End-to-End Business Intelligence Solution
Budget: ₹12,500 – ₹37,500 INR
Project Overview:
Designed and implemented a robust, end-to-end data engineering and business intelligence solution. The project involved automating multi-source data ingestion, complex ETL transformations, hybrid database management, and delivering actionable insights via an executive dashboard.
Key Deliverables & Technical Milestones:
Automation & Ingestion (Unix & Shell Scripting): * Developed production-grade Unix Shell scripts to automate daily data downloads from remote servers.
Implemented error handling, logging, and file validation checks within the shell pipeline to ensure zero data loss during ingestion.
Data Cleaning & Preprocessing (Python): * Utilized Python (Pandas/NumPy) to handle raw, messy source data.
Resolved structural data issues, treated missing values, eliminated duplicates, and standardized data formats for downstream consumption.
Scalable ETL Processing (PySpark): * Leveraged PySpark to process and transform large-scale datasets efficiently, optimizing partition strategies to reduce execution time.
Executed complex business logic, aggregations, and data joins across massive distributed data frames.
Hybrid Data Storage (PL/SQL & MongoDB):
Relational (PL/SQL): Designed relational schemas, wrote optimized stored procedures, triggers, and complex analytical queries to manage structured transactional data.
NoSQL (MongoDB): Handled semi-structured and unstructured data elements, managing high-throughput document storage with optimized indexing for fast retrieval.
Business Intelligence & Insights (Power BI): * Built an interactive, dynamic Power BI Dashboard connected to the processed data layer.
Utilized advanced DAX measures to track key performance indicators (KPIs), enabling stakeholders to make data-driven decisions at a glance
Designed and implemented a robust, end-to-end data engineering and business intelligence solution. The project involved automating multi-source data ingestion, complex ETL transformations, hybrid database management, and delivering actionable insights via an executive dashboard.
Key Deliverables & Technical Milestones:
Automation & Ingestion (Unix & Shell Scripting): * Developed production-grade Unix Shell scripts to automate daily data downloads from remote servers.
Implemented error handling, logging, and file validation checks within the shell pipeline to ensure zero data loss during ingestion.
Data Cleaning & Preprocessing (Python): * Utilized Python (Pandas/NumPy) to handle raw, messy source data.
Resolved structural data issues, treated missing values, eliminated duplicates, and standardized data formats for downstream consumption.
Scalable ETL Processing (PySpark): * Leveraged PySpark to process and transform large-scale datasets efficiently, optimizing partition strategies to reduce execution time.
Executed complex business logic, aggregations, and data joins across massive distributed data frames.
Hybrid Data Storage (PL/SQL & MongoDB):
Relational (PL/SQL): Designed relational schemas, wrote optimized stored procedures, triggers, and complex analytical queries to manage structured transactional data.
NoSQL (MongoDB): Handled semi-structured and unstructured data elements, managing high-throughput document storage with optimized indexing for fast retrieval.
Business Intelligence & Insights (Power BI): * Built an interactive, dynamic Power BI Dashboard connected to the processed data layer.
Utilized advanced DAX measures to track key performance indicators (KPIs), enabling stakeholders to make data-driven decisions at a glance