You will get End-to-End ETL Pipeline with Python, Airflow, Snowflake & Looker Studio
Project details
End-to-End ETL Pipeline with Python, Airflow, Spark, Docker, S3, Snowflake & Looker Studio
This project delivers a fully automated, scalable, and high-performance ETL pipeline that integrates cutting-edge data engineering technologies to streamline data extraction, transformation, storage, and visualization. Unlike traditional ETL solutions, this pipeline leverages Apache Airflow for orchestration, Apache Spark for big data processing, Docker for containerization, and Snowflake for optimized storage, ensuring seamless data flow with minimal latency.
What sets this project apart is its real-time analytics capability, allowing businesses to make data-driven decisions faster. By integrating Google Looker Studio, we provide interactive dashboards that transform complex datasets into actionable insights. Whether for finance, e-commerce, or SaaS applications, this solution is designed for efficiency, scalability, and automation, eliminating manual data handling and optimizing cloud costs.
This project is not just about data movement—it’s about empowering businesses with real-time intelligence and a future-proof data architecture.
This project delivers a fully automated, scalable, and high-performance ETL pipeline that integrates cutting-edge data engineering technologies to streamline data extraction, transformation, storage, and visualization. Unlike traditional ETL solutions, this pipeline leverages Apache Airflow for orchestration, Apache Spark for big data processing, Docker for containerization, and Snowflake for optimized storage, ensuring seamless data flow with minimal latency.
What sets this project apart is its real-time analytics capability, allowing businesses to make data-driven decisions faster. By integrating Google Looker Studio, we provide interactive dashboards that transform complex datasets into actionable insights. Whether for finance, e-commerce, or SaaS applications, this solution is designed for efficiency, scalability, and automation, eliminating manual data handling and optimizing cloud costs.
This project is not just about data movement—it’s about empowering businesses with real-time intelligence and a future-proof data architecture.
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$100
|
Standard
$200
|
Advanced
$300
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Pages Mined/Scraped | 3 | 5 | 7 |
Number of Sources Mined/Scraped | 1 | 2 | 3 |
Number of Revisions | 1 | 2 | 3 |
About ABU MOHAMMAD
Modern Data Platform Engineer
Dhaka, Bangladesh - 7:08 am local time
Steps for completing your project
After purchasing the project, send requirements so ABU MOHAMMAD can start the project.
Delivery time starts when ABU MOHAMMAD receives requirements from you.
ABU MOHAMMAD works on your project following the steps below.
Revisions may occur after the delivery date.
Milestone 1: Project Kickoff & Requirements Gathering
✅ Define project scope, data sources, and objectives ✅ Gather access credentials (APIs, databases, cloud storage) ✅ Confirm infrastructure preferences (AWS, GCP, Snowflake, etc.) ✅ Create a detailed project roadmap & timeline
Milestone 2: ETL Pipeline Development
✅ Data Extraction: Set up Apache Airflow to automate data ingestion from APIs, databases, or cloud storage ✅ Data Transformation: Implement Apache Spark jobs for cleaning, aggregating, and processing. ✅ Data Storage: ✅ Containerization & Deployment: