About the Customer
The client is a leading American fast food brand known for high-quality ingredients, operational excellence, and a distinctive commitment to customer service. Emphasizing innovation and data-driven solutions, they continually enhance efficiency and drive sustainable growth while delivering a consistent, top-tier experience nationwide. Their culture of service, community engagement, and empowering team members makes them a prominent force in the quick-service restaurant industry.
The Customer's Challenge
With rapid business growth and digital innovation, the organization faced the challenge of efficiently scaling and supporting a complex, expanding AWS-based data platform. Their environment grew from supporting 5 data products in 2021 to over 140 in 2025, spanning multiple AWS accounts and technologies and serving more than 1,000 active users.
This fast proliferation of data products, pipelines, and environments introduced significant operational complexity and risk. Without a robust support and optimization strategy, the client risked increased incident response times, rising IT costs, operational inefficiency, and reduced data platform reliability and scalability—potentially impacting their renowned innovation and customer experience across their network of quick-service restaurants.
phData's Solution
phData provided comprehensive L1-L3 support for the client’s entire AWS-based data platform, evolving from initial operational support in 2021 to a scalable, mature managed service. The solution covered end-to-end management and engineering for Amazon Redshift (including serverless and multi-tenant clusters), Amazon Aurora, AWS Glue, and AWS Managed Workflows for Apache Airflow (MWAA).Â
phData also supported data pipeline frameworks such as Databricks, dbt, and Airflow, all orchestrated using infrastructure as code via Terraform and CloudFormation.
Key AWS services and delivery included:
- Amazon Redshift: Multi-tenant and serverless clusters for analytics workloads, optimized through proactive tuning and cost management
- Amazon Aurora: Managed database support for transactional workloads.
- AWS Glue: ETL pipeline management.
- AWS MWAA: Managed orchestration for complex data workflows, with autoscaling and environment cleanup for cost efficiency.
- Monitoring and Observability: AWS CloudWatch, plus integrations with Datadog, Grafana, and Anomalo for comprehensive alerting and incident detection.
Support was delivered through a layered, 24×7 incident response model (L1-L3), covering both pre- and post-migration implementation phases. phData enforced unified authentication and RBAC, automated administrative/database tuning, and offered ongoing assistance in engineering, root-cause analysis, and migration of legacy pipelines (e.g., from legacy DAGs to dbt and troubleshooting SageMaker model deployments).Â
The engagement included FinOps-focused cost optimization and innovations such as scheduled workflows to keep Redshift datasets current, Banyan-enabled VPC endpoints for secure networking, and custom monitoring solutions.
phData actively managed and scaled a proprietary MLOps framework on AWS, supporting over 1.7 million model training runs per week and generating more than one billion forecasts every four days. The support model was continuously improved with structured post-incident reviews, Runbook enhancements, and team training, dramatically reducing escalation rates and improving efficiency.
Results & Benefits
phData’s managed AWS solution enabled the client to seamlessly scale data platform operations from 5 to over 140 data products and anticipate support for 160+ by the end of 2025—without a proportional increase in costs.Â
Over 11,000 production alerts were reliably triaged during the annual triage, while proactive database engineering and operational improvements delivered tangible savings and efficiency gains.
Key metrics and benefits include:
- Escalation rates from L1/L2 to L3 reduced from 20% in 2023 to below 8% in 2025, thanks to targeted process and knowledge improvements.
- Average onboarding cost per new data product dropped from $2,500 in 2023 to $550 in 2025.
- Monthly support cost per product decreased from $400 to $200.
- Autoscaling MWAA reduced resource contention failures and contributed to $32,000 in annual AWS cost savings.
- Redshift cluster optimization resulted in over $250,000 in estimated annual savings.
- Pipeline throughput improved by more than 4x; daily automated MLOps operations enabled innovation on advanced use cases such as drive-thru speech processing and micro-forecasting.
By transforming support from reactive incident management to proactive, metrics-driven operations, phData delivered increased efficiency, scalability, and cost control, freeing client data scientists to focus on innovation and ensuring the business is prepared for future data-driven growth.
pipeline throughput improvement
in estimated annual savings
Average onboarding cost per new data product in 2025
($2,500 in 2023)
Why phData?
phData is a premier AI and data services company specializing in end-to-end solutions for artificial intelligence, data engineering, analytics, and cloud-native applications. Serving global enterprises across industries, phData delivers strategic consulting, engineering, and managed services to help organizations unlock the full value of their data.Â
As an AWS Advanced Consulting partner, phData holds several AWS specializations & competencies, including Generative AI, Amazon EMR Delivery, Migration Services, and more.
phData has been an exceptional partner in our data journey, with their expertise and customer service in data engineering, machine learning, and cloud solutions. Their team consistently goes above and beyond to deliver tailored solutions that meet unique business needs. With a strong emphasis on collaboration and a deep understanding of modern data platforms in Databricks, and AWS (especially Redshift), phData is a reliable partner that drives real, measurable results. Their commitment to excellence and continuous improvement makes them a top choice for organizations looking to advance their data strategies.
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