A Complete Guide to Loading Data SAP to Snowflake

SAP to Snowflake

SAP to Snowflake – SAP as a data storage repository and database management system is preferred by most organizations today. However, the downside is that the transactional nature of SAP often leads to delays in execution, especially in recent times, due to the growth of data-driven applications. The main concerns are access to data stored in SAP regarding who gets to the SAP data and for whom this facility is blocked. 

One way to get around this issue is to move data from SAP to data warehouses that are based in the cloud such as Google BigQuery, Azure Synapse, Amazon Redshift, and Snowflake. The migration ensures that data can be replicated in multiple locations. The most optimized solution in this regard is moving databases from SAP to Snowflake, a process that assures stringent data security, very necessary in the present business environment.

Why Should Data be Moved from SAP to Snowflake

Moving data from SAP to Snowflake has several benefits. Here are a few of them.

  • The most critical advantage of moving data to Snowflake is that this cloud-based data warehousing solution provides fully-managed automated services such as data storage, compression, and high performance. Hence, businesses do not have to build indexes or carry out any internal changes.
  • Data from SAP or other third-party applications can be processed by Snowflake in its native format, regardless of whether it has an unstructured, semi-structured, or structured form. This is also possible when changes are made in the structure of the data files.
  • SAP data can be processed easily and seamlessly on Snowflake because of the simple structure of this cloud-based platform. SAP customers are provided single-window access to actionable data, helping businesses to follow FAIR (findable, accessible, interoperable, reusable) principles. 
  • By migrating data from SAP to Snowflake, users get authentic and credible business content that enables them to carry out multiple intricate queries, report generation, and data loading.  
  • Snowflake provides scalable data storage capabilities. It is possible to quickly scale up if required from 10GB to 30PB and then come down to 10GB again when the peak demand is over. Most critically, payment for storage is as per resources used and not flat fees like traditional databases where charges are levied regardless of capacities used. Being on Snowflake, therefore, leads to substantial savings. 

For continuous movement of data from SAP to Snowflake, it is necessary to keep them always in sync.  

Data Movement from SAP to Snowflake

Data Movement from SAP to Snowflake takes place over several stages. Here is a complete rundown of the various steps.

# Considerations Before Moving Data From SAP to Snowflake

The first stage is to decide which parameters have to be taken into account before moving data to Snowflake. These are generally the following.

  • The tables and databases
  • Who will have access to the tables and databases – users and applications.
  • Intervals for updating data to the tables.
  • The expected usage patterns of the migrated data in Snowflake

After these aspects are finalized the level of support and inputs required for data movement from SAP to Snowflakecan be initiated.  

# Prepare a Migration Blueprint

A blueprint has to be now prepared for the database migration and all the parameters as detailed above integrated into it. Start in a phased manner. Complete the movement first of low-impact databases, applications, and tables before taking up more complex tasks. What has to be kept in mind though is that regardless of the method adopted, the SAP data and Snowflake should be in sync when the process is completed.

What goes into the execution plan?

  • Study the results of the previous analysis. Break down the tables and databases into rational steps beginning with the tables requiring minimum changes and having a low impact on business operations.
  • Data movement, consumption, and end-to-end data ingestion should be concurrent as it will help you to identify any problems quickly at every step.  
  • Use optimized and automated tools that can speed up the movement of data from SAP to Snowflake.The advantage here is that the major part of the complex re-tooling and syncing activity can be done without any human intervention.         

# Create Snowflake and HANA Account

After the blueprint for executing SAP to Snowflake data movement is ready, set up HANA and Snowflake accounts. Create the following by configuring Snowflake with the appropriate UI/CLI.

  • Databases and warehouses on Snowflake
  • Users and accounts on Snowflake.

# Design SAP DATA Extractor and Build Snowflake Tables

Since SAP supports connections through ODBC/JDBC drivers and APIs, users can write their preferred code to extract SAP data. While extracting data, make sure that all custom fields are extracted and type information preserved so that tables can be easily created later in Snowflake. Avoid CSVs by using a typed format instead JSON/AVRO formats to store data.

The extracted data has to be used now to create Snowflake tables. Bring Snowflake field types in sync with the SAP field types and map the two. It is very easy if you have a typed format from the previous step. However, you have to rename the columns that do not match the column naming norms of Snowflake.

# Loading SAP Data to Snowflake

The final step is loading the files created in previous steps into Snowflake with the COPY command for bulk files. Integrate a scheduler in the process to seamlessly run the steps. Finally, automate the process to incorporate all changed data periodically. Alternately, use deltas to load the data by taking a snapshot of the data once from SAP and moving it to Snowflake, so that subsequently only the deltas have to be loaded into Snowflake.

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