Maestro Logic was engaged by one of the UK’s largest investment banks in London to design and deliver a high-performance, real-time data platform to support front-office and risk analytics. The existing data estate relied heavily on traditional batch-oriented processing, which limited the timeliness and scalability of critical reporting and downstream systems. The objective was to transition to a streaming-first architecture capable of handling high-velocity data ingestion, low-latency processing, and scalable analytical workloads.
Maestro Logic architected and implemented a distributed streaming data warehouse leveraging Apache Kafka and Apache Flink for real-time data ingestion and stream processing. A custom Service Broker Framework was designed to orchestrate ETL workflows, enabling reliable, asynchronous processing and decoupling of upstream and downstream systems. This framework also supported high-throughput ingestion pipelines and enabled write-behind patterns and event-driven cache synchronisation with Apache Ignite, significantly improving system responsiveness and reducing database contention.
The platform integrated multiple data storage and serving layers, including SQL Server, Apache Ignite for in-memory distributed caching, and SSAS Tabular for analytical modelling. Advanced database engineering techniques were introduced, including columnstore indexing for large-scale analytical queries, memory-optimised tables for low-latency transactional workloads, and temporal tables to support auditability and historical data tracking. A bespoke Partition Management Framework was also developed to automate sliding window partitioning and data compression, ensuring efficient data lifecycle management at scale.
In parallel, Maestro Logic led the adoption of modern engineering practices within the SQL Server development team. Declarative database development was introduced using SSDT, alongside test-driven development (TDD) with tSQLt to improve code quality and reliability. A fully automated CI/CD pipeline was implemented using TeamCity and Octopus Deploy, enabling consistent, repeatable database deployments across environments and significantly reducing release risk.
To support operational visibility and business insights, real-time monitoring and analytics dashboards were developed using React and D3, providing stakeholders with actionable insights into data flows, system performance, and key business metrics.
The resulting platform delivered a step-change in data processing capability, enabling the bank to process high-volume streaming data with low latency while maintaining strong consistency and governance. The modernised architecture reduced operational overhead, improved system scalability, and established a robust foundation for real-time analytics and future data-driven innovation.