Accelerating Healthcare Innovation Through Advanced Data Engineering and Platform Modernization

The client encountered three interconnected challenges that were limiting their ability to scale and compete effectively:

These challenges were deeply interconnected. Slow analytics pipelines delayed pricing insights, which limited the ability to build reliable predictive models. At the same time, the lack of a standardized microservices framework slowed the deployment of new data and ML services needed to operationalize these insights. Solving these problems independently would have delivered incremental gains – but addressing them together enabled the client to unlock a faster, intelligence-driven platform for pricing and product innovation.

Ciklum deployed specialized data engineering and platform engineering teams to address each challenge through deep technical optimization and systematic modernization.

Multi-Faceted Query Refactoring

Our data team performed comprehensive optimization of the Persistent Derived Table (PDT) powering the critical pricing report, deconstructing and rebuilding the query from the ground up.

Technical Implementation:

• Data Skewness Mitigation: Redesigned the query to handle large volumes of NULL values in join columns – a primary cause of data skew. Applied hashing functions to distribute processing load evenly across Redshift warehouse nodes.
• Query Logic Consolidation: Consolidated multiple Common Table Expressions (CTEs) that repeatedly scanned the same large tables into single, efficient CTEs. Converted performance-draining subqueries to optimized CTEs.
• Logical Pruning: Removed superfluous joins to tables whose columns weren’t used in final output. Implemented consistent, rolling 13-month date range across all tables to prevent unnecessary data processing.
• Rigorous QA: Implemented staged validation at every optimization step, comparing record counts and key metrics to ensure 100% accuracy against original query results.

End-to-End MLOps Framework

We developed a production-grade machine learning solution centered on price elasticity modeling – directly answering the core business question of how pricing changes impact demand and revenue.

Technical Implementation:

• Elasticity Modeling with XGBoost: Built predictive models that forecast the impact of different pricing scenarios on customer claims and profitability, providing immediately actionable insights beyond simple prediction.
• Robust MLOps Infrastructure: Implemented continuous monitoring, experiment tracking, model versioning, and automated retraining using Databricks and MLFlow to combat data drift and ensure long-term accuracy.
• End-to-End ML Lifecycle: Integrated data from Redshift, performed feature engineering in Databricks, tracked the entire process with MLFlow, and delivered model outputs to business teams via Looker dashboards for real-time insights.
• Proactive Drift Management: Built resilience into the solution to handle feature instability and changing market dynamics without manual intervention.

Golden Path Development Framework

We standardized and automated the entire path from idea to running service, creating an opinionated but extensible ecosystem for microservices development.

Technical Implementation:

Ecosystem Components:

1. Shared Microservice Library: Versioned building blocks (configuration, logging, health checks, error handling, metrics) with opinionated defaults to reduce stack sprawl
2. Production-Ready Template: Scaffold with common adapters (database, transport layer) and environment wiring pre-configured
3. Standardized CI/CD Pipelines: Unified pipeline definition across services for build, test, security checks, image build/sign, and deployment
4. Standardized Methodologies: Shared guidance for service boundaries, testing levels, code review, documentation, and release practices

Architecture & Automation:

• Repository scaffolding from template with pre-wired layout and documentation starters
• Automated Kubernetes baseline generation (namespaces, manifests, deployments, services, configs)
• Automated database resource and secrets provisioning through template adapters
• Auto-provisioned CI/CD with standard stages and security gates
• Service catalog registration with enforced delivery signals via issue/PR templates

Ciklum’s integrated approach delivered significant improvements across the client’s analytics, machine learning, and platform engineering ecosystem. By optimizing the analytics pipeline, introducing predictive pricing intelligence, and standardizing microservices development, the client accelerated both insight generation and service deployment. Faster analytics enabled near real-time pricing insights, while the Golden Path development framework dramatically reduced engineering friction, enabling teams to ship new services and iterate on features at a much faster pace.