Problem Statement
The organisation facedd two major bottlenecks in data modernization:
Cloud Migration Risks:
Vendor lock-in due to proprietary cloud DB features.
Data format incompatibility between legacy and cloud systems.
Downtime during migration impacting critical applications.
SQL Performance Degradation:
Poor indexing and join strategies led to slow queries.
Non-optimized SQL increased compute costs in pay-as-you-go cloud models.
Inefficient analytics pipelines causing delays in decision-making.
Proposed Solution
Stage 1: Cloud Migration Strategy
Use schema mapping tools to automate conversion between database engines.
Employ ETL/ELT pipelines to batch data transfers and reduce downtime.
Implement parallel migration with data validation at each step.
Stage 2: Advanced SQL Optimization Layer
Apply indexing strategies (B-Tree, Bitmap, composite indexes) for targeted workloads.
Use partitioning and clustering to improve scan efficiency.
Rewrite queries to avoid unnecessary nested subqueries and large joins.
Introduce materialized views for repeated aggregation queries.
Technical Architecture
Key Tools & Techniques:
Cloud migration tools: AWS DMS and Google Database Migration Service.
SQL optimization: dbt, PostgreSQL EXPLAIN plans, indexing, partition pruning.
API access: GraphQL layer to serve optimized queries to client apps.
Implementation Details
Migration Steps:
Conducted a data profiling audit to identify data type mismatches.
Used vendor-agnostic migration tools to reduce lock-in risk.
Ran checksum validation after each batch migration to ensure integrity.
Optimization Steps:
Applied multi-column indexing for frequent WHERE + ORDER BY clauses.
Denormalized high-read tables for analytics workloads.
Implemented query caching for repeated reports.
Used EXPLAIN ANALYZE to iteratively reduce query execution time.
Results & KPIs
Reduced query execution time by up to 65% on large analytics workloads.
Minimized downtime during migration to under 1 hour.
Lowered cloud compute costs by approximately 30% through query tuning.
Future Enhancements
Integrate AI-assisted query rewriting using LLMs.
Predictively scale cloud DB clusters based on historical load.
Automate indexing suggestions via machine learning.
