IntroZen BI
Data Integration, ETL & Business Intelligence Platform
A data platform for connecting data sources, building ETL pipelines, processing and preparing datasets, and turning structured data into interactive tables and visualizations.
Technologies
The Problem
What needed to be solved
Business data can come from different sources and formats, making it difficult to collect, process, prepare, and analyze that data through a unified workflow.
The Solution
How it was approached
IntroZen BI provides a centralized platform for adding data sources, building ETL pipelines, extracting and processing data, preparing datasets for analysis, and exploring the resulting data through tables and visualizations.
Capabilities
Key Features
Data source management
ETL pipeline building
Data extraction
Data transformation
Data processing
Dataset preparation
Parquet-based data handling
Processed data tables
Interactive charts and visualizations
My Role
What I worked on
Designed and developed the platform across its frontend, backend, data processing, pipeline execution, and visualization workflows.
Engineering
Architecture
IntroZen BI uses Django for the backend, Celery and Redis for asynchronous data-processing workflows, Parquet for structured data handling, and a Next.js frontend for managing pipelines and exploring processed datasets through tables and visualizations. The application is containerized with Docker.
Engineering Challenges
Problems solved along the way
ETL Pipeline Execution
Problem
Data extraction and processing tasks can be resource-intensive and should not block normal application requests.
Solution
Used Celery with Redis to handle data-processing workflows asynchronously in the background.
Outcome
Created a foundation for executing data-processing tasks independently from the main application request cycle.
Data Processing & Preparation
Problem
Raw extracted data needs to be processed into a structured format that can be efficiently explored and visualized.
Solution
Built the data workflow around processing and preparing datasets for downstream analysis and visualization, including Parquet-based data handling.
Outcome
Made processed datasets available for exploration through tables and visualizations.
Data Visualization
Problem
Processed datasets need to be presented in a way that makes business data easier to explore and understand.
Solution
Built interactive data views using tables and chart-based visualizations in the Next.js frontend.
Outcome
Provided users with a visual interface for exploring processed data.
Project Preview
Screenshots
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