SnowConvert AI CLI - Getting Started¶

What is SnowConvert AI CLI?¶

SnowConvert AI CLI (scai) encapsulates all SnowConvert functions into a single command line tool dedicated to increasing the speed of migrations from various source platforms into Snowflake.

With the SnowConvert AI CLI, migration engineers can extract code from their source platform, run a deterministic conversion on that code, further advance their migration using ai-conversion to cover objects that the deterministic engine could not translate, deploy that code to snowflake, migrate data from the source system to snowflake, and then validate that data between the two systems.

The CLI will also allow developers to create skills and agents that utilize the tool to automate their process.

• 1 Prerequisites

  • 1.1 Snowflake Connection Setup

• 2 Installation

  • 2.1 Package Manager (macOS only)

  • 2.2 Installer packages (macOS, Linux, Windows)

• 3 Understanding Projects

• 4 Quick Start: Code Conversion Only

• 5 Quick Start: End-to-End Migration

• 6 Workflow Examples

• 7 Getting Help

• 8 Troubleshooting

• 9 Supported Source Dialects

Prerequisites¶

• macOS, Windows, or Linux

• Snowflake CLI: recommended for Snowflake connection configuration (install guide)

Snowflake Connection Setup¶

The SnowConvert AI CLI reuses your Snowflake CLI connection configuration.

Before using scai init, scai code convert, scai code extract, scai ai-convert or scai code deploy, ensure:

• You can connect to Snowflake with snow connection test

• Your Snowflake CLI has a default connection configured (the one used when you don’t specify a connection name)

To configure a Snowflake connection:

# Add a new connection using Snowflake CLI

snow connection add

# Test your connection

snow connection test

Once configured, scai code deploy will use your Snowflake CLI connections automatically.

Installation¶

brew tap snowflakedb/snowconvert-ai
brew install --cask snowconvert-ai

scai --version

brew update && brew upgrade
brew untap snowflakedb/snowconvert-ai
brew tap snowflakedb/snowconvert-ai
brew install --cask snowconvert-ai

Installer packages (macOS, Linux, Windows)¶

Understanding Projects¶

A project is required before you can use any other scai command. This is similar to how Git requires you to run git init before using other Git commands.

A project:

• Organizes your migration work in a dedicated folder structure

• Tracks your source dialect (Oracle, SQL Server, Teradata, etc.)

• Stores configuration, source code, converted code, and reports

When you run scai init, it creates this folder structure in the target directory.

• If you pass a PATH, scai will create that folder (if it doesn’t exist) and initialize the project inside it.

• If you omit PATH, scai initializes the current directory (which must be empty).

.

├── .scai/ \# Project configuration
│ └── config/ \# project.yml and local settings
├── source/ \# Your source code (input)
├── converted/ \# Converted Snowflake code (output)
└── results/ \# Additional job artifacts (conversion reports are under
converted/Reports)

Important: After creating a project, run all subsequent scai commands from within the project folder (where the .scai/ directory exists).

CLI logs are written to ~/.scai/logs/jobs.log by default.

Quick Start: Code Conversion Only¶

Use this workflow when you have existing SQL files to convert. Works with all supported dialects.

# 1. Create a project folder and initialize it (project name is inferred from folder name)

scai init my-project -l Oracle
cd my-project

# 2. Add your source code

scai code add -i /path/to/your/sql/files

# 3. Convert to Snowflake SQL

scai code convert

# 4. Deploy to Snowflake

scai code deploy

Your converted code will be in the converted/ folder (including converted/Output/SnowConvert/), and conversion reports in converted/Reports/.

Quick Start: End-to-End Migration¶

Use this workflow to extract code directly from your source database. Only available for SQL Server and Redshift.

scai init my-project -l SqlServer \# or Redshift
cd my-project

scai connection add-sql-server

scai connection add-redshift

scai connection set-default -l sqlserver --connection \<NAME\>

scai connection set-default -l redshift --connection \<NAME\>

scai code extract

scai code convert

scai code deploy

Workflow Examples¶

# Create project

scai init oracle-migration -l Oracle
cd oracle-migration

# Add your PL/SQL files

scai code add -i ./oracle-procs/

# Convert

scai code convert

# Review converted code in converted/ folder, then deploy

scai code deploy --all

# Create project

scai init sqlserver-migration -l SqlServer

cd sqlserver-migration

# Add connection

scai connection add-sql-server

# Extract only the 'sales' schema

scai code extract --schema sales

# Convert

scai code convert
\# Deploy

scai code deploy
```Shell
### Example 3: Redshift Migration with Git Tracking

# Create project with git workflow

scai init redshift-migration -l Redshift --git-flow

cd redshift-migration

# Add connection

scai connection add-redshift

# Extract

scai code extract

# Convert (changes are tracked in git)

scai code convert

# Deploy

scai code deploy --all

# Create project

scai init ai-convert-demo -l SqlServer

cd ai-convert-demo

# Add connection

scai connection add-sql-server

# Extract and convert

scai code extract

scai code convert

# Start AI code conversion and wait for completion

scai ai-convert start -w

# Review last executed job results

scai ai-convert status

# Review executed job list

scai ai-convert list

Getting Help¶

Use –help with any command to see available options:

scai --help

scai init --help

scai code convert --help

scai connection add-redshift --help

Troubleshooting¶

cd \<project-folder\>

scai code convert

# List available Snowflake connections

snow connection list

# Test your connection

snow connection test

# Add a new connection if needed

snow connection add

Supported Source Dialects¶