Key highlights
Scaling demand forecasting and markdown optimization with AWS, Redshift, Airflow, and MLOps, with zero-ETL to boost accuracy, speed, and savings.
20%+
forecast accuracy
~50%
AWS cost savings
99%
streaming forecast coverage
10%
stock optimization
50%+
faster Glue/SQL jobs
Cross-cluster Redshift data Sharing
Zensar partnered with a leading South African retailer to build an AWS-powered data and analytics platform for scalable demand forecasting and markdown optimization. The solution unified data sources, enabled zero-ETL pipelines, implemented a three-tier Redshift architecture, automated workflows with Airflow, and operationalized MLOps, delivering 20%+ accuracy gains, ~50% AWS cost savings, 99% streaming forecast coverage, and faster production cycles.
Overview
Retail Demand Forecast & Markdown Optimization
End-to-end AWS data platform: Zero-ETL ingestion, Redshift three-tier curated data lake, Redshift Data Sharing, Airflow orchestration, MLOps runbook automation
The client needed a robust forecasting platform to improve perishable inventory decisions, reduce waste, manage peak demand, and control AWS costs. Zensar collaborated with them to design and implement a scalable data pipeline that integrates multiple source systems with AWS, curates Redshift layers, enables Zero-ETL direct ingestion to MySQL, and supports Redshift data sharing for secure cross-cluster access. Airflow orchestrated pipelines with parameterized workflows, metadata-driven ingestion, and event-based execution. An MLOps framework (Kedro-runbook) standardized model deployments and CI/CD, returning forecasts from partners (e.g., Blue Yonder) back into the lake. Data quality was embedded using configurable Redshift checks and DynamoDB for lineage tracking.
Zensar’s Brief – Steps taken as an implementation partner by Zensar
Built a three-tier Redshift framework (full/incremental loads, curated zone)
Established a Zero-ETL augmented pipeline, direct MySQL integration
Implemented Airflow orchestration with cross-account access and RBAC
MLOps runbook automation (Kedro parameterization, CI/CD)
Return-leg pipeline for forecasting partner → AWS (Blue Yonder)
Data quality framework and lineage; metadata and audit via DynamoDB
EMR automation; DevOps deployment, repositories, sanity checks
Redshift data sharing between BI, EDL, and food demand clusters
Beyond the Brief – How it helped the client
20%+ forecast accuracy; 99% streaming coverage
~50% AWS cost savings (1.1M → 565K Rand in five months)
50%+ faster Glue jobs and SQL execution; reduced process times
Reduced food waste and revenue leakage; better staff utilization
Near-real-time access for business analysts; secure data sharing
Scalable, managed services adoption; improved governance, and alerts
Challenges
Inaccurate fresh food forecasts, fragmented pipelines, rising AWS costs, and limited unified data access for business analysts across clusters
The retailer struggled with forecasting accuracy for fresh items, leading to waste and unmet demand during peak seasons. Siloed data sources and inconsistent ingestion hindered analytics and decision-making across teams. Operational issues, including misaligned restaurant staffing and ingredient stocking, added to inefficiencies. Business analysts lacked seamless access to datasets from multiple Redshift clusters, leading to duplication and increased processing costs. Additionally, AWS resource provisioning was suboptimal, resulting in oversized clusters and non-optimized schedules that inflated costs and cycle times.
Solution
AWS-native zero-ETL pipelines, a three-tier Redshift architecture with data sharing, Airflow-based orchestration, automated MLOps runbooks, and cost-optimized DevOps
Zensar partnered with the customer team to operationalize an AWS-first architecture with Zero-ETL ingestion and curated Redshift layers. Data sharing bridged BI, EDL, and food demand clusters, giving analysts secure, near-real-time access without physical data movement. Airflow orchestrated multi-account pipelines with parameterization, alerts, and metadata-driven execution. MLOps standardized model deployment via a Kedro runbook, integrating CI/CD and operations. Return-leg pipelines captured forecasts from partners. EMR, Glue, and SQL jobs were optimized to cut execution times by 50%+. Cluster RPUs were right-sized (128 → 64), and lower environments were shut down when idle, achieving ~50% cost savings.
1.
Data pipeline and Redshift threetier:
Metadata-driven ingestion; full/incremental loads; curated zones for consumption; UAT-ready provisioning and access.
2.
Airflow orchestration and automation:
Cross-account orchestration, parameterized workflows, event-based runs, RBAC, alerts via SNS; operator-friendly access.
3.
MLOps runbook and return-leg:
Kedro parameterization; CI/CD integration; partner forecast ingestion (Blue Yonder → AWS); EMR automation for scale.
4.
Zero-ETL and data sharing
Direct MySQL integration; Redshift data sharing across BI and Foods clusters; near-real-time analyst access; no data duplication.
Solution enablers
Configurable data quality in Redshift
Lineage and audit (DynamoDB)
Star schema ETL templates
Pivot/unpivot Redshift templates
Managed services-first approach
CloudFormation infra provisioning
Bucket versioning
Git-based version control
SNS notifications
Detailed logging
IAM role-based security
Encryption for data and services
Two-prod account orchestration
DevOps pipelines for code migration
UAT build accelerators
Documentation and sanity checks
Production customizations
Impact
Near real-time, cost-optimized forecasting and markdown analytics delivering >20% accuracy gains, 50%+ execution speedups, and ~50% AWS savings.
Forecasting performance
20%+ accuracy uplift
99% streaming forecast coverage
10% stock optimization
Operational efficiency
Glue/SQL 50%+ faster
Target management: 60 → 10 mins
Return leg: 120 → 60 mins
Cost optimization
~50% savings in five months
1.1M → 565K Rand
RPUs: 128 → 64
Business agility
Data sharing across clusters
Analyst self-serve access
Faster time-to-insights
Business outcome
The client saw gains in forecasting precision and inventory planning, reducing food waste and revenue leakage. They streamlined batch processes and accelerated operational cycles, enabling near real-time decision-making for fresh foods. With Redshift data sharing, business analysts accessed curated datasets seamlessly across clusters without duplication, improving security and productivity. ~50% in cost savings were realized through cluster right-sizing, optimized job scheduling, and eliminating idle environments. Overall, the platform strengthened governance, reliability, and scalability while supporting executive reporting needs.
Client voice (highlights): “A huge achievement… The BI team is a real superstar… The business is very excited and recognizes the massive effort from IT.”
Conclusion
Zensar’s AWS-centric implementation delivered an end-to-end, scalable, and cost-efficient platform for demand forecasting and markdown optimization. By combining zero-ETL ingestion, a three-tier Redshift architecture, secure data sharing, Airflow orchestration, and standardized MLOps, the solution addressed critical challenges, including accuracy, waste reduction, data accessibility, and cost control. An incremental delivery approach, strong DevOps discipline, and metadata-driven frameworks enabled rapid adoption and continuous optimization.
The result: a resilient, high-performing data platform recognized by the client for excellence and delivering measurable business value.