Enterprise Cloud Migration & Common Data Federation Platform
A cloud-native enterprise manufacturing analytics ecosystem built using Databricks, Delta Live Tables, PySpark, BigQuery, and GCP services to unify enterprise data, modernize processing pipelines, improve observability, and enable scalable analytics.
Enterprise Cloud Migration & Common Data Federation Platform
Databricks + GCP + PySpark + BigQuery + Observability Case Study
Revuteck delivered an enterprise-grade cloud migration and Common Data Federation solution by modernizing fragmented manufacturing analytics workloads into a scalable Databricks and BigQuery-based cloud ecosystem.
The solution included Delta Live Tables frameworks, distributed PySpark processing, streaming pipelines, BigQuery analytical layers, federation modeling, ABC reconciliation frameworks, centralized observability, production support operations, and SRE-driven reliability practices.
Source basis: Enterprise manufacturing cloud modernization project using Databricks, Delta Live Tables, PySpark, Spark Structured Streaming, BigQuery, Cloud Functions, GitLab, centralized logging, Common Data Federation, ABC frameworks, production support, and observability-driven operations.
Business Required :
The manufacturing enterprise operated with multiple disconnected analytical systems spread across ERP platforms, operational applications, legacy databases, analytical stores, and departmental reporting environments.
The fragmented ecosystem created inconsistent reporting logic, operational inefficiencies, limited observability, scalability challenges, and increasing production support complexity.
The business required a modern enterprise cloud platform capable of:
Centralizing enterprise manufacturing data
Supporting scalable distributed processing
Enabling real-time and batch analytics
Standardizing enterprise reporting models
Improving operational visibility and monitoring
Strengthening reconciliation and governance
Supporting production-ready reliability
Building a scalable analytical foundation for future growth
Solution Summary
The solution modernized the enterprise manufacturing analytics ecosystem by introducing a scalable cloud-native architecture where:
Enterprise source systems landed data into GCP Cloud Storage
Databricks and Delta Live Tables handled scalable, distributed processing
PySpark pipelines performed cleansing, enrichment, federation, and transformation logic
Spark Structured Streaming enabled near real-time analytical processing
BigQuery served as the enterprise analytical warehouse platform
Common Data Federation standardized shared business entities
ABC reconciliation frameworks improved trusted analytics delivery
Log Explorer centralized operational observability and troubleshooting
Production support workflows improved incident management and operational reliability
SRE operational practices enhanced observability, recoverability, and SLA compliance
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 enterprise ecosystem struggled with fragmented legacy systems, siloed reporting structures, inconsistent transformation logic, scalability limitations, weak observability, operational inefficiencies, and increasing production support overhead.
Focus Areas
-Enterprise cloud migration
-Common Data Federation architecture
-Scalable Databricks processing
-Real-time and batch pipeline modernization
-Enterprise data governance
-Centralized observability and monitoring
-Production support and SRE reliability
Project Scope
The project included enterprise source onboarding, cloud storage implementation, Databricks and DLT pipeline development, PySpark transformation frameworks, BigQuery warehouse modernization, federation modeling, ABC reconciliation frameworks, monitoring implementation, production support workflows, and SRE-driven operational reliability.
Development Approach
The engineering phase focused on scalable PySpark processing, Delta Live Tables orchestration, federated business modeling, reusable transformation frameworks, streaming optimization, operational observability, and enterprise-grade support reliability.
Key Research Areas
-Enterprise migration strategy
-Databricks performance optimization
-DLT layered pipeline architecture
-Common Data Federation modeling
-BigQuery analytical optimization
-ABC reconciliation strategy
-SLA-driven operational support
Solution Provided
A layered cloud-native architecture was designed to separate ingestion, storage, streaming, processing, federation, warehousing, reporting, monitoring, and operational support layers for better scalability, maintainability, governance, and observability.
Architecture Goals
-Centralize enterprise manufacturing data
-Support scalable distributed processing
-Enable batch and streaming analytics
-Standardize transformation frameworks
-Improve observability and monitoring
-Strengthen reconciliation and governance
-Enable production-ready reliability
-Support future enterprise analytics expansion
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 & Current-State
Analyzed legacy manufacturing platforms, reviewed enterprise data dependencies, identified reporting gaps, documented operational challenges, and gathered modernization requirements for enterprise cloud migration planning.
Key Activities:
-Legacy platform assessment
-Enterprise source analysis
-Reporting dependency mapping
-Operational workflow review
-Data quality assessment
-Migration roadmap planning
-Risk and impact analysis
Cloud-Native Architecture
Designed a scalable GCP + Databricks + DLT + BigQuery cloud-native architecture with separate ingestion, federation, warehousing, reporting, observability, and operational support layers.
Key Activities:
-GCP architecture planning
-Databricks platform design
-Delta Live Tables framework planning
-Federation model design
-Security and governance setup
-Monitoring architecture implementation
-Streaming strategy definition
Cloud Storage Landing Layer
Configured GCP Cloud Storage landing, bronze, silver, gold, archive, reject, audit, and reprocessing zones to support scalable enterprise ingestion and operational traceability.
Key Activities:
-Cloud Storage bucket setup
-Landing zone configuration
-Archive and reject handling
-Reprocessing workflow setup
-Audit logging structure
-File partition strategy
-Secure storage governance
Databricks & DLT Development
Implemented Databricks PySpark pipelines and Delta Live Tables workflows for ingestion, cleansing, federation logic, standardization, CDC handling, enrichment, and scalable distributed processing.
Key Activities:
-PySpark pipeline development
-DLT bronze-silver-gold implementation
-Cleansing and enrichment logic
-Incremental processing setup
-CDC workflow implementation
-Federation rule integration
-Audit metric generation
Streaming Pipeline
Developed Spark Structured Streaming pipelines for near real-time enterprise data processing, validation, standardization, and analytical publishing workflows.
Key Activities:
-Streaming workflow development
-Micro-batch processing setup
-Stream validation logic
-Real-time transformation handling
-Streaming observability
-Error and retry workflows
-Curated stream publishing
BigQuery Warehouse
Created BigQuery raw, bronze, silver, gold, curated, and audit datasets with optimized analytical structures, KPI models, reporting marts, and enterprise-scale warehouse optimization.
Key Activities:
-BigQuery dataset creation
-Curated analytical modeling
-KPI dataset implementation
-Reporting mart preparation
-Warehouse optimization setup
-Partitioning and clustering
-Audit dataset integration
Data Federation & ABC Framework
Implemented Common Data Federation models and ABC reconciliation frameworks to standardize enterprise entities, validate cross-domain consistency, and improve trusted analytics delivery.
Key Activities:
-Shared entity modeling
-Federation rule implementation
-Cross-domain standardization
-ABC reconciliation workflows
-Duplicate detection logic
-Business rule validation
-Reporting consistency checks
Observability, Production Support & SRE
Implemented centralized logging, monitoring dashboards, SLA tracking, incident management workflows, RCA processes, and SRE-driven operational reliability practices.
Key Activities:
-Log Explorer integration
-Databricks monitoring setup
-DLT observability implementation
-Incident response workflows
-SLA tracking setup
-Root cause analysis
-Operational support documentation
Features
Key Features
We build scalable mobile and web applications tailored to industry-specific workflows, user expectations, compliance requirements, and long-term business growth.
(01)
Common Data Federation Platform
A centralized enterprise federation layer designed to standardize manufacturing business entities, unify cross-domain analytics, and deliver governed enterprise-wide reporting datasets.
Key Points:
-Unified enterprise business entities
-Cross-domain analytics enablement
-Shared reference models
-Federated reporting structures
-Centralized governance framework
-Enterprise-wide analytical consistency
(02)
Delta Live Tables Framework
DLT bronze, silver, and gold pipelines automate ingestion, cleansing, transformation, enrichment, incremental processing, and publishing of enterprise manufacturing datasets.
Key Points:
-Bronze-silver-gold architecture
-Automated transformation pipelines
-Incremental processing support
-CDC handling workflows
-Scalable orchestration logic
-Pipeline observability integration
(03)
Databricks Distributed Processing Engine
Databricks and PySpark provide scalable, distributed enterprise processing for high-volume manufacturing workloads, streaming ingestion, cleansing, enrichment, and transformation logic.
,Key Points:
-Distributed PySpark processing
-High-volume data scalability
-Streaming and batch processing
-Cleansing and enrichment workflows
-Federation rule execution
-Scalable enterprise transformation
(04)
Enterprise Data Validation & ABC Framework
Multi-layer validation and ABC reconciliation frameworks ensure data quality, consistency, reconciliation accuracy, audit traceability, and trusted enterprise analytics.
Key Points:
-Source-to-target reconciliation
-Duplicate detection logic
-ABC validation framework
-CDC validation checks
-Business rule verification
-Reporting validation workflows
(05)
BigQuery Enterprise Analytics Warehouse
Scalable BigQuery analytical architecture supports raw, bronze, silver, gold, curated, KPI, and audit layers optimized for manufacturing analytics and enterprise reporting.
Key Points:
-Multi-layer analytical datasets
-Curated reporting marts
-KPI and metrics modeling
-Partitioned warehouse optimization
-Enterprise reporting enablement
-Query performance optimization
(06)
Observability, Production Support & SRE
Production-ready observability and SRE operations improve reliability, reduce downtime, centralize operational monitoring, and support enterprise SLA compliance.
Key Points:
-Centralized Log Explorer monitoring
-Databricks job observability
-DLT monitoring integration
-SLA tracking and reporting
-Incident response workflows
-RCA and operational reliability
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 enterprise reporting consistency
0
%
Reduction in manual operational effort
0
X
Faster scalable distributed processing capability
0
%
Improved observability and operational visibility
Enterprise Cloud Migration & Common Data
Admin
Client Review
“Revuteck successfully modernized our fragmented manufacturing analytics ecosystem into a scalable and governed cloud-native data federation platform using Databricks, DLT, PySpark, and BigQuery. The solution improved enterprise visibility, operational reliability, observability, and long-term analytical scalability.”
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