Legacy Data Warehouse Modernized to Cloud-Native Architecture
The Challenge
The client's 15-year-old on-premise Teradata data warehouse had become a costly bottleneck. Annual licensing and maintenance costs exceeded $2M, yet the system struggled to handle modern analytics workloads. Adding new data sources required months of effort, and business users waited days for ad-hoc analysis queries to complete. The aging hardware lacked elasticity to handle month-end and quarter-end reporting spikes, requiring expensive over-provisioning. IT spent 70% of their time maintaining the legacy system instead of enabling new analytics capabilities.
The Strategy
- 1 Assess current data warehouse workloads and identify modernization candidates
- 2 Design cloud-native architecture using Azure Synapse Analytics and Data Lake Storage
- 3 Implement phased migration strategy with parallel run to minimize risk
- 4 Establish modern data governance and security framework for cloud environment
📋 Data Warehouse Assessment & Planning
The Problem We Found
The Teradata warehouse contained 850+ tables, 12,000+ stored procedures, and 400+ ETL jobs with undocumented dependencies. Previous migration attempts failed due to lack of workload understanding and business impact analysis.
Our Approach
- Analyzed 6 months of query logs to identify usage patterns and performance bottlenecks
- Cataloged all data sources, transformation logic, and downstream consumers
- Classified workloads by migration complexity: lift-and-shift, refactor, or retire
- Created detailed migration wave plan prioritizing high-value, low-risk workloads
- Established success criteria and rollback procedures for each wave
The Result
Discovered that 40% of tables hadn't been accessed in 12 months and could be archived. Identified 25 critical reports driving 80% of business value. Created phased migration plan across 6 waves over 9 months with clear go/no-go criteria.
Metrics
☁️ Cloud-Native Architecture Design
The Problem We Found
Direct lift-and-shift of Teradata architecture to cloud would perpetuate existing problems and fail to leverage cloud benefits. The architecture needed redesign for cloud-native patterns.
Our Approach
- Designed medallion architecture (Bronze/Silver/Gold) in Azure Data Lake Gen2
- Implemented Azure Synapse Analytics for enterprise data warehouse workloads
- Created data lakehouse pattern combining structured warehouse and semi-structured lake
- Established PolyBase external tables for seamless data lake/warehouse integration
- Designed auto-scaling compute pools for variable workload demands
The Result
New architecture provided 10x data storage capacity at 60% lower cost. Compute resources now scale elastically based on demand, eliminating over-provisioning. Data scientists gained direct access to raw data in lake while business users consumed curated data warehouse views.
Metrics
🚀 Phased Migration Execution
The Problem We Found
Big-bang migration approach had failed previously, causing week-long outages and business disruption. The migration needed to be incremental with parallel run capability.
Our Approach
- Migrated data sources in waves, starting with non-critical analytics datasets
- Implemented dual-load strategy writing to both Teradata and Synapse during transition
- Created automated validation framework comparing Teradata vs Synapse query results
- Ran parallel systems for 30 days per wave with reconciliation reporting
- Migrated business users progressively with training and support
The Result
Successfully migrated 510 tables and 8,000 stored procedures with zero production incidents. Dual-run strategy caught 15 edge cases before cutover. All 6 migration waves completed on schedule with business confidence maintained throughout.
Metrics
Impact & Results
The data warehouse modernization delivered $1.2M annual cost savings (60% reduction) while dramatically improving performance and agility. Query performance improved 5x on average, with month-end reporting completing in hours instead of days. Time-to-insight for new analytics use cases dropped from months to weeks as data engineers leveraged cloud-native tools. The elastic compute model eliminated capacity planning bottlenecks, and the data lakehouse architecture unlocked advanced analytics and ML capabilities that were impossible on the legacy platform.
"Zatsys didn't just migrate our data warehouse—they transformed our entire approach to analytics. We're now a data-driven organization with the infrastructure to support our ambitions."
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