As the client plans to migrate to Google Cloud Platform (GCP), the architecture and processes must accommodate a seamless transition.

Key Considerations for GCP Migration

• Cloud-Native Services:
  Replace current on-premises and hybrid tools with GCP-native services to reduce operational overhead.
• Scalability and Performance:
  GCP’s distributed architecture ensures high availability and the ability to scale dynamically with data growth.
• Incremental Migration Approach:
  Gradually migrate workloads to minimize disruptions.

Current Architecture on GCP

Data Integration and Ingestion
Replace existing ETL/ELT tools with GCP-native services:

• Dataflow:
  For real-time and batch data processing.
• Cloud Data Fusion:
  For building scalable and reusable pipelines with pre-built connectors for SAP, MySQL, FTP, and other sources.
• Pub/Sub:
  For streaming data ingestion from APIs and SAP SLT, enabling real-time data flow.

Centralized Data Warehouse
Migrate the data warehouse to BigQuery, GCP’s serverless, fully managed data warehouse solution:

• Advantages of BigQuery:
  → Automatically scales to handle current (4 TB) and future data growth.
  → Supports ELT workflows with built-in SQL transformation capabilities.
  → Partitioning and clustering improve query performance for large datasets.

Reporting and Analytics

• Power BI on GCP:
  Configure Power BI to use BigQuery as a direct data source for real-time analytics.
  
• Optimization:
  Implement BigQuery BI Engine for low-latency, in-memory analytics to accelerate Power BI and Looker dashboards.

 

Enhanced GCP Architecture Diagram

1. Data Sources:
   SAP-Hana, MySQL, FTP, SAP-SLT, and APIs.
2. Ingestion:
   Cloud Data Fusion, Dataflow, and Pub/Sub.
3. Storage and Processing:
   BigQuery for the data warehouse.
   Cloud Storage for raw data files and archives.
4. Data Quality:
   Informatica IDQ or GCP-native tools like Data Catalog.
5. Reporting:
   Power BI (via BigQuery) and Looker.

 

Expected Benefits with GCP

• Improved Data Processing:
  Dataflow’s scalability reduces processing times for large tables, addressing the 6-hour ETL job issue.
• Simplified Architecture:
  GCP-native tools eliminate the need for staging area duplication and streamline data pipelines.
• Cost Optimization:
  BigQuery’s pay-as-you-go model ensures cost efficiency as data grows.
• Real-Time Insights
  Pub/Sub enables real-time data ingestion for operational analytics.
  Power BI dashboards deliver near real-time insights using BigQuery’s live connections.
• Future-Ready Infrastructure:
  GCP provides a robust foundation for advanced analytics, such as:
  → AI/ML Integration:
      Use Vertex AI for predictive analytics and customer segmentation.
  → Data Lakes:
      Expand into Cloud Storage for unstructured data and long-term archives.

 

Post-Migration KPIs

• ETL Job Duration:
  Reduced from 6 hours to under 1.5 hours using Dataflow and BigQuery transformations.
• Report Loading Times:
  Improved Power BI performance with BigQuery BI Engine, reducing report load times to under 3 seconds.
• Cost Savings:
  20–30% cost reduction in operational expenses compared to on-premises infrastructure.
• Real-Time Insights:
  Achieved sub-minute latency for operational dashboards using Pub/Sub and BigQuery.

 

Next Steps for Migration

1. Proof of Concept:
   Test end-to-end pipelines in GCP for a small subset of data.
2. Stakeholder Training:
   Train teams on GCP tools such as BigQuery, Dataflow, and Looker.
3. Execution Plan:
   Develop a phased migration plan with a clear timeline, resource allocation, and risk mitigation strategies.

This extended solution ensures a seamless transition to GCP while addressing current challenges, paving the way for a scalable, cost-effective, and high-performance data architecture.