About the Customer
The client is a data-centric enterprise focused on transforming raw consumer behavior into actionable insights for the automotive industry. Their core requirement was to optimize and automate ETL (Extract, Transform, Load) processes that feed critical decision-making dashboards used by stakeholders across regions, product lines, and customer segments.
With fast-growing data volumes and recurring reporting cycles (daily, weekly, monthly), the existing workflows had become too manual and rigid. The client needed a solution that not only modernized their data pipeline but also ensured scalability, transparency, and efficiency across their entire data infrastructure.
Project Overview
DataDrive is a high-frequency ETL automation system designed to process granular car shopper data across DMAs (Designated Market Areas), regions, and national levels. The system extracts information from homogeneous sources, applies transformations like calculations and data cleansing, and loads it into target destinations such as data lakes, marts, or warehouses.
Our primary objective was to engineer a flexible, automated ETL architecture capable of evolving with the client’s business logic. To achieve this, we implemented modular ETL pipelines, intuitive dashboards, and a robust job scheduling system to reduce operational bottlenecks and improve data accessibility.
Purpose
The core goal of datadrive was to make data preparation fast, repeatable, and maintainable for all stakeholder teams. By automating repetitive tasks and creating smart visual workflows, the system empowers analysts and business leaders with timely, trustworthy data — without depending on engineering teams for each update.
Business Challenge
To deliver a seamless and scalable ETL automation solution, the project involved:
Creating custom DAG files using Apache Airflow to orchestrate various ETL jobs.
Building and optimizing ETL pipelines that handle high-volume data transfers to warehouse systems.
Developing interactive web dashboards using Plotly Dash for reporting and monitoring.
Implementing CI/CD-enabled periodic job scheduling using Jenkins for daily, weekly, and monthly operations.
Our Solution
To meet the business needs and overcome the technical hurdles, our team at Inexture designed a modular ETL automation platform using modern data engineering practices.
Built reusable and optimized DAG structures in Apache Airflow to run scheduled and on-demand jobs with minimal manual intervention.
Developed Plotly Dash interfaces to help business users and analysts interact with ETL logs, reports, and summaries in a visual, easy-to-understand format.
Integrated Jenkins pipelines to ensure regular ETL task execution with complete logging and alerting.
Created Python-powered APIs and backend services that control, monitor, and report ETL activities in real time.
This approach helped the client reduce manual ETL efforts by over 60%, improve reporting agility, and eliminate common points of failure in their earlier setup.
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Key Challenges
Custom DAG Creation with Airflow:
We had to design and deploy highly tailored DAGs to support varied ETL operations across datasets and regions. This required deep exploration of Airflow’s scheduling, dependencies, and error-handling systems.
Advanced GUI with Plotly Dash:
Creating a user-friendly, visually rich dashboard in Python required leveraging Plotly Dash’s complex components and layouts while ensuring performance with large datasets.
Optimized APIs for ETL Control:
We designed and built RESTful APIs to control and trigger ETL pipelines, delivering performance and reliability with Python optimizations and robust exception handling.
Jenkins Job Management:
Jenkins was integrated to automate all recurring ETL jobs, ensuring data from multiple external sources was extracted, transformed, and loaded into data warehouses efficiently and on schedule.
Project Name
DataDrive
Category
Python Development
Technology Stack
- Python
- Django
- Flask
- Apache Airflow
- Plotly Dash
- Pandas
- NumPy
- Jenkins
Industry
Automotive Data Intelligence
