Fortune 500 Apparel Retailer Stabilizes Cloud Data Warehouse Batch Processing and Achieves SLA Alignment
Achieved over 2 hours of runtime optimization for critical batch processes
Aligned cloud batch execution with SLA expectations
Improved reliability and predictability of data pipelines
Engagement snapshot
The challenge
As part of its data modernization initiative, the client transitioned its data warehouse from an on-premises environment to a cloud-based architecture. During this phase, both systems were run in parallel to ensure continuity and validation.
While the on-premises batch processes consistently met SLA commitments, the cloud-based batch environment experienced delays and failed to meet expected timelines. The environment involved over 1,000+ tables spanning raw to curated layers, with challenges including SLA breaches, data dependency mismatches between on-prem and cloud pipelines, lack of visibility into critical vs. non-critical processes, and inefficient orchestration leading to delays and incomplete data loads.
What Everest delivered
Everest Technologies took end-to-end ownership of stabilizing the cloud batch processing environment through a structured remediation approach.
Solution components
- Comparative analysis of batch processes
- Assessed on-prem batch processing and raw data ingestion workflows
- Compared with cloud implementation to identify inconsistencies
- Dependency optimization
- Identified gaps in source-to-raw table dependencies
- Re-architected dependencies by grouping source tables logically
- Established accurate sequencing to prevent missing or delayed data loads
- Critical path identification
- Mapped critical processing paths for key data warehouse tables
- Segregated critical jobs into dedicated applications for focused execution and monitoring
- Operational excellence framework
- Defined on-call support model and escalation hierarchy
- Established structured response procedures for SLA risks
- Proactive monitoring and alerting
- Implemented automated monitoring for batch processing
- Enabled alerts every 30 minutes
- Differentiated between critical and non-critical batch processes
- Enabled early detection of delays and failures
Approach
The engagement followed a structured remediation plan focused on understanding existing processes, identifying gaps, and implementing targeted improvements. The team combined comparative analysis with dependency restructuring, critical path identification, and operational governance to stabilize batch execution.
Continuous monitoring and alerting mechanisms were introduced to ensure proactive issue detection and faster response to SLA risks.
Impact & outcomes
- Achieved over 2 hours of runtime optimization for critical batch processes
- Aligned cloud batch execution with SLA expectations
- Improved reliability and predictability of data pipelines
- Enhanced operational visibility and incident response time
Business impact
- Ensured timely availability of business-critical data
- Increased stakeholder confidence in the cloud data platform
- Established a scalable and resilient batch processing framework
Tools & platforms
- Cloud Platform: Microsoft Azure (Data Lake architecture)
- Data Processing / ETL: Azure Databricks
- Scheduling Tool: CA Workload Automation ESP
- Reporting & Visualization: Microsoft Power BI
- Version Control: Git
Team composition
- 4-member team
- Data Engineers with experience in Azure cloud
- Expertise in ETL tools such as Databricks and Datastage
- Experience in SQL
Have a similar challenge?
Talk to our practice leads about how a comparable engagement could work for your team.