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.