Maestro Logic was engaged by a Tier-1 investment bank in London to migrate a complex, near real-time on-premise data warehouse to Google Cloud Platform (GCP). The legacy architecture processed high-volume trade data through a combination of TIBCO Enterprise Service Bus and Apache Kafka, feeding downstream systems via Apache Ignite and SQL Server Service Broker queues. This platform supported a range of front-end applications and Qlik-based reporting tools, but was increasingly constrained by scalability limits, operational overhead, and the need for more flexible, cloud-native data processing capabilities.
Maestro Logic designed and delivered a modern GCP-native data platform, re-architecting the ingestion, processing, and analytics layers to leverage fully managed, horizontally scalable services. Trade data ingestion pipelines were migrated to Google Pub/Sub, providing a resilient and scalable messaging backbone. Stream and batch processing workflows were orchestrated using Cloud Composer (Airflow), enabling improved pipeline visibility, scheduling, and dependency management across complex data flows.
At the data storage and analytics layer, workloads were strategically distributed across Google Cloud services. Google BigQuery was implemented as the primary analytical engine for large-scale querying and reporting, while Cloud Spanner was introduced to support globally consistent, high-throughput transactional workloads. Bigtable was leveraged for low-latency, high-performance ELT lookup use cases, significantly improving enrichment and reference data access during processing.
The application and consumption layers were also modernised as part of the migration. Existing React-based front-end applications were rehosted and refactored to integrate with cloud-native services, including MemoryStore for low-latency caching and session management. Legacy Qlik reporting solutions were transitioned to Looker, enabling a more flexible and governed semantic layer for enterprise analytics.
Infrastructure and deployment processes were standardised using Terraform, enabling fully automated, version-controlled infrastructure provisioning and CI/CD pipelines across environments. This approach improved consistency, reduced manual intervention, and accelerated delivery cycles.
The migration resulted in a highly scalable, resilient, and cloud-native data platform capable of supporting near real-time analytics across trading and risk domains. By transitioning from a tightly coupled on-premise stack to GCP managed services, the bank significantly improved system elasticity, reduced operational complexity, and established a modern foundation for data-driven innovation and advanced analytics.