Cloud-native data warehouse in Google Cloud

Executive Snapshot

A leading FinTech SaaS provider partnered with Transcloud to architect its resilient and cost-effective data infrastructure. The goal was to create a serverless, automated, and ML-ready ecosystem capable of processing millions of events while maintaining cost efficiency and operational simplicity.

Key Outcomes:

• Implemented a BigQuery-based data warehouse for real-time analytics.
  
• Streamlined data ingestion for 1M+ events per day with 20% monthly growth.
  
• Achieved centralized monitoring for improved reliability and visibility.
  
• Enhanced scalability, security, and performance through serverless architecture.

The Situation: Scaling Data for FinTech Growth

A leading FinTech SaaS provider partnered with Transcloud to architect its data infrastructure and streamline operations for scalability, analytics, and machine learning. The organization empowers India’s rapidly growing blue-collar and gig workforce through innovative credit-on-tap services, offering near-instant access to funds via smartphone.

However, as the user base expanded, so did the complexity of managing massive and continuously growing data volumes. The existing adhoc setup on AWS—leveraging S3, Athena, and Glue—introduced significant maintenance challenges. Slow processing cycles and inconsistent environments hindered analytics performance, making it difficult to extract reliable insights at scale.

Recognizing that a modern, resilient data platform was essential for downstream ML and analytics, the company turned to Transcloud to re-architect its data foundation on Google Cloud—unlocking real-time insights, faster decisions, and improved customer experiences.

The Solution: Data Platform Modernization on Google Cloud

Transcloud designed and executed a comprehensive modernization strategy, aligning architecture, scalability, and cost-efficiency through serverless and managed Google Cloud services. The choice of going serverless is to keep the cost less initially with Pay-as-you-go model and grow as the data and processing grows, and no infrastructure to manage.

We began with a deep analysis of the client’s data structures, dependencies, and performance bottlenecks to identify opportunities for automation, elasticity, and observability. The roadmap focused on transitioning to Google BigQuery for Cloud Workflows for pipeline orchestration, adopting an ELT-first approach for efficiency.

Key implementations included:

• Migration from AWS to Google Cloud: Consolidated data from S3 and Glue into Cloud Storage and BigQuery for centralized, serverless analytics.
  
• Automated Data Pipelines: Built scalable pipelines with Pub/Sub, Cloud Workflows, and Cloud Functions for continuous data ingestion and transformation.
  
• Database Modernization: Leveraged BigQuery’s compute and query optimization features for rapid insights and cost efficiency.
  
• Unified Monitoring & Observability: Implemented centralized logging, error reporting, and performance metrics through Cloud Logging and Monitoring.
  
• Master Data Management: Standardized and validated datasets to improve data trust, consistency, and accessibility.
  

This transformation introduced a single-pane operational view, reduced manual interventions, and aligned infrastructure costs with usage patterns—eliminating underutilized compute and storage overhead.

The Outcome: Scalable, Cost-Efficient, and Future-Ready

The modernization initiative delivered measurable performance and operational gains:

• 1M+ events processed daily, handling continuous data inflows effortlessly.
  
• 20% month-over-month data growth accommodated without latency or performance degradation.
  
• 100 GB+ data ingestion from multiple sources unified into a single analytics-ready environment.
  
• Accelerated analytics deployment, enabling faster experimentation and ML model iteration.
  
• Reduced operational overhead, thanks to a fully managed, serverless stack.
  

By integrating BigQuery, Cloud Storage, Pub/Sub, and Cloud Workflows, Transcloud helped the client establish a resilient, automated, and future-ready data platform capable of supporting advanced analytics and machine learning workloads—while maintaining transparency, reliability, and cost predictability.

Technology Stack

Google BigQuery | Cloud Storage | Pub/Sub | Cloud Workflows | Cloud Functions | Cloud Scheduler | Cloud Firestore | Cloud IAM | Cloud Logging & Monitoring | Error Reporting

Why Transcloud

The client partnered with Transcloud for our capability to engineer data platforms that scale effortlessly while ensuring performance, reliability, and measurable ROI.

• Data Modernization Expertise: Proven success in designing scalable and automated data pipelines for FinTech environments.
  
• Multi-Cloud Capabilities: Deep understanding of both AWS and Google Cloud architectures for seamless migration.
  
• Serverless-first Approach: Delivered an agile, cost-optimized, and low-maintenance data ecosystem.
  
• Focus on Business Impact: Our architecture empowered faster analytics, improved decision-making, and long-term scalability.
  
• SLA-driven Delivery: Committed to 99% uptime compliance and proactive support for business-critical workloads.
  
• Client-Centric Partnership: Transparent communication, continuous monitoring, and fast resolution cycles ensured long-term reliability and trust.