Cloud-native data warehouse in Google Cloud

The Challenge

The company caters to India’s burgeoning blue-collar workforce and offers innovative financial solutions tailored to the needs of gig and contract workers. Their smartphone-linked credit-on-tap service, facilitated by aggregatory and employers, revolutionizes access to funds, enabling users to secure and spend funds within three minutes. This streamlined approach enhances financial inclusivity and underscores the company’s obligation to empower individuals in the rapidly evolving gig economy. However, there is one significant hurdle to this novel strategy: the vast amount of data collected by the SaaS application is a challenge.

Nonetheless, the company recognizes that overcoming this challenge is imperative for building a robust data platform. Such a platform is essential for facilitating downstream machine learning and analytics functions. It plays a critical role in providing trustworthy insights and enabling swift and informed business decisions. Ultimately, it leads to creating a better experience for customers. To address this challenge head-on, the company is committed to leveraging its expertise and resources to construct a resilient data infrastructure. By investing in a comprehensive data platform, the company aims to unlock the full potential of its data assets. This initiative strengthens our analytics capabilities and shows our commitment to innovation and delivering great value to customers.

Modernizing Data Architecture: Enhance scalability and ease of management.

Identifying the pain points: 

The existing data architecture within AWS has revealed limitations in scalability and maintenance. A clear operational challenge for the team! As the user base expanded and data volumes surged, the dependence on AWS services like S3, Athena, and Glue became burdensome, impeding smooth data processing and utilization.

Strategic Shift to Serverless and Managed Services: 

To address these challenges, the team is proactively pursuing a strategic shift towards serverless and managed services, particularly Google BigQuery.By leveraging these modern technologies, the aim is to streamline the data pipeline and warehouse operations. The intent is to reduce the operational efforts and enhance sustainability.

Key Action Points:

Evaluate the suitability of Google BigQuery for specific use cases within the data architecture.
Plan and execute the migration of relevant data processing and storage tasks to serverless and managed services.
Implement best practices for optimizing data processing and consumption workflows, ensuring scalability and efficiency.
Continuously monitor and refine the modernized data architecture to adapt to evolving business needs and technological advancements.

Key Highlights of Data Architecture Transformation:

Scalable Event Processing: 
Managing over 1 million events daily, with a growth rate of 20% month-over-month.Underscores the robustness and scalability of the revamped data architecture. This capability ensures seamless handling of escalating data volumes, laying a strong foundation for future expansion.

Diverse Data Sources:
Ingesting and processing more than 100 GBs of data from three distinct sources showcases the versatility and adaptability of the data infrastructure. This multi-source integration enhances data richness, providing comprehensive insights for analytics and machine learning initiatives.

Accelerated Analytics Solutions Deployment: 
By streamlining processes and standardizing procedures, the revamped architecture expedites the implementation of new analytics solutions. This agility empowers the organization to swiftly adapt to changing business requirements and capitalize on emerging opportunities.

Reliable Master Data Management: 
The transformation initiative prioritizes the reliability and integrity of master data. Through streamlined and standardized processes, the architecture ensures the consistency and accuracy of critical data assets. It bolsters decision-making capabilities and fostering trust in data-driven insights.

Cost-Efficient Infrastructure: 
Embracing serverless Google Cloud Platform (GCP) services aligns system costs more closely with actual usage. This optimizes expenditure and enhances cost-effectiveness. This strategic adoption of serverless technology ensures scalability and flexibility while maximizing resource utilization efficiency.

Operational Ease: 
Centralized integration of logging, monitoring, and error reporting simplifies operations. This acts like a repository, enhancing visibility and control over the data infrastructure. This facilitates proactive issue resolution, and analysis workflows, and ensures uninterrupted data processing.

Demonstrating Technical Excellence: 

In our quest for technical excellence, we thoroughly reviewed the current data setup, carefully examining data structures, processing methods, and overall needs. Through meticulous analysis, we identified opportunities to enhance scalability, maintainability, and overall performance. 

Our insights enabled us to architect a robust and maintainable data warehouse leveraging the power of Google BigQuery. This strategic move was pivotal in aligning our data infrastructure with the evolving needs of our product and business landscape.Our main focus was on creating and putting in place efficient data pipelines for all our data sources. This ensured that data flowed seamlessly and remained intact throughout our system.Leveraging the agility and scalability of serverless Cloud Workflows, we orchestrated these pipelines with precision and efficiency

Furthermore, we embraced an Extract-Load-Transform (ELT) paradigm, storing raw data and leveraging the processing capabilities of BigQuery.This approach not only optimized data processing workflows but also facilitated rapid insights generation for informed decision-making. To ensure operational excellence, we established a comprehensive monitoring framework, providing a single window view of critical metrics such as resource utilization, latency, performance, and service availability. 

This monitoring system enabled the timely detection of anomalies, facilitating prompt resolution and minimizing potential disruptions.Our architecture integrates a range of Google Cloud services, such as BigQuery, Cloud Storage, Pub/Sub, Cloud Workflows, Cloud Scheduler, Cloud Functions, Cloud Firestore, Data Transfer Service, Cloud IAM, Logging, Monitoring, and Error Reporting. Our thorough use of cloud services demonstrates our focus on employing advanced technology to innovate and bring value to our stakeholders.

In summary, our ongoing efforts to improve our data infrastructure demonstrate our commitment to meeting the evolving needs of our products and business environment. We strive to maintain high standards of performance and reliability without overtly celebrating our achievements.