This US retail major has a strong e-commerce presence and is a world leader in selling fashion, beauty, and home goods. This case study explores the migration from a legacy data lake to a business intelligence platform using Big Query.
The hyper-scaled Google Cloud-based solution enables an end-to-end data platform for all the reporting, BI, and AI/ML needs within the digital sub-function.
Key Challenges
- Legacy on-premise infrastructure that was not scalable and robust to deliver quick insights.
- Strategy to leverage Google Cloud services for most data analytics services.
- Legacy on-premise data lake was not agile and scalable.
- The legacy data engineering framework required extended development and test cycles.
Ready to experience?
TALK TO EXPERTS
The Solution
Google Cloud-based solution enables an end-to-end data platform for all the reporting, BI, and AI/ML needs.
- Leveraged partnership with Google to deliver a scalable, high-performance digital business intelligence platform.
- Google Big Query data warehouse that leverages cloud-native components and existing investments made by the client on enterprise-wide cloud tools.
- High degree of automation through a metadata-based ingestion framework which automates the data pipeline generation and reduces the lead time to onboard any new source system data. The low-code approach reduces the effort that would otherwise have been required to develop data pipelines from scratch.
Building a robust business intelligence platform with Google Cloud framework.
- Leveraged partnership with Google to deliver a scalable, high-performance digital business intelligence platform.
- Google Big Query data warehouse that leverages cloud-native components and existing investments made by the client on enterprise-wide cloud tools.
- The platform will also act as the data foundation on which advanced AI/ML models would be built for use cases such as data anomaly detection and data reliability.
- The metadata-based automated data ingestion framework reduces time to onboard new source system data.
Benefits
The hyper-scaled Google Cloud-based solution enables an end-to-end data platform for all the reporting, BI, and AI/ML needs within the digital sub-function.
The metadata-based automated data ingestion framework reduces time to onboard new source system data.
Robust data warehouse specific for the digital sub-function that enables quick insights relevant for the marketing and digital sub-functions.
The platform will also act as the data foundation on which advanced AI/ML models would be built for use cases such as data anomaly detection and data reliability
