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Case
Manufacturing Data Warehouse Modernization
A scalable cloud-native manufacturing analytics platform built to modernize SAP BW-driven data ecosystems using BigQuery, Cloud Composer, DBT, GCP Storage, SQL optimization, and enterprise-grade SRE operations.
SAP BW + GCP BigQuery + Cloud Composer + DBT Case Study
A scalable cloud-native manufacturing analytics platform built to modernize SAP BW-driven data ecosystems using BigQuery, Cloud Composer, DBT, GCP Storage, SQL optimization, and production-ready monitoring practices.
Revuteck delivered an enterprise-grade manufacturing data warehouse modernization solution by migrating SAP BW-centered analytical workloads into a scalable GCP and BigQuery-based architecture.
The solution included automated cloud storage ingestion, Cloud Composer DAG orchestration, DBT-driven transformation frameworks, BigQuery optimization, validation and reconciliation workflows, reporting enablement, production support operations, and SRE-driven operational reliability.
Source basis: Enterprise manufacturing analytics modernization project using SAP BW, BigQuery, Cloud Composer, DBT, GCP Cloud Storage, SQL optimization, production support, and SRE monitoring operations.
Business required :
The client operated a complex manufacturing analytics ecosystem supporting production planning, procurement, inventory management, supply chain operations, finance reporting, sales analytics, and plant-level operational reporting.
The legacy SAP BW-centered architecture created performance bottlenecks, operational dependency complexity, limited scalability, and reduced production visibility across critical reporting workflows.
The business required a modern enterprise analytics platform capable of:
Supporting scalable manufacturing analytics
Improving BigQuery processing performance
Reducing workflow dependency complexity
Improving reporting readiness and freshness
Enabling enterprise-grade validation frameworks
Improving operational monitoring visibility
Supporting production reliability and SLA compliance
Establishing a scalable cloud-native reporting architecture
M
Manufacturing Data Warehouse Modernization
Founder
★★★★★
Client Review
“Revuteck successfully modernized our manufacturing analytics ecosystem into a scalable GCP and BigQuery-based architecture. The platform improved reporting performance, operational visibility, transformation maintainability, and long-term analytical scalability.”
Research
Project Research
An inside look at how we identified the core problems, structured our approach, and delivered a scalable solution.
Business Challenges
The existing manufacturing analytics ecosystem struggled with legacy SAP BW dependency, complex transformation workflows, slow reporting performance, operational visibility gaps, data quality inconsistencies, and increasing production support overhead.
Focus Areas:
-SAP BW modernization
-BigQuery warehouse optimization
-Workflow orchestration automation
-DBT-driven transformation modeling
-Enterprise data quality validation
-Production monitoring and SRE
-Reporting scalability and performance
Project Scope
The project included SAP BW source integration, GCP Cloud Storage implementation, Cloud Composer DAG orchestration, BigQuery warehouse development, DBT transformation frameworks, validation automation, reporting enablement, production support workflows, and SRE-driven operational reliability.
Deliverables:
-Cloud-native manufacturing analytics architecture
-Composer DAG orchestration workflows
-BigQuery warehouse implementation
-DBT transformation framework
-Curated manufacturing analytics layers
-Data validation and reconciliation framework
-Monitoring dashboards and alerts
-Incident response workflows
-Enterprise reporting enablement
Development Approach
The engineering phase focused on scalable BigQuery modeling, modular DBT transformations, workflow orchestration, SQL optimization, data validation automation, and enterprise-grade operational monitoring.
Key Research Areas:
-SAP BW integration strategy
-BigQuery performance optimization
-DBT modular transformation patterns
-DAG orchestration best practices
-Manufacturing reconciliation strategy
-SLA-driven support operations
-Production observability framework
Solution Provided
A layered cloud-native architecture was designed to separate source extraction, storage, orchestration, warehousing, transformation, reporting, monitoring, and operational support layers for improved scalability, maintainability, and reliability.
Architecture Goals:
-Reliable SAP BW data ingestion
-Scalable BigQuery analytics processing
-Modular DBT transformation framework
-Optimized reporting datasets
-Automated data quality validation
-Production-ready observability
-Reliable incident response workflows
-Enterprise-grade manufacturing analytics
Process
Process Flow
We build scalable mobile and web applications tailored to industry-specific workflows, user expectations, compliance requirements, and long-term business growth.
Discovery & Assessment
Analyzed SAP BW data structures, reviewed manufacturing reporting workflows, identified source dependencies, documented transformation complexity, and gathered modernization requirements for cloud migration planning.
Key Activities:
-SAP BW source analysis
-Manufacturing workflow assessment
-Reporting dependency analysis
-Business requirement gathering
-SLA and operational review
-Migration roadmap definition
-Risk and impact assessment
Target Architecture
Designed a scalable GCP + BigQuery + Cloud Composer + DBT cloud-native architecture with separate ingestion, orchestration, warehousing, transformation, reporting, monitoring, and operational support layers.
Key Activities:
-GCP architecture planning
-BigQuery warehouse design
-Cloud Composer orchestration setup
-DBT transformation strategy
-Security and governance planning
-Monitoring architecture implementation
-Scalable workflow design
Cloud Storage Setup
Configured GCP Cloud Storage landing, archive, reject, audit, and reprocessing zones to support secure source ingestion, traceability, structured storage, and operational data management.
Key Activities:
-Bucket structure implementation
-Landing zone setup
-Archive and reject path configuration
--Audit storage management
-File naming standards
-Reprocessing strategy setup
-Secure storage configuration
BigQuery Warehouse
Developed BigQuery raw, staging, curated, mart, and audit datasets with optimized warehouse structures, partitioning strategies, clustering logic, and scalable analytical models.
Key Activities:
-BigQuery dataset creation
-Partitioning and clustering setup
-Table and schema modeling
-Audit column implementation
-Curated analytics layer creation
-Reporting mart preparation
-Query optimization setup
Cloud Composer DAG
Implemented Cloud Composer DAG workflows for ingestion orchestration, dependency management, DBT execution, data quality validation, retry handling, notifications, and SLA tracking.
Key Activities:
-DAG workflow development
-Dependency orchestration
-Retry and failure handling
-DBT execution integration
-Notification setup
-SLA monitoring implementation
-Workflow observability
DBT Transformation
Implemented DBT modular transformation models for cleansing, standardization, deduplication, incremental processing, business rule application, and manufacturing analytics preparation.
Key Activities:
-Staging model development
-Intermediate model creation
-Mart layer implementation
-Incremental logic setup
-Data testing framework
-Business rule transformation
-Documentation and lineage setup
Reporting & Analytics
Enabled curated manufacturing analytics datasets and reporting models to support production analytics, inventory insights, procurement visibility, finance reporting, and operational KPIs.
Key Activities:
-Reporting dataset preparation
-KPI analytics enablement
-Manufacturing reporting optimization
-Curated business consumption layers
-Operational analytics modeling
-Executive reporting support
-Data consumption optimization
Production Support & SRE
Implemented production support workflows, DAG monitoring, BigQuery job observability, incident management processes, SLA tracking, RCA documentation, and SRE-driven operational reliability practices.
Key Activities:
-DAG monitoring setup
-BigQuery job monitoring
-Incident response workflows
-SLA tracking implementation
-Root cause analysis
-Production issue management
-Operational support documentation
Project Features
Key Features Delivered
(01)
Intelligent Manufacturing Dashboard
Centralized manufacturing analytics dashboards designed to provide visibility into production, inventory, procurement, finance reporting, operational KPIs, and enterprise reporting readiness.
Key Points:
-Real-time manufacturing insights
-Production KPI visibility
-Reporting performance monitoring
-Inventory and procurement analytics
-Business-ready dashboards
-Executive reporting enablement
(02)
Cloud Composer Orchestration
Cloud Composer DAG workflows automate ingestion, orchestration, dependency handling, DBT execution, retry mechanisms, audit tracking, and workflow monitoring.
Key Points:
-Automated DAG orchestration
-Dependency management
-Retry and failure handling
-Workflow monitoring
-Notification and alerting
-SLA tracking integration
(03)
DBT Transformation Framework
DBT modular transformation architecture enables scalable manufacturing data cleansing, standardization, deduplication, testing, lineage tracking, and analytical modeling.
Key Points:
-Modular DBT models
-Incremental transformations
-Deduplication workflows
-Business rule implementation
-Data lineage tracking
-Testing and validation framework
(04)
BigQuery Analytical Warehouse
Scalable BigQuery warehouse architecture supports raw, staging, curated, mart, and audit layers optimized for manufacturing analytics and enterprise reporting.
Key Points:
-Multi-layer BigQuery datasets
-Optimized warehouse structure
-Partitioning and clustering
-Curated analytical marts
-Query performance optimization
-Scalable reporting datasets
(05)
Enterprise Data Validation
Multi-layer validation framework ensures schema consistency, reconciliation accuracy, duplicate prevention, business rule validation, and reporting readiness across all processing stages.
Key Points:
-File and schema validation
-Duplicate detection logic
-Reconciliation workflows
-Business rule verification
-DBT testing framework
-Error and reject handling
(06)
Production Support & SRE
Production-ready monitoring and SRE operations improve observability, reduce downtime, manage incidents, monitor DAG health, and support enterprise SLA compliance.
Key Points:
-Cloud Monitoring integration
-DAG health monitoring
-BigQuery job observability
-Incident response workflows
-SLA tracking and reporting
-Operational reliability management
Results
Project Results
We build scalable mobile and web applications tailored to industry-specific workflows, user expectations, compliance requirements, and long-term business growth.
0
%
Improved reporting readiness and visibility
0
%
Reduction in manual operational effort
0
X
Faster, scalable warehouse processing capability
0
%
Improved monitoring and production observability