Data vault integration and automation with Google Cloud architecture

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Data vault integration and automation for the CPG industry with Google Cloud architecture

Apr 28, 2025

Authors

Ashish Mahajan

Lead Architect, Cloud & Data Tech

Data integration using data vault

The CPG industry is navigating an era of rapid change, where effective data management is essential for survival. Shifting consumer preferences, tightening regulatory requirements, and the complexity of managing global data ecosystems have created significant challenges. The Data Vault methodology addresses these hurdles by enabling seamless integration of diverse data sources, providing comprehensive historical tracking, and offering the agility to adapt to constantly evolving market conditions.

To understand how DV can transform the CPG industry, it is essential to first grasp the unique dynamics of this sector. CPG companies operate in a fast-moving environment defined by high competition, evolving consumer behaviors, complex supply chains, complex partners, and multi-region and multi-country integration. These characteristics drive the need for agile, data-driven strategies that can adapt to constant market changes.

Consumer packaged goods

CPG (Consumer Packaged Goods) refers to everyday products that are sold in packaged form, such as food, beverages, cleaning supplies, toiletries, and personal care items. These products are mass-produced, frequently purchased, and distributed through various channels, including retail stores and online platforms.

Key characteristics of CPGs:

High competition in crowded markets
Frequent promotions to drive sales
Strong pricing sensitivity and brand loyalty among consumers
Complex supply chains involving multiple stakeholders
Regulatory compliance requirements across regions
Rapid e-commerce growth is influencing purchasing behaviors

The dynamic nature of the CPG market demands agility and data-driven strategies to respond to shifting consumer preferences and evolving industry trends.

Find more about how the CPG industry is evolving in today’s AI and engineering space here

What is Data Vault?

Data Vault is a modern data warehousing methodology that offers scalability, flexibility, and auditability. Designed to integrate large and complex datasets from multiple sources, it enables organizations to manage data effectively while supporting evolving business needs and maintaining historical records.

Key features of DV

Manages frequent schema changes without requiring a redesign
Scales to manage large data volumes efficiently
Separates raw data from business logic, ensuring adaptability
Maintains full traceability and auditability for regulatory compliance
Supports parallel data ingestion for faster processing

Core components of DV

Hub tables: Store core business entities such as customers, products, and transactions, stores unique business keys ensuring data integrity and consistency.
Link tables: Capture relationships between entities, such as customer-product or order-delivery links.
Satellite tables: Store descriptive attributes and historical data associated with hubs and links, enabling detailed analysis over time.

By structuring data into these core components, DV simplifies complex data integration, improves data quality, and ensures agility in adapting to business changes.

Leveraging Data Vault for optimized data management in CPG

The CPG industry is defined by high transaction volumes, diverse data sources, and constant market fluctuations. Managing these complexities requires a scalable, adaptable data architecture. DV offers a structured, auditable, and efficient approach to address these challenges, providing CPG organizations with the agility to respond to evolving business needs. Below are a few benefits of using DV in the CPG industry.

High-volume data management
CPG companies manage millions of transactions daily, generating vast amounts of data from sales, inventory, and operations. DV organizes this data into Hubs, Links, and Satellites, creating a scalable structure capable of managing large datasets efficiently while ensuring performance consistency.

Complex partner integration
The CPG ecosystem relies on extensive networks of suppliers, distributors, and retailers. DV simplifies partner integration by allowing new data sources to be added through additional Satellites without redesigning the existing data model. This reduces disruption and accelerates time-to-value.

Multi-region and multi-country integration
Operating across multiple regions involves navigating diverse regulations and product specifications. For instance, a product may have different packaging or attributes in the EU compared to the US. DV accommodates these variations by storing regulatory and regional data in separate Satellites, enabling localized compliance without altering the core data model.

Read more on this with Mastering Global Data Management

Dynamic product pricing and promotions
Frequent updates to pricing, bundling, and promotional strategies are integral to CPG operations. DV decouples raw data from business logic, storing transactional data in Hubs while applying business rules (e.g., discounts or loyalty programs) in the Business Vault. This approach allows for rapid adjustments to pricing models without affecting historical data.

Integration of disparate data sources
CPG organizations draw data from diverse sources, such as ERP, CRM, POS systems, and social media. The DV methodology ensures seamless integration by isolating each unique dataset in dedicated Hubs and Satellites. This modular approach allows for the addition of new sources without the need to reengineer existing systems, ensuring flexibility and scalability.

Regulatory compliance tracking
Sustainability mandates and evolving industry regulations require robust data management capabilities. DV ensures compliance by maintaining a historical record of changes in Satellites, enabling organizations to track and adapt to new requirements while preserving data integrity and auditability.

Read more on this with adaptive data governance

Changing relationship models
Shifts in supply chain dynamics, such as transitioning from one-to-many to multiple relationships, often demand structural changes. DV enables organizations to capture and adapt to these evolving relationships without altering existing Hubs or Satellites, preserving data consistency and reducing complexity.

Data Vault architecture

Data Vault architecture is a modern methodology for building scalable, flexible, and auditable data warehouses. It integrates data from multiple sources, maintaining historical records and supporting business agility in changing environments.

Key layers of DV architecture

Each layer in the architecture serves a distinct purpose, working together to ensure a structured, adaptable, and efficient approach to data management:

Landing zone: The starting point for all incoming data. Raw, unprocessed data from various sources (e.g., databases, APIs, and file systems) is stored here in its original form. This ensures no data is lost, even if records are incomplete or erroneous, providing a foundation for future processing and auditability.
Raw vault: The central repository for untransformed and auditable data. It organizes data into three core components:
Hubs: Represent core business entities, such as customers, products, or transactions.
Links: Capture relationships between these entities, such as "customer-to-product" or "order-to-delivery."
Satellites: Store additional details and historical data for Hubs and Links, such as product descriptions, pricing, or customer attributes.
Business vault: This layer applies business rules and logic to the raw data, transforming it into actionable insights and metrics. Apart from Hubs, Links, Satellites, it may also include:
Point-in-time (PIT) tables: Snapshots of data at specific intervals, enabling faster queries and historical comparisons.
Bridge tables: Pre-joined data structures that model complex relationships for optimized analytics and reporting.
Information marts: This is where business users access the data. Data is transformed into easily consumable facts and dimensions or denormalized tables that support analytical and reporting needs. Data is aggregated or summarized, tailored to specific business needs. This layer supports both historical and real-time data for dynamic reporting.
Metric vault (optional): This optional layer focuses on operational metadata, tracking metrics such as data load success rates, processing times, and data quality checks. It provides transparency into the performance of data pipelines and ensures operational efficiency.

How it works

The data flow begins in the Landing Zone, where all raw data is ingested and retained in its original state. This raw data is then processed into the Raw Vault, where it is organized into Hubs, Links, and Satellites for scalability and traceability. Next, the Business Vault applies transformations and business rules, generating meaningful metrics and creating optimized structures. Finally, the Information Marts layer delivers business-ready insights to end users in formats tailored for analytics and reporting.

Figure 1: Data vault architecture

Why the architecture works

Scalability: The modular structure ensures the system can manage increasing data volumes and new data sources without disruption.
Auditability: Historical records are preserved, providing complete traceability and compliance with regulations.
Adaptability: The architecture allows quick adaptation to new sources, new KPI’s, and metrics, thus making it easy to adapt to evolving business requirements. Modular design allows for quick adjustments to changes in the business environment.
Performance: Uses Parallel loading, optimizes query performance, ensuring timely access to critical insights. PIT, Bridge tables speed up complex queries. Information mart ensures faster queries for reports and the consumption layer.
Flexibility: The layered structure allows for flexible integration of new data sources without disrupting existing systems, as each layer is independent yet linked. Business rules, transformations, and aggregations are applied separately in the Business Vault and Information Mart, which means the system can evolve with changing business needs without disrupting the foundational data.

Find out how Cloud Services and GenAI are shaping the future of data strategies

Google Cloud Platform architecture on Data Vault

Google Cloud Platform (GCP) provides the tools and infrastructure to operationalize DV, ensuring scalability and auditability in data management. By leveraging GCP’s capabilities, organizations can streamline data ingestion, processing, governance, and performance optimization while building a robust foundation for analytics and compliance.

Figure 2: Data vault on Google Cloud Platform

Data ingestion

The Data Ingestion Layer is responsible for capturing batch data from various sources like on-premises databases, SaaS applications, files, and APIs. The ingestion process leverages Cloud Storage, Cloud Functions, and Dataflow for efficient batch processing and seamless data transfer. For real-time data, such as logs, IoT device data, and messaging systems, Pub/Sub is used to support continuous ingestion.

Initially, the ingested data is stored in the Landing Zone within Cloud Storage.
The data is further processed and moved to the Raw Data Vault in BigQuery, where it is stored in a more structured format for further processing.

Data processing and consumption

Once the data is ingested and stored, the next step is Data processing. This is achieved through various tools that automate and manage complex workflows.

Dataform automates SQL-based workflows, simplifying data transformation.
Dataflow is used for more complex ETL (Extract, Transform, Load) operations, managing sophisticated ETL pipelines.

Orchestrate and manage these workflows, Cloud Composer is employed for scheduling and monitoring.

Data is moved to the Business vault and Information mart, where business rules, aggregations, and analytical models are applied to generate meaningful insights.
The transformed data is stored in BigQuery, enabling high-performance querying.
Vertex AI is integrated with BQ and data pipelines to enable ML model training, deployment, and integration.
Visualization and reporting are facilitated through tools like Looker, Data Studio, and other analytics platforms.

Governance and metadata management

Data governance is essential for managing data securely and efficiently.

Data plex plays a key role in ensuring centralized policy enforcement, monitoring data quality, and managing metadata across the platform.
Data catalog automates processes like data discovery, tagging, and classification, enhancing metadata management.
BigQuery policy tags provide fine-grained, column-level security to ensure sensitive data is protected and accessible only to authorized users.

Operational efficiency

Maintaining operational efficiency is crucial for smooth data pipeline execution and scalability.

Cloud monitoring and logging provide real-time insights into system performance, resource usage, and pipeline execution.
Custom dashboards and alerts help with anomaly detection and performance monitoring, allowing administrators to address issues and optimize resource usage proactively.
Initiative-taking monitoring aids in cost optimization by identifying inefficiencies and areas for improvement.

Security and governance

Security is paramount in a cloud-based data management system, and a variety of GCP components ensure robust security practices:

IAM (Identity and Access Management) enforces role-based access control (RBAC) at the dataset and table levels to restrict data access to authorized users.

BigQuery policy tags are used for column-level security, safeguarding sensitive data.

VPC service controls create secure perimeters around data to prevent unauthorized access based on network policies.

Cloud audit logs provide detailed records of access and modifications, ensuring compliance with regulatory standards and enabling real-time detection of suspicious activity.

Practical applications of Data Vault in CPG

The DV methodology addresses the complexity of managing diverse datasets and evolving business requirements in the CPG industry. By structuring data into Hubs, Links, and Satellites, DV provides scalability and traceability across critical CPG functions, ensuring robust data management and actionable insights.

Sell-in data model

Sell-In data encompasses product, sales, and financial transactions, forming the foundation for planning, order management, and performance tracking. DV’s modular structure supports the efficient organization and auditability of high-volume transactional data.

Hubs Currency Hub: Stores currency identifiers and exchange rates
Customer Hub: Manages customer data, linking regions, trade channels, and customer hierarchies.
Period hub: Tracks periods and fiscal years Sell-in
Invoice Hub: Stores invoice-level transaction data
Geography hub: Manages geographic data from country to region.

Sell-in AOP hub: Houses planning targets (Annual Operating Plans). Business unit company code hub: Identifies business unit codes. Sales representative hub: Tracks sales rep data

Links

Sell-in order invoice link: Connects invoices to orders.
Sell-in delivery order link: Links deliveries to orders.
Sell-in forecast link: Associates forecasts with sales and geographic data.

Satellites

Currency satellite: Includes additional details on currency types and exchange rates.
Customer satellite: Augments customer data with industry and geographic specifics
Sell-in invoice satellite: Captures line-level details such as SKUs and product pricing.

Retail Management Systems (RMS) data model

RMS data focuses on retail operations, vendor management, and product distribution across physical and digital channels. DV ensures comprehensive tracking of retail performance while enabling seamless integration of diverse data sources.

Hubs

Product hub: Centralized catalog with unique product identifiers
Retailer hub: Manages retailer data, including geographical locations.
Store hub: Contains store details for both physical and online locations.
Vendor hub: Tracks vendor and supply chain information

Links

Retailer store product link: Associate products with retail stores
Store vendor link: Links stores with their vendors.

Satellites

Product satellite: Provides product details like specifications and stock levels.
Retailer satellite: Enhances retailer data with operational details like store size and hours.
Vendor satellite: Augments vendor data with contract terms and product offerings

Inventory and demand planning baseline model

Managing inventory and accurately forecasting demand are essential for cost control and meeting customer expectations. DV’s architecture integrates data from manufacturing plants, warehouses, and distribution channels to provide end-to-end visibility.

Hubs

Manufacturing plant hub: Tracks plant-specific data, including codes and location.
Warehouse hub: Manages warehouse locations, capacities, and distribution strategies.
Finished goods inventory hub: Monitors finished goods inventory levels.
Warehouse stock detail hub: Captures SKU-level stock details with timestamps.

Satellites

Manufacturing plant satellite: Enhances plant data with capacity and workforce information.
Warehouse satellite: Provides data on warehouse size and operational efficiency.
Finished goods inventory satellite: Captures stock levels, reorder points, and allocation status.

Warehouse stock detail satellite: Tracks stock lifecycle, including expiration data.

Links

Manufacturing plant warehouse link – Maps the movement of goods from manufacturing plants to warehouses.
Warehouse retailer link – Connects inventory transfers.
Demand forecast link – Links forecasts with products, regions, and sales history.
Stock Movement Link – Tracks stock transfers.

eCommerce data model

eCommerce operations generate critical data on online sales, campaign performance, and customer engagement. Data vault enables seamless integration of eCommerce data with the broader CPG ecosystem, supporting analysis and optimization.

Hubs

Product hub: Tracks unique product identifiers and associated attributes
Campaign hub: Manages marketing campaign metrics.
Retailer hub: Identifies digital sales channels and retailers.
Date hub: Tracks time-related data for trend analysis.
Product mapping hub: Organizes products across categories and channels.

Links

Sales link: Connects product sales transactions to retailers and dates.
Campaign product link: Links products to specific campaigns.

Satellites

Sales satellite: Contains transactional sales data, including payment methods and promotions.
Campaign satellite: Tracks campaign performance metrics such as ROI, impressions, and conversions.
Search satellite: Analyses search behaviours, including volume and product relevance.
Availability satellite: Tracks product availability across retailers and warehouses
Rating review satellite: Captures customer ratings and reviews to assess product satisfaction.
Content satellite: Tracks engagement metrics related to content and user interaction.
Media satellite: Manages multimedia assets used for product listings and campaigns.

Success story: Streamlining data quality and data governance for an electronics giant

Conclusion

Data has become more than just a resource—it is the foundation for innovation and growth. The DV methodology provides a game-changing framework, empowering organizations to manage their data and harness it as a strategic asset.

By adopting DV, CPG companies can transform their approach to data management. Its scalability ensures businesses can manage massive transaction volumes, while its flexibility allows for seamless integration of new partners, data sources, and market requirements. More importantly, DV does not just support compliance and operational efficiency—it opens the door to deeper insights and smarter decision-making across functions.

When paired with advanced cloud technologies like Google Cloud Platform, DV becomes even more powerful, delivering enhanced data governance, real-time processing, and cost-efficient scalability. This combination equips businesses with a robust foundation for navigating uncertainty and meeting customer expectations.

As CPG leaders adopt DV to meet today’s challenges, they are also laying the foundation for tomorrow’s innovations, ensuring their ability to not just survive but to lead in the years to come.

Learn more about Google Cloud architecture