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IntroZen BI technical preview
Data EngineeringETLBusiness IntelligenceStatus: In Development

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.

Business Context & Problem

Business data can come from different sources and formats, making it difficult to collect, process, prepare, and analyze that data through a unified workflow.

System Solution

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.

Architecture & Engineering Execution

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.

DjangoDjango ChannelsGrafanaPrometheusCeleryRedisParquetPandasNumPyDockerNext.jsTypeScriptAxiosTailwind CSSRechartsWebSocketDockerDocker Compose

Key Engineering Challenges

ETL Pipeline Execution

Requirement

Data extraction and processing tasks can be resource-intensive and should not block normal application requests.

Implementation

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

Requirement

Raw extracted data needs to be processed into a structured format that can be efficiently explored and visualized.

Implementation

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

Requirement

Processed datasets need to be presented in a way that makes business data easier to explore and understand.

Implementation

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.

Core System Capabilities

  • 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