Hello! I'm a software engineer based in Los Angeles, CA with expertise in distributed systems, data engineering, and machine learning. I believe iteration is essential and that great ideas can come from anyone so it's important to keep an open mind.

A personal homelab environment setup and configuration repository. This project documents my home infrastructure setup with automation scripts, configuration files, and deployment procedures. Using infrastructure as code principles to maintain a robust and scalable home lab environment for experimentation and learning.

Tech Stack: Python

Ray Vector Database Input/Output Utilities. This project provides tools for seamless integration between Ray, a distributed computing framework, and various vector databases. Designed to optimize data pipeline workflows involving vector embeddings for AI applications like semantic search and recommendation systems.

Key Features:

• Streamlined data loading/extraction from vector databases
• Integration with Ray's distributed computing ecosystem
• Support for various vector database formats

An example project demonstrating how to combine Apache Spark Streaming, Kafka, and Parquet to build a robust data pipeline. This project transforms JSON objects streamed over Kafka into Parquet files stored in S3, showcasing a complete ETL workflow.

Tech Stack: Scala, Apache Spark, Kafka, Parquet, S3

Key Features:

• Real-time data processing with Spark Streaming
• JSON message consumption from Kafka topics
• Efficient storage using Parquet file format
• Cloud integration with Amazon S3

A demonstration of how to build microservices with Spring Boot using Redis for various purposes. Includes examples of authentication, feature flags, and HyperLogLog for user counting.

Tech Stack: Java, Spring Boot, Redis

Key Features:

• Microservices architecture patterns
• Redis integration for caching and data storage
• Authentication and authorization examples
• Feature flag implementation
• User count tracking with HyperLogLog

A personal repository for experimenting with Rust programming language concepts and patterns. This project serves as a learning environment and reference for Rust development.

Tech Stack: Rust

A tutorial repository demonstrating how to effectively use regular expressions with Apache Spark. Covers pattern matching, text extraction, and data transformation techniques using Spark's regex capabilities.

Tech Stack: Apache Spark, Scala

• Languages: Python, Scala, Java, Rust
• Big Data: Apache Airflow, Apache Spark, Kafka, Ray, Parquet
• Databases: Redis/Valkey, Cassandra, PostgreSQL, Qdrant
• Cloud: AWS, GCP
• DevOps: Docker, Kubernetes, GitHub Actions

Feel free to reach out to me through my LinkedIn profile or visit my personal website to learn more about my work.