How a global financial services company's North America BI team modernized its Enterprise Data Lake with isolated compute clusters for ingestion, modeling, and analytics — enabling AI initiatives and improving performance and cost visibility across business units.
A global financial services leader partnered with MacondoTek to expand and modernize its Enterprise Data Lake (EDL) platform to support new analytics and AI initiatives across the organization. The North America BI team required an architecture capable of supporting conversational reporting and advanced analytics workloads while improving performance and cost visibility across business units. MacondoTek designed and implemented a multi-cluster Amazon Redshift architecture that separated data ingestion, modeling, and consumption workloads into distinct, independently scalable layers — enabling the client to scale its analytics platform, isolate compute resources per business unit, establish cost attribution, and create a foundation for AI-driven analytics.
The client is a global leader in business payments and financial services. Its North America BI team operates a large Enterprise Data Lake (EDL) that consolidates data from across the organization to power reporting, analytics, and increasingly AI-driven insights. Managing a growing volume of data and analytics consumers across multiple business units requires scalable, governed data architecture.
MacondoTek is a cloud consulting and professional services firm headquartered in Atlanta, USA, with nearshore delivery teams across Latin America, specializing in cloud architecture, data platforms, and application modernization on AWS. As an AWS Advanced Tier Partner with Service Delivery Program designations for Amazon RDS and Amazon DynamoDB, MacondoTek supports organizations across the full cloud lifecycle, from migration through cloud-native development to 24/7 managed operations. Its proprietary MTK CloudOpps platform powers FinOps and cost governance engagements with enterprise-wide visibility across large multi-account AWS environments, including deep experience serving financial services organizations that must balance reliability, security, and compliance with cost optimization.
The client’s existing data platform was built around a centralized Redshift cluster that handled multiple responsibilities simultaneously — data ingestion, transformations, and analytics queries — within a single shared compute environment. As the platform grew to serve multiple business units, this architecture created compounding challenges.
Increasing analytics workloads from multiple business units overwhelmed the shared compute environment, while new AI and conversational reporting initiatives demanded dedicated, scalable query infrastructure. Data engineering pipelines and BI analytics workloads contended for the same resources, degrading performance for both. Business unit teams had no visibility into their individual compute costs, making it impossible to attribute spend or hold teams accountable. The tight coupling between ingestion and analytics workloads also created deployment and operational dependencies that slowed iteration.
The client needed a scalable architecture that could separate compute workloads across distinct layers while maintaining centralized enterprise data governance and a single source of truth.
MacondoTek designed a multi-cluster Amazon Redshift architecture introducing three distinct, independently managed layers within the Enterprise Data Lake — separating ingestion, modeling, and consumption workloads across dedicated clusters.
EDL Hub (Data Ingestion Layer). The existing Redshift cluster was restructured into the central data collection hub, responsible for integrating data from multiple enterprise systems, running ingestion pipelines using AWS Glue and AWS DMS, and storing enterprise datasets as the authoritative source of record.
Producer Cluster (Data Modeling Layer). A new Redshift cluster was implemented to handle data modeling, transformations, and curation in preparation for analytics workloads. The Producer cluster accesses datasets from the EDL Hub using Amazon Redshift Data Sharing — enabling clean separation of compute while maintaining a single source of truth across all layers.
Consumer Clusters (Analytics Layer). Additional clusters using Amazon Redshift Serverless were created to isolate compute resources for each business unit’s analytics workloads — enabling independent scaling, improving query performance, and enabling granular cost attribution per BI consumer.
MacondoTek also delivered the full platform foundation: a multi-AWS account solution with hub, consumer, and shared services accounts for cost and governance isolation; integrated networking with isolated VPCs and AWS Transit Gateway; multiple environments supporting the full SDLC (dev/QA/staging/prod); and CI/CD pipelines implementing Infrastructure as Code across all accounts, including DDL mappings and AWS Glue job deployments.
The new multi-cluster architecture delivered major improvements in scalability, performance, and operational governance across the client’s Enterprise Data Lake. Data engineering and BI analytics workloads are now fully decoupled — each team operates on dedicated, independently scalable compute without contention. Per-business-unit compute isolation through Redshift Serverless consumer clusters enables accurate cost attribution, giving leadership clear visibility into analytics spending by team. MacondoTek delivered a production-grade SDLC with full CI/CD automation across all environments, enabling the data engineering team to iterate rapidly. The architecture is positioned to support conversational AI and generative analytics as the client’s data initiatives continue to grow.
MacondoTek designed a multi-cluster Redshift architecture that cleanly separates ingestion, modeling, and analytics workloads — enabling each layer to scale independently based on its own performance and cost profile.
Extensive experience delivering secure, scalable cloud platforms for financial services organizations where governance, security, and auditability are core architectural requirements.
MacondoTek implemented the full platform foundation — multi-account architecture, networking, SDLC environments, and CI/CD — not just the data layer, delivering a production-ready platform from day one.
MacondoTek engineers worked side-by-side with the client's internal BI and engineering teams, ensuring alignment with internal governance, security requirements, and full knowledge transfer.
