Modernizing data systems is crucial for organizations to remain competitive in today’s digital landscape. Transitioning data from legacy systems like SAP to a modern platform like Snowflake is a key aspect of this process.
This blog presents an overview of Hakkoda’s architectural patterns to extract SAP to Snowflake, touching on the benefits, risks, and potential drawbacks of each of the available options.
Comparison of Key SAP Data Extraction Options
There are four main approaches to extracting data from SAP and bringing it into Snowflake. Each of these approaches, in turn, may be more or less suitable for different businesses and use cases. In no particular order, those extraction options include:
- SAP Data Services is a legacy SAP ETL tool that writes data to S3, ADLS, or GCS prior to Snowflake ingestion. This option is highly limited and is scheduled for sunset in 2027. However, for some organizations, it may be the recommendation for businesses that have legacy SAP ETL investments and are looking to undertake a staged data migration.
- BDC is an SAP-managed semantic data product layer that offers governed access and enables data transformation using the SAP data model logic as the foundation. This option is ideal for SAP-centric organizations looking to handle abstraction and governance natively from within SAP’s proprietary ecosystem.
- SNP Glue is a fit-to-purpose commercial tool from the SNP Group that operates natively within the SAP ecosystem. SNP Glue hinges on ABAP-based extraction functionality, writing directly to Snowflake via Snowpipe and SNP’s native Snowflake application. This approach is recommended for SAP ECC/S4HANA customers looking for fast, compliant access.
- Cloud Integration tools like Fivetran, Matillion, Domo, and Snowflake OpenFlow with Kafka offer modern solutions that operate by way of connectors outside the SAP product ecosystem. Typical use cases for this approach include businesses built on generalized data stacks to sidestep vendor lock-in and minimize their technical footprint.
Understanding the Hidden Risks
Each of the extraction options above carries potential challenges. Some of those challenges include:
- SAP Data Services can be costly and difficult to scale, and its closed nature can lead to compliance risks.
- BDC may result in vendor lock-in, limiting integration flexibility.
- SNP Glue requires technical expertise due to the absence of out-of-the-box extractor logic.
- Cloud Integration tools may struggle with semantic layers and necessitate reverse engineering for complex data joins and deltas, increasing risk in intricate data environments.
Hakkōda’s Solutions and Recommendations for SAP Data Extraction
Hakkoda tackles SAP data extraction challenges by developing specialized tools to simplify the process. Our SAP accelerators address semantic model complexities, ensuring efficient data transformation and integration within Snowflake.
This approach allows organizations to seamlessly transition data, enhancing openness and integration capabilities, and supports a more modern, flexible data architecture.
Hakkoda advocates for leveraging SNP Glue or Fivetran to ensure a compliant, agile data architecture. This approach supports reverse-engineered modeling with tools like dbt or Coalesce, integrating SAP data with non-SAP sources.
By focusing on reusable, governed data products, organizations can maintain the flexibility and openness essential for long-term success in a Snowflake-native environment.
Governance Frameworks for SAP-Centric Organizations
For organizations deeply rooted in the SAP ecosystem, adopting a SAP-Centric Governance Framework like BDC offers harmonized data access with robust governance and compliance features.
This approach is particularly advantageous for those utilizing SAP Analytics Cloud (SAC).
However, while BDC provides strong oversight and integration within the SAP environment, it can limit flexibility for broader integration needs.
Organizations should carefully consider these trade-offs in relation to their broader data strategy goals to ensure they align with long-term objectives.
Choosing the Right Extraction Methodology for Your Business
Crafting a data strategy that emphasizes flexibility and openness while eliminating the need for swivel-chair analytics is key for organizations transitioning from SAP to Snowflake.
By addressing semantic model complexities and leveraging hybrid models, companies can achieve efficient data transformation and integration.
Prioritizing reusable and governed data products ensures agility and long-term success in a Snowflake-native environment. Adopting these best practices will enhance data architecture, promoting seamless integration and effective data utilization.
Ready to determine which approach to SAP to Snowflake data extraction makes sense for you? Talk to one of our experts today.
