd:/etc/passwd:ro " f"--conf spark.yarn.appMasterEnv.YARN_CONT
  • NTAINER_RUNTIME_DOCKER_MOUNTS=/etc/passwd:/etc/passwd:ro " f"{path} {script_args}"
  • IME_DOCKER_MOUNTS=/etc/passwd:/etc/passwd:ro " f"{path} {script_args}" ) retu
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.mypy-lang.org/static/mypy_badge.svg
  • Non-HTTPS external link: http://mypy-lang.org/
✓ Git Repository History

Repository aws/aws-sdk-pandas appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Amazon Web Services" 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 awswrangler 
Your task is to develop a fully-functional mini-application using the 'awswrangler' Python package, which simplifies data analysis and manipulation tasks within the AWS ecosystem. This application will serve as a data pipeline tool that ingests data from S3, performs complex data transformations, and then stores the processed data back into S3. Here’s a step-by-step guide to building this application:

1. **Setup and Configuration**: Begin by setting up your development environment. Ensure you have Python installed along with the necessary AWS SDK and awswrangler packages. Configure your AWS credentials and region settings.
2. **Data Ingestion**: Write a function that reads raw data stored in an S3 bucket. Use awswrangler's `s3.read_csv` or `s3.read_parquet` functions depending on the file format of your raw data.
3. **Data Transformation**: Develop a series of data transformation steps. These could include filtering, aggregating, joining datasets, or applying custom functions. Utilize awswrangler's capabilities to perform these operations efficiently.
4. **Data Validation**: Implement checks to ensure the transformed data meets certain criteria. For example, verify if the data types match expectations, or if there are no null values in critical columns.
5. **Data Export**: Finally, export the processed data back to S3 in a different format or location than the input files. Use awswrangler's `s3.to_csv` or `s3.to_parquet` functions to accomplish this.
6. **Error Handling and Logging**: Incorporate robust error handling mechanisms and logging to track the execution flow and capture any issues encountered during the process.
7. **Automation and Scheduling**: Consider automating the pipeline using AWS Lambda, Step Functions, or another service to run at scheduled intervals.

Suggested Features:
- Ability to handle multiple file formats (CSV, Parquet)
- Support for dynamic data transformation based on user-defined scripts
- Integration with AWS Glue Data Catalog for schema management
- Real-time monitoring of the data pipeline's performance

This project will not only demonstrate your proficiency with awswrangler but also showcase your ability to build scalable and efficient data processing solutions within the AWS environment.

💬 Discussion Feed

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