aws-solutions-constructs.aws-kinesis-streams-gluejob

v2.102.0 safe
3.0
Low Risk

CDK Constructs for streaming data from AWS Kinesis Data Stream for Glue ETL custom Job processing

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators with no network calls, shell executions, obfuscations, or credential risks. The metadata suggests a potentially new or less active author, but there are no additional red flags.

  • No network calls detected
  • Single package from the author
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting that the package is not likely involved in malicious credential theft activities.
  • Metadata: The author has only one package, which might indicate a new or less active account, but no other red flags are present.

📦 Package Quality Overall: Low (3.8/10)

○ 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 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 6 unique contributor(s) across 100 commits in awslabs/aws-solutions-constructs
  • Active community — 5 or more distinct contributors

🔬 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

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository awslabs/aws-solutions-constructs 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 aws-solutions-constructs.aws-kinesis-streams-gluejob 
Create a mini-application that leverages the 'aws-solutions-constructs.aws-kinesis-streams-gluejob' package to stream real-time financial market data for analysis. This application will demonstrate the integration of AWS Kinesis Data Streams with AWS Glue for processing the incoming data. The goal is to create a system where stock tickers are streamed into a Kinesis Data Stream, and then processed by a Glue ETL job configured through the provided constructs.

Steps to follow:
1. Set up an AWS account and configure your local environment for AWS CDK development.
2. Install the necessary Python packages including 'aws-cdk-lib', 'constructs', and 'aws-solutions-constructs.aws-kinesis-streams-gluejob'.
3. Create an AWS Kinesis Data Stream to ingest real-time stock ticker data.
4. Use the 'aws-solutions-constructs.aws-kinesis-streams-gluejob' package to define and configure a Glue ETL job that processes the data from the Kinesis Data Stream.
5. Implement a simple mechanism to simulate the streaming of stock ticker data into the Kinesis Data Stream (e.g., using a script or API).
6. Configure the Glue ETL job to perform basic analytics on the stock ticker data, such as calculating moving averages or identifying price trends.
7. Integrate AWS CloudWatch for monitoring the health and performance of both the Kinesis Stream and the Glue ETL job.
8. Optionally, extend the application by adding features like visualizing the processed data using Amazon QuickSight or storing the results in Amazon S3.

Features to consider:
- Real-time data streaming from a simulated stock market data source.
- Configurable Glue ETL jobs for various types of data analysis.
- Monitoring and logging of the data stream and processing activities.
- Scalability to handle varying loads of data streaming.
- Visualization of processed data insights.

By completing this project, you'll gain hands-on experience with AWS Kinesis, AWS Glue, and CDK Constructs, as well as understand how these services can be used together to process large volumes of real-time data.

💬 Discussion Feed

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