SnowConvert AI - Best practices¶

1. Extraction¶

We highly recommend you use our scripts to extract your workload:

Teradata: DDL Export Scripts for Teradata (https://github.com/Snowflake-Labs/SC.DDLExportScripts/blob/main/Teradata/README.md).
Oracle: DDL Export Scripts for Oracle (https://github.com/Snowflake-Labs/SC.DDLExportScripts/blob/main/Oracle/README.md).
SQLServer: DDL Export Scripts for SQL Server (https://github.com/Snowflake-Labs/SC.DDLExportScripts/blob/main/SQLServer/README.pdf).
Redshift: Redshift code extraction guide.

2. Preprocess¶

We highly recommend you use a Preprocess Script that aims to give you better results before starting an assessment or a conversion. This script performs the following tasks:

Create a single file for each top-level object
Organize each file by a defined folder hierarchy (The default is: Database Name -> Schema Name -> Object Type)
Generate an inventory report that provides information on all the objects that are in the workload.

2.1 Download¶

Download the binary of the script for macOS (https://sctoolsartifacts.z5.web.core.windows.net/tools/extractorscope/standardize_sql_files) and make sure to follow the setup instructions in Step 2.3.
Download the binary of the script for Windows (https://sctoolsartifacts.z5.web.core.windows.net/tools/extractorscope/standardize_sql_files.exe).

2.2 Description¶

The following information is needed to run the script:

Script Argument	Example Value	Required	Usage
Input folder	`/home/user/extracted_ddls`	Yes	`{ -i \| ifolder= }`
Output folder	`/home/user/processed_extracted_ddls`	Yes	`{ -o \| ofolder= }`
Database name	`sampleDataBase`	Yes	`{ -d \| dname= }`
Database engine	`Microsoft SQL Server`	Yes	`{ -e \| dengine= }`
Output folder structure	`Database name, top level object type and schema`	No	`[ { -s \| structure= } ]`
Pivot tables generation	`Yes`	No	`[ -p ]`

Note

The supported values for the database engine argument (-e) are: oracle, mssql and teradata

Note

The supported values for the output folder structure argument (-s) are: database_name, schema_name and top_level_object_name_type.
When specifying this argument, all the previous values need to be separated by a comma. For example: -s database_name,top_level_object_name_type,schema_name.

This argument is optional and when it is not specified the default structure is the following: Database name, top-level object type and schema name.

Note

The pivot tables generation parameter (-p) is optional.

2.3 Setup the binary for Mac¶

Set the binary as an executable:
chmod +x standardize_sql_files
Run the script by executing the following command:

./standardize_sql_files
- If this is the first time running the binary the following message will pop-up:
  Click OK.
- Open Settings -> Privacy & Security -> Click Allow Anyway

Running the script¶

Running the script using the following format:
1. Mac format
  ./standardize_sql_files -i "input path" -o "output path" -d Workload1 -e teradata
2. Windows format
  ./standardize_sql_files.exe -i "input path" -o "output path" -d Workload1 -e teradata
If the script is successfully executed the following output will be displayed:

Splitting process completed successfully!
Report successfully created!
Script successfully executed!