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Enterprise Platform Evolution

Enhancement Arena

Execute architectural upgrades and feature enhancements on existing production data platforms. Implement schema evolution, incremental strategies, governance frameworks, and performance optimizations.

High PriorityENH-001

Customer Segmentation Integration

Introduce customer_segment across Databricks, Snowflake, and reporting layers to enable segment-wise revenue analytics.

Impact: Executive Reporting Enhancement
Difficulty:Beginner
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Critical PriorityENH-002

Late Arriving Customer Data Enhancement

Handle late arriving customer correction records across Databricks, Snowflake, and reporting systems to ensure accurate regional revenue reporting.

Impact: Reporting Accuracy Improvement
Difficulty:Intermediate
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High PriorityENH-003

Data Quality Framework

Implement validation rules, quarantine tables, audit logging and monitoring for production datasets.

Impact: Data Trust & Governance
Difficulty:Intermediate
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High PriorityENH-004

Performance Optimization

Reduce runtime, optimize Spark transformations and improve warehouse efficiency.

Impact: Cost & Performance Improvement
Difficulty:Advanced
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High PriorityENH-005

Incremental Loading Implementation

Enhance the existing full-load pipeline by implementing an incremental loading strategy to reduce processing time and infrastructure cost.

Impact: Runtime & Cost Optimization
Difficulty:Advanced
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Critical PriorityENH-006

Schema Evolution & Backward Compatibility

Enhance production pipelines to support new source attributes while preserving backward compatibility across Databricks, Snowflake, and reporting systems.

Impact: Zero-Downtime Platform Evolution
Difficulty:Expert
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