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    • Before Using the SMA
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  • Issue Analysis
    • Approach
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    • Issue Codes by Source
      • General
      • Python
        • SPRKPY1000
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        • SPRKPY1059
        • SPRKPY1060
        • SPRKPY1061
        • SPRKPY1062
        • SPRKPY1063
        • SPRKPY1064
        • SPRKPY1065
        • SPRKPY1066
        • SPRKPY1067
        • SPRKPY1068
        • SPRKPY1069
        • SPRKPY1070
        • SPRKPY1071
        • SPRKPY1072
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      • Spark Scala
        • SPRKSCL1000
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        • SPRKSCL1111
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        • SPRKSCL1119
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      • SQL
        • SparkSQL
          • SPRKSPSQL1001
          • SPRKSPSQL1002
          • SPRKSPSQL1003
          • SPRKSPSQL1004
          • SPRKSPSQL1005
          • SPRKSPSQL1006
        • Hive
          • SPRKHVSQL1001
          • SPRKHVSQL1002
          • SPRKHVSQL1003
          • SPRKHVSQL1004
          • SPRKHVSQL1005
          • SPRKHVSQL1006
      • Pandas
        • PNDSPY1001
        • PNDSPY1002
        • 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
          • Union
      • 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
  • Purpose
  • Statements identified
  1. Translation Reference

SIT Tagging

Purpose

As part of the Internal Consumption Tracking, different elements processed by the Snowpark Migration Accelerator (SMA) will be identified by a comment in JSON format, this one will contain the following information:

  • A version of the core processor.

  • Language of the current migration (HiveSQL, SparkSQL, Scala, Python).

  • Name of the tool (it is SMA by default).

Statements identified

On the subsections you will find the statements tagged by each one of the languages.

PreviousTranslation Reference OverviewNextSQL statements

Last updated 10 months ago