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SAP HANA to Snowflake Migration (Detailed Guide)

Honestly speaking, data warehouse migrations can be quite a colossal undertaking. Hence, no matter how experienced you are in the market, it’s a must that you don’t take it lightly.

However, if you have found this article, it means that your company has weighed in all of the options and gone for a scalable-cloud solution. So, I commend you on that.

Nonetheless, if you’ve been relying on SAP structure and excellence since the beginning, it’ll dishearten you a little when moving to Snowflake. But there’s no need to worry.


Let’s find out more about Snowflake before getting into any other topic.

What is Snowflake?

Snowflake, in essence, focuses on offering a specific platform to an SAP users where they may move their data and ensure its safety. By using the same, you can migrate all of your data into a single location and evaluate it accordingly. This, in turn, can proffer them with a 360-degree view of the business and gain more insights accordingly.

Snowflake can also get scalable to cater to your ever-so-increasing storage needs. With it, you can combine all of your SAP data with information from another system in one warehouse.

Due to this reason, Snowflake SAP Integration can be of great importance for a business. But it’s not really easy to go through the same, especially for someone who hasn’t done it before.

In this article, we have shared everything that you need to do to perform integration into the Snowflake ecosystem. Hopefully, that will help you out in your endeavor.

The Prerequisites

There isn’t anything too much that you may require to move to Snowflake from SAP HANA. In my opinion, having these three things will be more than enough for your purpose –

  • An active SAP account.
  • A Microsoft account.
  • A Snowflake account.

In addition to this, you will also need to have a clear idea about what SAP and Snowflake are. It might be needed later on when you have started working with both.

How Do You Set Up the Integration?

As mentioned before, setting up a cloud data warehouse can be quite complex. Therefore, I’ve broken the procedure into different stages accordingly. 

Step – 1: Analyze – What Do You Need to Get to Snowflake?

Unlike an SAP infrastructure, you simply can’t start working or integrating into Snowflake out of nowhere. It should be done once you have asked and answered the following questions –

  • Which tables and databases should be made available at the beginning?
  • How often do you update the data in tables?
  • How are you loading the data into these tables? Are you using a specific application or script? Or is there something else that you need to know about?
  • Which roles, users, and applications currently have access to the tables and databases?
  • What are the common consumption patterns of this data?

Document all of your answers to these questions and use them to assess the level of information or data to support these inputs accordingly. Using it in a Q&A process can be possible too.

Step – 2: Create a Low-Level Execution or Design Plan

Once you have documented the requirements for your purpose, it’s time for you to create a thorough plan for your project. If you are well-versed in the process, it might feel tempting to go for an all-at-once method. However, if you are new, go for a phased approach instead.

In this case, you will need to move the low-impact databases, tables, and applications first and then focus on the complicated syncing tasks. 

Nonetheless, no matter what method you’re going for, you’ll have a proper plan ready at your base at the end of this step. Here are some tips that might be helpful in your case –

  • Take the output from your previous analysis step and divide up the tables into logical phases. The first segment should include the databases that require minimal changes or don’t affect your business needs and requirements at all.
  • It’s always ideal for planning a complete vertical slice – such as data movement, end-to-end ingestion, and consumption – together. This, in turn, can help an individual isolate the concerning issues at the earliest.
  • Don’t forget to identify tools that may help speed up the entire process accordingly. Be sure to remember – it’s not recommended if you’re using a hand code.
  • You may consider trying out a DataOps tool for executing the process more quickly. It may reduce the overall time required by automating a portion of retooling and syncing.

Step – 3: Create a Snowflake and a HANA Account

With a proper plan of action in hand, your next step should be trying to execute it quickly and efficiently. And for that, you will need to create a HANA and a Snowflake account first. After you are done, use the Snowflake CLI/UI to configure the following aspects of Snowflake –

  • Create users and the accounts you want to use on Snowflake.
  • Curate warehouses and databases on Snowflake.

Step – 4: Develop a Data Extractor from SAP

Unlike any other similar system, SAP supports connection through JDBC/ODBC drivers and APIs. Hence, with it, you can write codes by using your favorite programming language while extracting data from the platform. However, here’s the catch.

When you are extracting data from SAP, it will be best if all of the custom fields are extracted and you preserve the type information as type information. This, in turn, can help you create a new table or user in Snowflake later on. 

It’s ideal to use a typed format to keep all of these data, as it’s safer and more effective. Hence it’s best to avoid CSVs as much as you can and use the AVRO/JSON format instead.

Step – 5: Create a Snowflake Table

This step tends to involve the process of creating a Snowflake table for the data you extracted previously. It’s always best to map the entire SAP field types to the field types available in the Snowflake. Having a proper typed format in the previous step can make it really easy too.

However, if you find a column not adhering to the column naming convention of Snowflake, make sure to rename them accordingly. 

Note: Once you are done with the previous step, it might be best to use the Snowflake COPY command right away. It can help you bulk-load a lot of files that were created in the previous step. However, make sure to integrate a proper scheduler to run steps on the desired frequency and ensure that the entire process is ending properly.

Bonus Step – Optimizations

So, are you done with loading all of your data into Snowflake? Well, then, it’s time for you to optimize the entire process accordingly. But how can you save time and automate it?

What if it was possible to update the only things that you have changed? 

Is there any way to make it easier for more apps to access your SAP environment? 

Let’s find out.

APIs to Snowflake

Providing access to an SAP environment can definitely be a reason to hesitate a little. But on the other hand, offering access to the Snowflake platform is much easier. With it, you will be able to offer access to other applications and share the tables you have created with them.

Delta Loads

One solution, in this aspect, is using deltas to load all of your data. If you want, you will be able to snapshot data once from the SAP environment and load the same into Snowflake.

Hence, you will only have to load the deltas if you want to go forward with it. That’ll require you to remember the very last loaded row from SAP HANA. 

But, using the COPY command will be enough in this aspect.

Is It Possible to Use a Tool?


Like any other SAP-related work, this one can be performed through a tool or two too. This, in turn, can help you automate a lot of work and ensure that you are completing your project within a short amount of time. However, before you choose a tool, make sure to talk to an expert accordingly. They’ll let you know what you should use and how you need to use it.