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GCP Data Pipeline Automation

Project Overview

This project focused on designing and implementing an automated data pipeline using webhooks to track requests and generate KPI reports. The solution connected Wrike's project management platform to Google Cloud Platform services, creating a seamless data flow that provided critical business insights without manual intervention.

Pipeline Architecture

The Challenge

The organization faced several challenges with their existing reporting processes:

  • Manual Data Collection: Team members spent hours each week manually extracting data
  • Inconsistent Reporting: Lack of standardization led to varying interpretations of results
  • Delayed Insights: Decision-makers received information too late to take timely action
  • Limited Visibility: No real-time understanding of request pipeline and team capacity

Solution Architecture

I designed a comprehensive solution using the following components:

Data Flow Architecture

  1. Data Source: Wrike project management system
  2. Data Ingestion: Webhooks triggered on task/request status changes
  3. Data Processing: GCP Cloud Functions to process and transform incoming data
  4. Data Storage: BigQuery tables for structured storage and analysis
  5. Data Transformation: Dataform for SQL-based data modeling and transformations
  6. Data Visualization: Looker Studio dashboards for stakeholder reporting

Technical Implementation Details

  • Set up webhook listeners for real-time data capture from Wrike
  • Developed Cloud Functions to process incoming webhook data and store it in BigQuery
  • Created a robust data transformation layer using Dataform
  • Implemented data quality checks to ensure reliability
  • Designed interactive dashboards in Looker Studio for various stakeholders

Results and Impact

The implementation of this automated data pipeline delivered significant business value:

  • Efficiency Gain: Eliminated approximately 15 hours of manual data collection work weekly
  • Improved Decision-Making: Provided near real-time insights into request pipelines
  • Enhanced Planning: Improved team capacity planning with reliable data
  • KPI Tracking: Established measurable, consistent KPIs for business performance
  • Scalability: Created a foundation for expanding automation to other business processes

Technical Skills Demonstrated

  • Cloud Architecture: Designed scalable GCP-based data processing pipelines
  • Data Engineering: Built robust data models and transformation processes
  • SQL Development: Created complex SQL transformations in Dataform
  • API Integration: Connected systems using webhook technology
  • Data Visualization: Designed intuitive dashboards for various user types

Lessons Learned

  • The importance of robust error handling in webhook-based systems
  • Strategies for ensuring data consistency across system integrations
  • Balancing real-time data needs with processing costs
  • Effective ways to present technical solutions to non-technical stakeholders

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