GCP-Powered Data Engineering Solution

The client wanted to develop pipelines to manage unstructured data sourced from various channels such as Instagram, eCommerce sites, fashion magazines, store surveys, and third-party providers. The primary objective was to create a centralized storage system for this data to facilitate visualization and analytics.

• Data integration challenges: Dealing with diverse data formats and sources required robust extraction and transformation methods.
• Quality assurance and real-time handling: Ensuring data accuracy across sources while managing real-time data requires stringent validation and continuous monitoring mechanisms.
• Scalability, security, and compliance: Building scalable infrastructure, implementing strong security measures, and adhering to data privacy regulations are crucial for data management.
• Analytics compatibility and maintenance: Enabling seamless integration with analytics tools and establishing ongoing monitoring and maintenance protocols for reliable data pipelines.

• Centralized Data Management: Consolidation of unstructured data from diverse sources into Firestore enables easier access and management.
• Enhanced Security and Compliance: Robust measures such as face masking API, encryption, and access controls ensure compliance with data regulations and bolsters data security.
• Improved Analytics and Decision-making: Organized data infrastructure enables better visualization and analysis, fostering informed strategic decisions.
• Efficient Data Handling: Streamlined extraction, transformation, and loading processes through GCP services and custom pipelines enhance operational speed and agility.
• Scalability and Performance: GKE clusters and performance enhancements ensure scalability and optimized efficiency even with increased data volumes.
• Controlled Data Lifecycle Management: DAGs for data retraction and deletion align with regulatory guidelines, facilitating effective data lifecycle management.
• Collaboration and Access Control: Integration with Azure AD enables controlled access for authorized users, promoting collaboration while maintaining security.
• Actionable Insights: Export jobs allow easy retrieval of data for visualization and analytics, ensuring data-driven decision-making and business growth.

We developed a data engineering solution to manage unstructured data from third-party sources. Leveraging Google Cloud Platform (GCP) services, we built pipelines for extracting, transforming, and loading data into Firestore, our centralized cloud storage database.

To protect Personally Identifiable Information (PII), we created a face masking API integrated into the data transformation process. This API ensured the security of PII data during processing. Additionally, robust data protection measures, including encryption and regular database backups, were implemented to safeguard sensitive information.

Continuous monitoring and feedback ensured the reliability, security, and compliance of the data pipelines and protection mechanisms.

• Google Cloud Platform
• GCS bucket
• GKE cluster
• Cloud Composer
• Pub/Sub notification
• Dead Letter Queue
• Secret Manager
• Cloud Run
• API Gateway
• Cloud Firestore
• VPC
• Cloud Logging
• Alert Policy
• Monitoring Dashboard
• Secret Manager
• Google Drive API
• Google Cloud Vision API
• Terraform
• Python 3.11
• Azure AD