Does ‘Lift and Shift’ suffice for SAP HANA to Snowflake Migration? This question looms large for organizations contemplating the switch from legacy systems like SAP to modern cloud platforms such as Snowflake. While the transition from SAP HANA to Snowflake is a strategic move to enhance data capabilities, the popular 'lift and shift' approach, known for its speed and simplicity, may not be sufficient. The complexity of SAP demands more than just a quick transfer to Snowflake.
Key Considerations to “lift and shift” SAP HANA to Snowflake
The "lift and shift" strategy hinges on transferring existing applications and data to a new platform with minimal changes. Simply put, it offers a quick route to transition from SAP HANA to snowflake while minimizing disruption to ongoing activities and delivering a swift return on investment. However, with a simple lift and shift approach, you'll bypass many of Snowflake's advanced features and miss out on its full potential. But, with some smart adjustments during the move, you may realize Snowflake's full potential, surpassing SAP HANA/BW's known but limited capabilities. To achieve this, it's crucial to consider several key factors in your transition strategy.
The "lift and shift" strategy, which involves transferring existing applications and data to a new platform with minimal changes, offers a quick route to transition while minimizing disruption and potentially delivering a swift return on investment.
However, this approach comes with significant trade-offs:
The Need for SAP Domain Expertise
Even within a lift and shift framework, the complexities of SAP's data structures and business logic call for the participation of SAP Domain Subject Matter Experts (SMEs). SAP Domain SMEs are critical in organizations for employing their extensive SAP knowledge to craft efficient queries. They understand SAP's structure, enabling them to navigate databases and write precise SQL queries. These SAP Domain SMEs often have expertise in SAP data analytics, enabling them to extract meaningful insights from complex SAP systems. They interpret business needs, optimize performance, and ensure alignment with goals. Additionally, they improve communication between technical and business teams by converting requests into actionable queries. Their ability to bridge technical and business aspects makes them important in realizing SAP's full potential through query formulation and data interpretation.
Differences in SAP BW/Hana and Snowflake Architectures
SAP BW, HANA and Snowflake have different architectures. While BW is an OLAP (Online Analytical Processing) system optimized for SAP ERP data, HANA is a columnar in-memory relational database. Snowflake, on the other hand, is a cloud-native data warehouse that enables scalability and flexibility across a wide range of data types and sources. Migrating to Snowflake without understanding its architecture, limits its potential benefits.
Identifying Optimization Opportunities
Snowflake's architecture offers numerous advantages, including flexible schema designs, automatic scaling for enhanced performance, and superior handling of semi-structured data. However, migrating BW/HANA models 'as-is' overlooks opportunities to better utilize Snowflake's unique capabilities. By identifying and implementing optimizations, even incrementally, organizations can achieve significant long-term gains in both performance and cost-efficiency.
Handling Complex Data Shifts
SAP BW/HANA models often include complex transformations, data flows, and business logic embedded within the system from the requirements stage to that of rework. Mapping these intricacies to Snowflake's SQL-based transformations and ELT (Extract, Load, Transform) processes requires careful planning and execution. Ensuring data integrity and accuracy throughout the transfer is critical to preserving the reliability and value of the migrated data.
Expanding Data Integration
Snowflake excels at integrating diverse data sources, extending far beyond traditional SAP environments and enhancing SAP data analytics capabilities. Access to expert SAP data services can help organizations fully utilize these integration features. Failing to consider these broader integration capabilities when migrating SAP models may significantly limit the potential benefits of the Snowflake platform.
Keeping Costs in check
Running legacy SAP models on Snowflake without optimization could lead to higher costs due to inefficient queries and data storage. Proactive measures such as monitoring usage patterns, optimizing query performance, and utilizing Snowflake's built-in cost-control features can help optimize performance and also control costs.
Automation advantage of SAP HANA to Snowflake
SAP HANA to snowflake migration is complex, demanding significant effort and expertise. However, there's potential to derive even greater value from this exercise.
What if you could drastically reduce the time and effort spent on migration? Think automation. It accelerates data ingestion and cleaning while enhancing metadata quality and consistency - critical for enterprise data management. High-quality metadata improves data governance and discoverability, laying the foundation for advanced analytics and Generative AI applications. This approach unlocks new possibilities for AI-driven insights, redefining your entire data strategy.
What's Next ?
The potential of automation in this process is vast. Leveraging expert SAP data services can further enhance this automation process, ensuring a smooth transition while maintaining data integrity. In our upcoming blog posts, we'll explore how this enhanced approach can drive sustained value and maximize returns in SAP to Snowflake transitions, turning possibilities into tangible benefits for your organization.
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