This project focuses on building an efficient ETL (Extract, Transform, Load) Pipeline. The primary aim is to facilitate data transfer and visualization through a web dashboard, improving error reporting in the pipeline.
- Aim: Building an ETL Pipeline for data transfer and visualization.
- Initial Solution: Our early attempts using SQL Server, Postgres, Airflow, and Alteryx.
- Our Cloud-Based Solution: Transitioning to cloud for simplified data processing and handling large data volumes.
- Tools Used: A range of AWS services including DBeaver, Redshift, Glue, CloudWatch, and QuickSight.
- Front-End Development: Built using ReactJS and deployed on Netlify.
- Optimization Techniques: Improvements for efficiency, including compound sort keys, Redshift DIST keys, and vacuuming.
- Viability: Initial and final metrics showing the effectiveness of our pipeline.
- Market Realism: The practicality of our solution for large-scale businesses and diverse data.
- Future Plans: Our roadmap for continued development and enhancement.
Our goal was to create an efficient ETL pipeline, capable of handling large-scale data migrations and processing, with a focus on visualization and error reporting.
We found ETL Cloud Data Warehousing to be a new and exciting challenge, offering an opportunity to learn from scratch and explore a less-trodden path in data engineering.
Our initial approach involved extracting data from SQL Server, loading it into Postgres, and automating the pipeline using Airflow, with a focus on incremental data loading and visualization using Alteryx.
We transitioned to a cloud-based solution to simplify data processing, handle large data volumes, and enable the combination of data from multiple sources. This approach facilitated easier visualization, debugging, and data analysis.
- DBeaver: For database management.
- Redshift Data Pipeline: For large-scale data storage and analysis.
- Glue: To extract data and incorporate it into data lakes and warehouses.
- CloudWatch: For application and resource monitoring.
- QuickSight: For delivering insights and data visualization.
Our solution involved setting up a Redshift cluster, creating S3 buckets, configuring DataNodes, and creating EC2 instances. We also implemented incremental data pipelines and AWS Glue Jobs.
The front end was developed using ReactJS and deployed on Netlify, with domain configurations done on the AWS page.
We employed various optimization techniques like compound sort keys, Redshift DIST keys, and vacuuming to enhance the efficiency of our pipeline.
Our pipeline showed significant improvements in metrics like time in queue, run time, and data scanned, demonstrating its viability.
Our solution is ideal for large-scale businesses working with huge data volumes, offering centralized location benefits, business intelligence capabilities, and cloud-based safety and optimization.
We aim to further develop and enhance our ETL pipeline, ensuring it remains cutting-edge and market-relevant.