Whoozbuying

Whoozbuying is a SaaS-based ETL tool that ensures seamless data integration with dynamic health scoring for high data accuracy and reliability.

Introduction

Whoozbuying, a SaaS-based ETL (Extract, Transform, Load) tool, required a robust platform to efficiently extract data from various sources, clean and transform this data, and then load it into target systems.

Challenges

At a Glance

Here are the challenges we encountered while developing the application.

WHOOZBUYING

1 Complex Data Integration

Ensuring seamless data flow between source and destination systems.

2 Data Cleansing and Transformation

Developing a system to clean data by removing unnecessary characters and ensuring it matches input patterns. Allowing users to map input data and apply customizable cleansing rules.

3 Health Score Calculation

Implementing a system to calculate dynamic health scores for uploaded data, reflecting its accuracy and reliability.

4 Scalable and Efficient Back-end

Building a scalable back-end for large data set and ensuring efficient data processing and storage.

Solutions Blueprint

DEVtrust implemented a comprehensive solution to address these challenges

Data Integration with Airbyte

Developed a front-end using ReactJS and integrated Airbyte APIs for easy data extraction from various sources.
Implemented source and destination mapping to ensure seamless data flow
These are the various data sources from which the ETL tool can extract and load data: CSV, S3 Bucket, Snowflake, Google Sheet, Salesforce.
The ETL (Extract, Transform, Load) process is facilitated by AWS for infrastructure, Airbyte for data integration, and Python for data transformation

Advanced Data Cleansing

Utilized Rapid API for data cleaning and pre-processing, removing unnecessary characters and ensuring data standardization.
Provided users with the ability to map inputs and apply customizable cleansing rules.

Dynamic Health Score Calculation

Developed a system to calculate health scores for each data point using RAG (Retrieval-Augmented Generation) methodology.
Implemented features to display these scores, reflecting data accuracy and reliability.

Scalable Back-end Infrastructure

Built a robust back-end using Python and Django, ensuring scalability and efficient data processing.
Used PostgreSQL for database management and AWS S3 for secure data storage.

Impacts

& Achievements

The platform enabled seamless data integration, improved cleansing accuracy, and real-time health scoring, significantly enhancing data reliability and trust. Its scalable infrastructure boosted processing efficiency and supported business growth through higher user retention and increased subscriptions.

Enhanced Data Integration and Processing

25%

Improved Data Cleansing and Health Scoring

60%

Operational Efficiency and Scalability

60%

increase in subscription

15%

Whoozbuying user-friendly interface

Unlock seamless data integration and accuracy with DEVtrust. Contact us to explore how we can elevate your data processing needs.