It is almost impossible, or very costly to combine all the data in the SAP system itself. Moreover, businesses also need to combine their SAP data with data from other systems used. In most cases, SAP users will need to move their data into a Data Warehouse to use it efficiently.
Snowflake provides SAP users with a platform where they can move their data and information and ensure that it is safe. Using Snowflake, SAP users can move all their data into a single location and analyze it to get a 360-degree view of their business.
Read along to learn more about setting up the Snowflake SAP Integration yourself!
Prerequisites
- An SAP Account.
- A Snowflake Account.
- A Microsoft Account.
Steps to Set Up the Snowflake SAP Integration
This section will show you how to perform the Snowflake SAP Integration using Azure Data Factory. The goal is to load SAP data onto Snowflake in real-time.
To set up the Snowflake SAP Integration, use the following steps:
Step 1: Log in to the Azure portal and open the Azure Data Factory. Create a new pipeline as shown in the below image.
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Step 2: Give the pipeline the name “Copy_SAPTable”. Create a new folder under the “Datasets” section and give it the name “SAP2Snowflake”. Right-click the folder and create a new dataset. Give it the name “Azure Blob Storage”.
Step 3: The new dataset will be shown on the right panel of the Portal as shown in the below image. Select it, that is, Azure Blob Storage, and then choose CSV format type for the dataset. Click the “Continue” button.
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Step 4: On the Set Properties window, enter the name “DelimitedText” and click the “+New” option to link new services. Choose “Azure” under the Integration runtime setup window as shown in the below image and click “Create”. This step will require you to install the SAP .Net Connector on your local computer or Remote desktop.
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Step 5: Provide a name for the Snowflake SAP Integration runtime and fill in the other options. Use the SAS URI Authentication method to connect the Blob Storage. Create a new SAP dataset for the SAP Table connection as shown in the below image.
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Step 6: Click the “+New” linked connection and then fill in the SAP login credentials and run a test connection. Use “Preview Data” to be sure that the SAP connection and data are visible. It should show you the table data. Log into your Snowflake account and create the table structure. Make sure that the snowflake table has the same structure as the SAP table.
Step 7: Click the “Publish All” button located at the top. You will receive a notification of activation. Once the SAP Table has been loaded into Blob storage, check whether the CSV file is available in the Blob storage. Once you confirm that the Blob storage has been connected to Snowflake, use Snowflake as a sink to trigger a pipeline. Verify-in your Snowflake account that the table has been created and the data loaded.
Congratulations! You have completed the Snowflake SAP Integration using Azure Data Factory.
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Benefits of Snowflake SAP Integration
You will enjoy the following benefits by setting up the Snowflake SAP Integration:
1) Simple Architecture
Snowflake’s simple architecture makes it easy for people to use SAP. By putting all SAP data into a single place, it makes SAP data easily accessible and actionable. Snowflake can also ingest both structured and semi-structured data. When the data is centered, SAP users will be able to gain a 360-degree view of their products, customers, and supply chain.
Snowflake is also capable of automatically managing storage in terms of compression, capacity, performance, and statistics. This means that its users don’t have to do housekeeping or indexing. Snowflake offers elastic computational power that is isolated from the other compute clusters that are used by the other teams. Thus the Snowflake SAP Integration will allow the teams can focus on tasks that add value to their company.
2) Reliable Data Security
Snowflake is good for data security. It encrypts the data, whether in storage or in transit, which is a built-in feature of the platform. Data is also stored once and the views are shared out, which translates to one copy, one security model. So, the Snowflake SAP Integration will provide strong security to your SAP data.
3) Convenient Workload Elasticity
The Snowflake platform is very scalable, allowing its users to attach and detach their computational services any time they need. So, the Snowflake SAP Integration provides operational efficiency, convenience, and cost savings. By splitting computation from storage, it allows each of them to scale elastically and independently, making effective use of the Cloud Computing functionality and avoid the need to handle their quirks and complexities. Therefore, the Snowflake SAP Integration makes the work of data handling easy and helps in saving time.
4) Streamlined Use of SAP Data
The Snowflake SAP Integration will enable you to streamlines the process of combining the SAP data with data from other sources. This will ensure that trusted and relevant business content is accessible to all users. Since, Snowflake was designed and developed to handle the full volume, velocity, and different types of data. So, the Snowflake SAP Integration allows the users to load data and generate reports at the same time.
Snowflake also provides its users with a system with virtually no downtime, no weekly upgrades, and no system shutdown to decrease or increase compute. Thus, a Snowflake SAP Integration will enhance the efficiency of performing basic operations on the SAP data.
Conclusion
The article provided a step-by-step explanation of setting up your Snowflake SAP Integration. The method used here is based on the Azure Data Factory tool and can be implemented easily. Also, the article listed down the multiple benefits of moving your data from SAP to Snowflake.
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Skilled in freelance writing within the data industry, Nicholas is passionate about unraveling the complexities of data integration and data analysis through informative content for those delving deeper into these subjects. He has written more than 150+ blogs on databases, processes, and tutorials that help data practitioners solve their day-to-day problems.