Pentester - ETL Pipeline Development

Pentester.com is a cybersecurity platform that offers services to help individuals and businesses identify and mitigate security risks. The platform provides tools for monitoring digital footprints, detecting data breaches, and identifying website vulnerabilities. It features automated data removal, AI-driven automation, and extensive security testing to protect sensitive information and ensure compliance with industry standards.

My Role

I designed an ETL pipeline for Pentester.com to handle the extraction, transformation, and storage of large-scale profile information from raw data sources. With 8TB of data to process, requiring up to a month to complete, I implemented advanced features like dynamic configuration generation, file sharding, and checkpoint-based resume/pause functionality to maximize efficiency and data integrity.

Challenges

  • Automate data extraction and parsing with minimal manual intervention.
  • Implement checkpoint-based processing to enable resumption from the last processed file in case of interruptions.
  • Ensure scalability for large datasets by introducing file sharding.

Solutions

  • Data Collection: Aggregate individual raw data files from the source location.
  • Dynamic Parsing Configuration: For each file, retrieve an existing parsing configuration or use Langchain to auto-generate one via LLM if it does not exist.
  • File Sharding for Large Files: Split large files into smaller shards to improve processing speed and reduce memory load.
  • Data Parsing and Storage: Apply parsing configurations to each shard, extracting structured data. Store the parsed data in PostgreSQL using Django ORM.
  • Checkpoint-based Resume/Pause Functionality: Implemented using Python to maintain a checkpoint log for each processed file, enabling the system to pause and resume from the last checkpoint.

Impact

A scalable, automated solution capable of handling large datasets. Improved fault tolerance and reduced downtime with resume/pause capabilities. Successfully structured and stored profiles for efficient use in downstream applications.