Projects using PySpark

Projects using PySparkProjects using PySpark

Cover image for Creating a Dimensional Model (Star Schema) in Databricks

Creating a Dimensional Model (Star Schema) in Databricks

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Cover image for Tableau Viz

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6

Cover image for Retail Demand Forecasting Dashboard

Retail Demand Forecasting Dashboard

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Cover image for Problem:

Users needed a faster way

Problem: Users needed a faster way to find the most relevant counselor based on specialization and available data. Solution: Built a recommendation workflow using PySpark for data processing and Redis for fast lookup and recommendation serving. Tools: Python, PySpark, Redis, Docker, Docker Compose Result: Created a containerized recommendation system that processes counselor data and returns relevant matches efficiently.

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73

Cover image for Big Data Aggregation and Processing System

Big Data Aggregation and Processing System

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30

Cover image for USDA Health Score Prediction Using Deep Learning

USDA Health Score Prediction Using Deep Learning

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13

Cover image for Analyzed customer acquisition funnel data

Analyzed customer acquisition funnel data using PySpark and Tableau to identify conversion bottlenecks, drop-off points, and high-value customer segments. The project combined large-scale data processing with interactive dashboards to support data-driven marketing and sales decisions

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Cover image for I engineered a large-scale package-network

I engineered a large-scale package-network intelligence pipeline using Azure Databricks and PySpark. The system joined approximately 4.5 billion historical records and transformed them into reusable fingerprint signals covering package movement and network behavior. I developed time-windowed feature logic using native Spark expressions rather than Python UDFs, preserving distributed performance at scale. The pipeline produced an approximately 200-million-row fingerprint dataset and a 22-million-row operational output that classified package activity along a cold-to-hot temperature scale. The resulting signals supported downstream monitoring, prioritization, and operational analysis. The architecture shown here is a sanitized representation; client identifiers, proprietary schemas, infrastructure paths, and business rules have been removed.

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Cover image for Scalable E-commerce Big Data Platform for BuildItAll

Scalable E-commerce Big Data Platform for BuildItAll

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5

Cover image for The pipeline focuses on data quality,

The pipeline focuses on data quality, scalability, and analytics readiness, converting raw transactional data into a clean, trusted fact table suitable for BI and reporting. https://github.com/antara-1505/Sales-Analytics-System

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Cover image for Real-time NFL Draft Prediction Model Deployment

Real-time NFL Draft Prediction Model Deployment

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7

Cover image for End-to-End Microsoft Fabric Development

End-to-End Microsoft Fabric Development

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Cover image for CIFAR-10 Image Classification Model Development

CIFAR-10 Image Classification Model Development

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6

Cover image for AI Pricing Agent

AI Pricing Agent

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Cover image for Microsoft Fabric Project - End-to-End

Microsoft Fabric Project - End-to-End Analytics Solution with Microsoft Fabric - Developed an end-to-end analytics solution using Microsoft Fabric, including Lakehouse, Notebooks, Pipelines, and semantic models to automate data ingestion and reporting workflows.

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Cover image for NYC Taxi Data Processing and Analysis Pipeline

NYC Taxi Data Processing and Analysis Pipeline

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4