In today’s competitive business landscape, harnessing the power of data is paramount for making informed decisions and gaining a competitive edge. This case study delves deeper into the intricacies of migrating data from SAP, a widely used enterprise resource planning (ERP) system, to Google Big Query, a powerful data warehousing and analytics platform. By leveraging Google Cloud’s robust ETL tool, Cloud Data Fusion, businesses can seamlessly orchestrate the migration process while ensuring data integrity, accessibility, and actionable insights.
Complexity of SAP Data Extraction: SAP databases are renowned for their complex data structures and vast volumes of transactional data, making extraction challenging.
Near-Real-Time Data Synchronization: Maintaining near-real-time synchronization between SAP and Big Query is crucial for ensuring the timeliness and relevance of analytics insights.
Data Transformation Complexity: Transforming raw SAP data into structured formats suitable for analysis involves complex data transformation processes, including handling historical changes and ensuring data quality.
Data Security and Access Control: Enabling secure access to sensitive data while maintaining data security and integrity poses significant challenges.
Integration with SAP: Cloud Data Fusion seamlessly integrates with SAP using a variety of plugins tailored for SAP environments, including SAP ODP, SAP SLT Replication, and SAP Table Batch Source.
Leveraging SAP SLT Replication, data changes in SAP are captured in real-time and pushed to Google Cloud Storage, facilitating near-real-time synchronization with BigQuery.
This approach ensures that the latest SAP data is readily available for analysis, empowering users with up-to-date insights.
Raw Layer: Initial data from SAP is ingested into Big Query, serving as the raw data layer.
Staging Layer: Data is extracted from the raw layer and transformed using sophisticated data transformation techniques, including store procedures, and implementing Slowly Changing Dimension (SCD) type 2 for capturing historical changes.
DWH (Data Warehousing) Layer: Dimensional and fact tables are created to organize and normalize the data, facilitating efficient analytics and reporting.
Canonical Layer: Denormalized data based on business entities is curated in the canonical layer, enabling external access while maintaining data integrity and security through authorized views.
Looker Integration: Looker connects to the Canonical Layer to extract data for generating comprehensive insights and reports.
Integration with Public Datasets: Integration with public datasets such as COVID and weather data enriches analytics capabilities, providing insights into broader business trends and external factors influencing operations.
Forecast Analysis with AUTOML: Leveraging AutoML capabilities, the solution enables advanced forecast analysis of inventories, empowering businesses with predictive analytics capabilities.
Google Cloud’s monitoring tools and Google Composer’s built-in capabilities are utilized for monitoring and reporting, ensuring real-time visibility into the migration process.
The solution is designed with scalability in mind, allowing it to seamlessly adapt to changing data volumes and evolving business requirements.
We are establishing a streamlined process for migrating data from a source database (SAP) to Google Big Query, with a primary focus on leveraging the GCP ETL tool, Cloud Data Fusion. The initial phase involves integrating Cloud Data Fusion with SAP, utilizing several available plugins within the GCP ecosystem:
Among these plugins, we have employed SAP ODP, SAP SLT Replication, and SAP Table Batch Source to extract data from SAP. The SAP ODP plugin utilizes CDS views as data sources, offering the option to enable delta settings in SAP for incremental loading.
SAP SLT Replication extracts data from SAP, transmitting it to Google Storage. We utilize Cloud Data Fusion to extract data from Google Storage and load it into Big Query, enabling near-real-time synchronization of SAP data with Big Query.
In the SLT replication process, we identify necessary tables for the business and configure the replication in SAP, facilitating data synchronization to Google Storage using the SLT replication plugin.
Data loaded from the SAP plugin into GCP constitutes the raw layer. From this layer, data is extracted and loaded into the staging layer via stored procedures, implementing SCD type 2 to accommodate historical changes.
Data is further extracted from the staging layer and loaded into the Data Warehouse (DWH) layer, where dimension and fact tables are created, normalizing the data.
For external access, we establish the Canonical Layer, deformalizing based on entity and creating authorized views for database access, exposed to Looker. Looker connects to the Canonical Layer to extract data and generate insights, incorporating SAP data and public data sources such as COVID, weather, and logistics to illustrate delivery processes and inventory forecast analyses.
The Canonical Layer also facilitates inventory forecast analysis using AUTOML.
Efficient Data Migration: The streamlined migration process ensures efficient extraction, transformation, and loading (ETL) of SAP data into Google Big Query, minimizing downtime and maximizing data availability.
Enhanced Analytics Capabilities: By integrating with public datasets and leveraging advanced analytics tools, the solution provides actionable insights for strategic decision-making, driving business growth and innovation.
Improved Data Security and Access Control: Controlled access to data through authorized views and robust security measures ensures data integrity, compliance, and user privacy.
Scalability and Flexibility: The solution’s scalable architecture allows it to adapt seamlessly to evolving business needs and accommodate fluctuations in data volumes, future-proofing data management and analytics capabilities.
By connecting the combined capabilities of Cloud Data Fusion, Google Big Query, and other Google Cloud services, organizations can overcome the complexities of SAP data migration, unlock the full potential of their data assets, and drive digital transformation in today’s data-driven world. This case study showcases the effectiveness of leveraging cloud-native solutions for seamless data integration, transformation, and analytics, ultimately empowering businesses to thrive in the era of data-driven decision-making.