A Natural and Organic Food Company

Fresh Gravity rearchitected and optimized an existing AWS-based data and analytics platform using Databricks. The new platform enhanced data discovery through a 30% improvement in data processing efficiency and a reduction in data onboarding life cycle by 25%.

Problem

The client’s data and analytics platform (an integrated data lake), heavily reliant on AWS-managed services like Redshift, Lambda, and others, faced significant challenges in management, operation, and scalability. This architecture struggled to meet the demands of modern data platforms, limiting the ability to address future business needs. The client sought a streamlined solution architecture to reduce complexity, enable advanced analytics, and minimize operational friction.

Solution

Databricks was used to streamline the overall platform architecture, reduce operational overhead, and empower higher velocity and data throughput. Fresh Gravity’s Auto Data Mapping and Data Modeling tool, Penguin played a pivotal role in building a canonical model for the integrated data lake thus enhancing data integrity that helped in reducing data-related issues

Impact

The redesigned architecture achieved substantial optimization of the client’s data and analytics platform including more efficient and accelerated data discovery. The new platform improved data processing efficiency by 30% which allowed the platform to manage and process 2x the volume of data. In addition, the configuration-driven data processing framework accelerated time-to-delivery of new data analytics initiatives by 25%.