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This project simulates user behavior on a SaaS learning platform and analyzes product growth metrics using Python. The analysis focuses on understanding how users move through the product funnel, identifying drop-off points, and evaluating experiments that aim to improve conversion to paid users. The project also includes an A/B testing simulation
🧠 Model-driven synthetic test data for CI/CD and analytics - deterministic, privacy-preserving, and domain-aware. Includes Python APIs, XML pipelines, and MCP/IDE integration to orchestrate realistic datasets for finance, healthcare, and other regulated environments.
High-performance, multi-stream data ingestion simulator Built for testing real-time pipelines, PB-scale throughput, and stream processing systems like Kafka, Flink, FastAPI, and Iceberg.
This repository contains projects and exercises I completed during my "Big Data Architecture" course. It reflects the concepts I’ve learned about data processing using Apache Spark and PySpark.