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    • Before Using the SMA
      • Supported Platforms
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  • Issue Analysis
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      • General
      • Python
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      • Spark Scala
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      • SQL
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        • Hive
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      • Pandas
        • PNDSPY1001
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        • PNDSPY1003
        • PNDSPY1004
      • DBX
        • SPRKDBX1001
    • Troubleshooting the Output Code
      • Locating Issues
    • Workarounds
    • Deploying the Output Code
  • Translation Reference
    • Translation Reference Overview
    • SIT Tagging
      • SQL statements
    • SQL Embedded code
    • HiveSQL
      • Supported functions
    • Spark SQL
      • Spark SQL DDL
        • Create Table
          • Using
      • Spark SQL DML
        • Merge
        • Select
          • Distinct
          • Values
          • Join
          • Where
          • Group By
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      • Spark SQL Data Types
      • Supported functions
  • Workspace Estimator
    • Overview
    • Getting Started
  • INTERACTIVE ASSESSMENT APPLICATION
    • Overview
    • Installation Guide
  • Support
    • General Troubleshooting
      • How do I give SMA permission to the config folder?
      • Invalid Access Code error on VDI
      • How do I give SMA permission to Documents, Desktop, and Downloads folders?
    • Frequently Asked Questions (FAQ)
      • Using SMA with Jupyter Notebooks
      • How to request an access code
      • Sharing the Output with Snowflake
      • DBC files explode
    • Glossary
    • Contact Us
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On this page
  • Supported Platforms
  • SQL Dialects
  1. User Guide
  2. Before Using the SMA

Supported Platforms

The destination is Snowflake and the source is...

PreviousBefore Using the SMANextSupported Filetypes

Last updated 9 months ago

Supported Platforms

The Snowpark Migration Accelerator (SMA) is currently available for the following source languages:

  • Python

  • Scala

  • SQL

The SMA will scan both code files and notebook files for references to the Spark API and other third party APIs. (See to learn more about which file types can be scanned by the SMA.)

SQL Dialects

The SMA can also scan for SQL Elements present in certain files in a codebase. Currently, the SMA supports SQL written in:

  • Spark SQL

  • Hive QL

Notes on SQL assessment and conversion:

  • Conversion is limited for SQL though there is high compatibility between Spark SQL and Snowflake SQL.

  • SQL can only be analyzed if the code is in a SQL cell in or in a .sql file. SQL statements passed to functions in Python or Scala code will not be analyzed as SQL.

SQL elements are accounted for in the object inventories. Please, refer to the section to take a look at this metric. with more details.


Are you looking for a new platform? Please and let us know.

Supported Filetypes
Readiness Scores
get in touch with us
a supported notebook file