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Healthcare

Enhancing Data Processing Efficiency through Cloud-Based Solutions

Visualization of automated data ingestion and processing through cloud services

Focus Areas

Cloud Architecture

Data Engineering

Scalability & Performance Optimization

Dynamic resource scaling in a cloud environment, supporting efficient and cost-effective data processing.

Business Problem

A fast-growing media analytics firm was struggling with inefficient data processing pipelines and increasing infrastructure costs. Their on-premise systems couldn’t scale with the rising volume of streaming and social media data, leading to slow batch processing, delays in client deliverables, and resource bottlenecks. The company needed a modern cloud-based solution to improve processing speed, reduce costs, and support real-time data workflows.

Key challenges:

  • Performance Bottlenecks: On-premise data pipelines couldn’t meet SLAs for daily analytics reports and client dashboards.

  • Limited Scalability: Infrastructure could not scale elastically to handle peak workloads or expand to new data sources.

  • High Operational Overhead: Manual provisioning, maintenance, and patching consumed valuable engineering time.

  • Cost Inefficiency: Overprovisioned infrastructure and idle compute resources led to high fixed costs and low resource utilization.

  • Data Silos: Fragmented storage and processing environments hampered unified analytics and slowed experimentation.

The Approach

Curate partnered with the firm to design and deploy a fully managed, scalable cloud data platform that automated processing, optimized performance, and enabled real-time data access. The initiative streamlined ETL workflows, introduced modern orchestration, and reduced compute overhead—significantly improving time-to-insight.

Key components of the solution:

  1. Discovery and Requirements Gathering: Collaborated with data, engineering, and product teams to identify core inefficiencies and define success metrics. Key priorities included:

    • Modernize data pipeline architecture for cloud-native performance

    • Migrate legacy workloads to cost-effective cloud services

    • Enable parallel and real-time processing

    • Reduce infrastructure management overhead

  2. Cloud Data Platform Implementation:

    • Cloud Architecture Design: Built a modular architecture using AWS (S3, Lambda, EMR, Redshift) with Terraform for infrastructure-as-code.

    • Data Lake Creation: Centralized all structured and unstructured data in Amazon S3 with metadata tagging and lifecycle policies.

    • ETL Workflow Modernization: Replaced batch processing with serverless and container-based pipelines using AWS Glue and Fargate.

    • Real-Time Streaming: Integrated Amazon Kinesis for ingestion and transformation of high-velocity data sources.

    • Scalable Warehousing: Migrated data marts to Redshift and BigQuery for fast analytics queries and dashboarding.

    • Monitoring & Alerting: Enabled CloudWatch and Prometheus/Grafana dashboards to track job performance and optimize resource allocation.

  3. Process Optimization & Automation:

    • Workflow Orchestration: Implemented Apache Airflow to coordinate end-to-end pipelines and trigger downstream analytics jobs.

    • Auto-Scaling & Scheduling: Configured compute jobs to scale based on workload volume, reducing idle time and controlling spend.

    • Data Quality Checks: Added validation layers and anomaly detection for improved trust in downstream analytics.

    • Access & Governance: Integrated IAM roles and audit logs to ensure secure, role-based data access.

  4. Stakeholder Engagement & Change Management:

    • Cross-Functional Planning: Engaged data analysts, engineers, and business leads in sprint planning and prioritization.

    • Training Sessions: Delivered workshops to upskill staff on new tooling and best practices in cloud-native development.

    • Documentation & Support: Created detailed runbooks, cost dashboards, and support escalation paths to ensure adoption.

    • Performance Reviews: Conducted regular performance benchmarking and cost audits post-migration.

Business Outcomes

Faster Data Processing and Delivery


Report generation time was reduced from 8–10 hours to under 1 hour, significantly improving service delivery timelines.

Scalable Infrastructure


Elastic scaling enabled the platform to handle 5x data volume during peak campaigns without service degradation.

Cost Optimization


Pay-as-you-go compute and storage saved 37% in annual infrastructure costs compared to the previous setup.

Improved Data Accessibility


A unified cloud-based data platform allowed business teams to self-serve insights faster and reduced dependencies on engineering.

Sample KPIs

Here’s a quick summary of the kinds of KPI’s and goals teams were working towards**:

Metric Before After Improvement
Data pipeline processing time 8-10 hours 1 hour 90% reduction
Data scalability threshold 2 TB/day 10+ TB/day 5x increase
Monthly infrastructure cost $42,000 $26,500 37% savings
Job failure rate 12/month 2/month 83% reduction
Analyst access latency (avg.) 45 min 10 min 4.5x faster access
**Disclaimer: The set of KPI’s are for illustration only and do not reference any specific client data or actual results – they have been modified and anonymized to protect confidentiality and avoid disclosing client data.

Customer Value

Accelerated Time-to-Insight


Teams could access fresh data quickly to make real-time business decisions.

Operational Efficiency


Automated pipelines freed up engineers to focus on innovation rather than maintenance.

Sample Skills of Resources

  • Cloud Architects: Designed resilient, secure, and scalable infrastructure in AWS and GCP.

  • Data Engineers: Developed parallelized ETL workflows and optimized data lake performance.

  • DevOps Specialists: Automated infrastructure deployment and configured CI/CD for pipelines.

  • Analytics Engineers: Ensured data model integrity and performance of downstream reporting tools.

  • Training & Enablement Leads: Facilitated platform onboarding and adoption across teams.

Tools & Technologies

  • Cloud Platforms: AWS (S3, Glue, Lambda, EMR, Kinesis), GCP (BigQuery, Dataflow)

  • Workflow Orchestration: Apache Airflow, AWS Step Functions

  • ETL & Streaming: Python, Spark, dbt, Kafka, Fargate

  • Data Warehousing: Redshift, BigQuery

  • Monitoring & Governance: CloudWatch, Prometheus, IAM, Grafana

  • Collaboration & Knowledge Sharing: Confluence, Notion, Slack

Process illustration of secure data migration from on-premise servers to cloud infrastructure for enhanced processing efficiency.

Conclusion

Curate’s cloud-based data transformation strategy empowered the media analytics firm to dramatically improve processing efficiency, reduce operational costs, and deliver real-time insights at scale. By leveraging scalable infrastructure, modern orchestration, and secure data practices, the company shifted from slow, reactive reporting to fast, predictive decision-making—enabling both business growth and technical innovation.

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