How ProCogia helped T-Mobile to
migrate 2.5PBs of Data Lake to
Snowflake
70%
Cost Reduction
67%
Improvement in Operational Efficiency
68%
Reduction in
Data Size
Company Information
Our client, T-Mobile, sought a comprehensive solution to migrate their extensive Data Lake to Snowflake. The goal was to overcome scalability limitations, reduce operational costs, and enhance data processing efficiency. To achieve this, we designed and implemented a robust data migration and optimization strategy tailored to T-Mobile’s needs.
The Challenge
T-Mobile was experiencing several limitations with their existing systems:
Scalability Limitations
Legacy systems suffered from scalability issues, hampering their ability to handle growing data volumes and diverse data types efficiently.
High Maintenance Overheads
Managing and maintaining the legacy systems required significant manual intervention, leading to increased operational costs and resource allocation.
Inefficient Compute Processes
The compute processes in the legacy systems were not optimized for parallel execution, resulting in longer processing times and reduced operational efficiency.
Escalating Storage Costs
Data redundancy and inefficient storage practices led to escalating storage costs, posing financial challenges and hindering data management strategies.
Our Approach
Data Ingestion and Processing
Leveraged Snowpipe to stream data from Kafka directly into Snowflake's landing zone, eliminating manual intervention and accelerating data ingestion.
Utilized Snowpark for processing semi-structured data, transforming it into enriched, structured data stored in our data integration layer.
Storage Optimization
Migrated data from multiple S3 buckets and folders to Snowflake, reducing data redundancy and storage footprint.
Implemented secure views and dynamic masking based on RBAC roles to enhance data security and comply with regulatory requirements.
Compute Efficiency
Transitioned from EMRs to Snowpark, leveraging Snowflake's parallel execution capabilities and automatic scaling.
Integrated Git, external packages, and dynamic tables for faster development cycles, reduced code complexity, and improved resource utilization.
Architecture
The Results
Cost Reduction
We achieved a substantial cost reduction of 70%, significantly lowering annual data management expenses. This reduction was primarily driven by optimized storage utilization and streamlined compute processes.
Operational Efficiency
Our efforts led to a 67% improvement in ETL processing efficiency, reducing processing time from 6 hours to just 2 hours. This enhancement not only saved valuable time but also allowed resources to be redirected towards strategic data initiatives and accelerated time-to-market for analytics solutions.
Scalability and Agility
By leveraging Snowflake's scalability features and modern cloud architecture, we enabled the client to seamlessly adapt to evolving business requirements. This enhanced scalability and agility empowered the client to scale operations without compromising performance or data integrity.
Data Optimization
We successfully reduced the data size by 68%, optimizing storage utilization and minimizing data redundancy. This data optimization not only reduced storage costs but also improved data accessibility and management efficiency.
Enhanced Security and Compliance
Our migration to Snowflake also enhanced data security and compliance measures. By implementing RBAC-based access controls, dynamic masking, and secure views, we ensured that sensitive data was protected, and regulatory requirements were met effectively.
Improved Collaboration and Insights
The modern data platform facilitated improved collaboration among teams and stakeholders. Snowflake's collaborative development environment and advanced analytics capabilities enabled faster insights generation, leading to data-driven decision-making and business growth.
Customer Satisfaction
Ultimately, the successful migration and optimization efforts resulted in increased customer satisfaction. The client experienced improved data reliability, faster data processing, and enhanced data governance, leading to better overall performance and business outcomes.
Conclusion
Migrating to Snowflake unlocked new possibilities and paved the way for data-driven innovation at T-mobile. By addressing key challenges and leveraging modern cloud data platforms, we have not only optimized costs and processes but also laid a robust foundation for future growth and innovation in data management and analytics.
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