Client Background

• Client: A leading wellness firm in the USA, EU
• Industry Type: Healthcare
• Products & Services: Wellness products
• Organization Size: 2000+

The Problem

The client needed a robust and scalable data pipeline to extract, integrate, and analyze data from multiple sources, including Shopify, Recharge, GetFeedback, Salesforce, and SimplyBook. The existing data infrastructure was fragmented, leading to inefficiencies in reporting and analytics.

Our Solution

We developed a data pipeline that seamlessly extracts data from various sources and integrates it into a unified data warehouse. Initially, we implemented the solution using Y42 v1 and v2, creating orchestrations and automated data workflows. As the client transitioned to Kleene.ai, we migrated the data pipeline to this new platform to ensure continued efficiency and scalability. Additionally, Python scripts were used for integrating data from SimplyBook and GetFeedback, deployed on Google Cloud Functions and scheduled to run every morning at 7 AM.

Solution Architecture

Deliverables

• Fully automated data pipeline
• Migration from Y42 to Kleene.ai
• Data warehouse setup in BigQuery
• Integrated reporting dashboards in Looker Studio and Google Sheets
• Python-based integrations for SimplyBook and GetFeedback
• Documentation for pipeline maintenance

Tools used

• Y42 v1 & v2
• Kleene.ai
• Looker Studio
• Google Sheets
• Google Cloud Functions
• Google Bigquery

Language/techniques used

• SQL
• Python
• ETL (Extract, Transform, Load) methodologies
• API integrations

Models used

• Data orchestration models
• Data transformation models

Skills used

• Data Engineering
• ETL development
• Cloud Data Warehousing
• Dashboarding and Visualization
• API Integration

Databases used

• Google BigQuery

Web Cloud Servers used

• Google Cloud Platform (GCP)

What are the technical Challenges Faced during Project Execution

1. Integration of multiple data sources with varying APIs and formats.
2. Transitioning from Y42 to Kleene.ai without disrupting ongoing operations.
3. Ensuring data consistency and accuracy during migration.
4. Deploying and scheduling Python scripts on Google Cloud Functions.

How the Technical Challenges were Solved

1. Developed API-based connectors for seamless data extraction.
2. Created a parallel processing environment to test Kleene.ai workflows before full migration.
3. Implemented data validation checks and automated reconciliation reports to ensure data integrity.
4. Used Google Cloud Scheduler to automate Python script execution every morning at 7 AM.

Business Impact

• Improved data processing efficiency and reduced manual intervention.
• Faster reporting with real-time data availability.
• Scalable data pipeline supporting future growth.
• Enhanced decision-making through reliable analytics and insights.

Project Snapshots