How We Achieved 4x Faster Decision-Making With Data Warehouse Modernization

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Additionally, they had diverse customer touchpoints that generated leads and customer records from multiple sources such as Salesforce, custom applications & external market data. Due to the legacy architecture, the client faced several business challenges such as,

• The overlay of customer & lead records led to data inconsistencies, misdirected campaigns, and lower onboarding rates.
• Due to numerous source touchpoints, the process of establishing precise customer profiles within the downstream data systems became notably intricate and challenging.

Overall, the stale and overlay records added complexity to the client’s data model, hampering their ability to streamline data, elevate marketing strategies, and achieve business goals.

Solution: Seamlessly transitioning legacy systems to the cloud with enhanced data processing and reporting dynamics

Our team devised a comprehensive solution for the data warehouse migration, considering the client’s unique business challenges and organizational data model.

• • Phased data warehouse migration and ETL transformation: To ensure a smooth transition, the data warehouse migration was meticulously planned and executed in phases. This approach encompassed an initial data load followed by data synchronization to AWS S3, effectively transforming it into a robust data lake.

Additionally, ETL processes were orchestrated using Databricks notebooks, aggregating data from a variety of sources.

• AWS DMS for migration: Historical data from the On-Premises data warehouse was migrated to AWS S3 using AWS Database Migration Service (DMS), facilitating change data capture for data synchronization.
• Streaming pipelines for real-time data ingestion: Leveraging advanced data streaming techniques, we established robust streaming pipelines that efficiently ingested JSON events from the AWS S3 delta lake, utilizing AutoLoader for data integration. Our solution ensured seamless data ingestion from the new POS system’s read-only database, orchestrated through AWS Event Bridge, SQS, and SNS services, operating at 15-minute intervals for real-time data availability.
• Data model optimization: We streamlined the existing data model, significantly reducing the number of tables from over 700 to under 300. This optimization enhanced data processing efficiency and simplified data management.
• Enhanced data quality: A robust framework for data quality and governance was meticulously implemented to improve the overall data delivery and quality within the warehouse. Additionally, we proactively mitigated data-related issues affecting reporting accuracy and the effectiveness of marketing campaigns.

Benefits: Elevating data efficiency and decision-making with modernized data architecture

Our implementation resulted in a transformative outcome & yielded a multitude of benefits for the client:

• • Enhanced data efficiency: Optimizing the data model and reducing table count improved data processing and reporting efficiency.
  • Faster decision-making: Synchronization between on-premises and cloud data platforms ensured real-time access to data, leading to faster decision-making.
  • Streamlined reporting: Modernizing the reporting system and leveraging Power BI improved report accessibility and insights for thousands of users.
  • Improved data quality: Event-based ingestion and data processing mechanisms ensured data quality and consistency

Ready to modernize your data platform?

Our partnership with the retail fintech client has transformed data strategies & positioned them for sustained success. This transformation showcases the potential of data modernization in delivering tangible business benefits.
Whether you’re grappling with a legacy data warehouse or seeking to modernize your data management, our expert team is here to guide you through the transition process.

Partner with us and embark on your journey to data-driven success today.