Powering Smarter Retail Growth with AI‑Driven eCommerce & Loyalty

Find out how our customer is transforming trusted customer data into personalized experiences that drive revenue, loyalty, and margin.

Quick overview

Client: Global retail enterprise

Technologies Used: Databricks, Microsoft Azure, Google Cloud Platform (GCP), Unity Catalog, Google Kubernetes Engine, and Google Storage Transfer services.

Goal: Streamline accessibility to customers’ trusted and governed data from over 30 countries to drive insights for their eCommerce and loyalty program applications at a lower cost. With trusted data easily accessible on GCP, the customer will be able to use advanced AI-driven personalization to efficiently manage marketing behavioral analysis, and promotion forecasting to drive sales.

Challenges: The customer’s current technical environment is preventing them from implementing cost-effective programs but they cannot afford any downtime during migration. Hence, Wavicle must maintain uninterrupted analytics operations during the migration while ensuring consistent governance, security, and data integrity across Azure and GCP environments.

Solution: Wavicle to develop a cost–efficient, scalable, and governed unified data layer using the Lakehouse foundation on GCP, for analytics, BI, and AI-driven personalization. Migrate eCommerce and Loyalty program applications on Databricks, from Azure to GCP. Validate the GCP Lakehouse architecture, implement centralized governance with Databricks Unity Catalog, and develop an automated framework to verify data consistency between the two cloud platforms during migration.

Result: With Wavicle’s solution, the customer’s eCommerce and loyalty program applications will have seamless access to trusted data for analytics, AI-driven customer personalization, and efficient management of marketing behavioral analysis and promotion forecasting. The solution will also meet the local compliance and GDPR requirements of multiple countries.

The retail challenge

Today’s customers expect relevance everywhere—online, in‑store, and on mobile.

But most retailers are limited by:

Fragmented data across stores and channels
One‑size‑fits‑all promotions that erode margin
Loyalty programs that reward spend—but don’t influence behavior

The consequences: Missed revenue, rising acquisition costs, and declining loyalty.

Our customer, a global retail enterprise operating in over 30 countries, currently relies on its eCommerce and loyalty platforms for personalized promotions, marketing insights, and customer engagement. Their current technical environment was preventing them from implementing cost-effective programs. The customer decided to migrate to Google Cloud Platform (GCP) to better prepare itself for the future and the promise of AI. To do that, the customer needed to address several concerns:

Migrating workloads without disrupting existing analytics pipelines
Ensuring governance, schema enforcement, security controls, local and GDPR compliance remain consistent while processing data from over 30 countries
Maintaining data integrity while multiple environments coexist during the transition?

The customer engaged Wavicle to address these concerns, ensure a structured validation approach, and reduce the risk of inconsistencies in data quality, governance, and performance that could affect BI reporting, analytics workflows, and downstream machine learning models.

Solution

Wavicle is migrating their current environment from Azure to GCP, ensuring reliable performance across both environments during and after migration.

To modernize the data ecosystem, Wavicle is migrating its analytic workloads from Azure to GCP with the goal to build a cost-efficient scalable Lakehouse foundation to support analytics, data engineering, and AI/ML workloads while improving access to trusted, and governed data.

Technical approach

Lakehouse architecture design and validation

Wavicle is designing and validating a scalable Lakehouse architecture on GCP to support analytics, data engineering, and AI/ML workloads.

The architecture uses Lakehouse and data mesh principles, organizing data as domain-owned products. This model supports digital CRM use cases such as marketing behavioral analysis, promotion forecasting, and customer personalization, while establishing a trusted data layer for enterprise reporting and machine learning.

Governance framework implementation

Wavicle is establishing a centralized governance framework to ensure consistent data management across global sources.

Using Unity Catalog, the platform is configured with enterprise security and governance controls, including:

Data lineage tracking
Contract-first schema enforcement and schema governance
Row-level and column-level access controls
Data masking and Zero-Trust security enforcement

These capabilities allow the platform to securely process and manage data from over 30 countries while supporting regional and GDPR compliance requirements.

Cloud platform setup and migration readiness

Wavicle is supporting the setup and validating the target GCP data platform to ensure it can reliably support migrated workloads.

Key platform components include:

Google Cloud Storage (GCS) for the raw data layer with governance-aligned bucket structures
Google Storage Transfer Service to replicate Azure raw datasets to GCP
Dedicated Databricks workspaces across development, testing, and production environments
Migration of supporting services to GCP-native equivalents, including Kafka-based messaging patterns and workloads running on Google Kubernetes Engine (GKE)
Secure cluster policies and private VPC configurations to ensure compute environments remain isolated and protected

Data validation framework development

To ensure migration readiness, Wavicle is developing a standardized data validation framework to verify consistency between the Azure and GCP environments during the transition.

The framework will automate validation checks, including:

File-level and table-level checksum comparisons
Dataset record count alignment across environments
Schema drift detection with automated enforcement policies
Performance benchmarking between Azure and GCP workloads
Validation of PII masking, encryption, and row-level/column-level security controls

This framework provides a structured approach to maintain data accuracy, governance compliance, and platform reliability while both cloud environments operate in parallel during migration.