Migrate PySpark (Spark Python)
to Snowflake

Move from PySpark to Snowflake seamlessly with Mobilize.Net.

Mobilize.Net has been performing code migrations since before the start of the millennium, and migrating legacy data warehouses has been no small part of that. But our specialty is code. If you can think of a codebase, there's a good chance we've worked with it. Bringing code beyond SQL to run in Snowflake is one of the purposes of Snowflake's launch of the Snowpark API. With Snowpark, you can take Scala, Python, and Java, and run it directly in Snowflake, effectively eliminating the gap between your cloud data and your scalable cloud analytics. As Snowflake supports additional codebases, Mobilize.Net's migration technology continues to be the most effective code translation tooling in existence.

But what if you're already running on Spark? There are a lot of reasons to choose Snowpark in Snowflake (single platform, scalability, security, low-maintenance, and others), and it's very simple to convert your Spark jobs to Snowpark by utilizing SnowConvert. 

Trust and Experience

Mobilize.Net has been building tools to accelerate modernization projects for more than a quarter century. SnowConvert is the product of all that experience applied towards migrating to the Data Cloud, regardless of whether you have SQL, Python, or any other code language. With more lines of code run through it than any other available tool, SnowConvert has seen, understood, and converted it all.

You can get an assessment on your PySpark code for free today. Snowflake and has created the Spark to Snowpark Migration Guide, and you can use the link below to learn more about how code understanding with SnowConvert for PySpark is an essential part of building any successful database migration strategy.

More than just Glorified
Find-and-Replace Translation

Mobilize.Net is not in the business of creating glorified regex or find-and-replace tools. SnowConvert builds a complete understanding of each workload analyzed by the tool, and creates a full symbol table and a comprehensive abstract syntax tree for each run of the tool. As a result, the output code is designed to be functionally equivalent with the input. In other words, what your code "does" in PySpark is what you can expect it to "do" with Snowpark in Snowflake by using SnowConvert.

If you'd like to learn more about the kind of transformations that SnowConvert can apply, you can visit our public documentation page or reach out to get access to our complete translation reference library.

And if you're looking for a partner to help you complete your migration beyond running a tool, we do that too.