How Experian, a leading credit bureau processing data for 8 million financial consumers, optimized over 27 machine-hours of daily distributed processing and established a rigorous technical governance model.
Experian, a leading credit bureau serving 8 million financial consumers, sought to optimize and strengthen its data processing platform running on Amazon EMR with Apache Spark and Airflow. Partnering with AWS Partner MacondoTek, Experian achieved a 31% reduction in processing times and 15% savings in infrastructure costs, while stabilizing critical pipelines that handle more than 27 machine-hours of daily distributed processing. The engagement also delivered a technical governance model based on ITIL, transforming how the platform is operated and maintained.
Experian is part of a global conglomerate present in 37 countries with over 16,000 employees. Its data platform processes daily credit information that serves approximately 8 million financial consumers, offering credit history queries, personalized financial offers, and debt normalization services. The platform is the critical engine behind all of Experian's credit bureau products.
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.
As part of its technology evolution strategy, Experian sought to optimize and strengthen its data processing platform running on Amazon EMR with Apache Spark and Apache Airflow. The pipelines process daily credit information that feeds all of the credit bureau’s products, making performance and stability directly tied to business outcomes.
The platform faced several strategic improvement opportunities:
Experian needed a partner that could deliver measurable technical improvements while simultaneously transforming the operational model — balancing performance, cost, and stability.
MacondoTek deployed a specialized Data Platform Team that worked across three simultaneous fronts: performance optimization, pipeline stabilization, and technical governance. The methodology was rigorous, with over 100 controlled tests executed in the development environment before any change was promoted to production.
Performance Optimization. MacondoTek tuned Apache Spark parameters to improve parallelism and concurrency, evolved the infrastructure to next-generation r7gd instances with better CPU and memory bandwidth, and optimized the write efficiency of Apache HBase components. A key architectural change was decoupling Apache Airflow processes from a shared cluster into independent clusters sized specifically to each process’s workload — eliminating resource contention and enabling more predictable performance.
Pipeline Stabilization. The team identified and resolved the primary root causes of pipeline instability, establishing per-stage checkpoints for data integrity validation. A proof of concept was developed to democratize pipeline visibility — surfacing performance attributes and pre-load data quality rules to enable earlier detection of issues.
Technical Governance. MacondoTek implemented ITIL-based governance with structured support levels: Experian’s operational team as Level 1 and MacondoTek’s platform team as Level 2, with defined escalation rules and SLAs. A cost estimation model was also developed to project infrastructure cost behavior against data volume growth, enabling proactive capacity planning.
The optimization delivered a 31% reduction in processing time for the main data transformation pipeline — a platform that executes more than 27 machine-hours of daily distributed processing across large-scale EMR clusters with r7gd instances. Optimized infrastructure sizing and cluster configuration produced a 15% reduction in infrastructure costs.
The main pipeline achieved controlled operational stability, with 5 processes optimized incrementally and verifiably, and the primary root causes of instability identified and resolved. MacondoTek’s rigorous methodology of 100+ controlled tests in the development environment ensured that every change was validated before promotion to production. The ITIL-based technical governance model, with structured support levels and defined escalation rules, transformed how the platform team operates — and the complete technical documentation with incident traceability establishes a reusable knowledge base for the platform’s continuity and evolution.
Specialized expertise in Amazon EMR, Apache Spark tuning, and distributed data pipeline architecture — enabling precise, evidence-based optimization decisions with measurable outcomes.
Deep experience supporting financial services organizations where data reliability, processing stability, and auditability are critical platform requirements.
Over 100 controlled tests in development before any production change, ensuring that every optimization is validated and risk is minimized before deployment.
MacondoTek established a structured ITIL-based support model with defined escalation rules, cost visibility, and full documentation — delivering a lasting operational improvement, not just a technical fix.
