infrayantra/databridge-migrator repository overview

⁠DataBridge — Migration Studio

Migrate SQL Server databases to PostgreSQL in one click.
Run the container, open a browser, fill your credentials, and DataBridge handles the rest — schema, data, indexes, sequences, and foreign keys.

⁠Quick Start

docker run -d --restart unless-stopped -p 8080:8080 --name databridge \
  infrayantra/databridge-migrator:latest

Open → http://localhost:8080⁠

⁠What it does

• Transfers full database schema (tables, indexes, sequences, foreign keys)
• Streams source data directly into the target database
• Runs entirely inside Docker — nothing to install on your host
• Web UI shows live status, exit code, and migration duration
• Also usable as a CLI for scripted or automated pipelines

⁠Web UI

Click Run Migration — the UI shows a progress indicator while the job runs, then displays the full log output along with a success or failure badge and total duration.

⁠CLI Usage

docker run --rm infrayantra/databridge-migrator:latest python migration_tool.py \
  --mssql-host     <SOURCE_HOST>  \
  --mssql-port     1433           \
  --mssql-database <SOURCE_DB>    \
  --mssql-user     <SOURCE_USER>  \
  --mssql-password <SOURCE_PASS>  \
  --pg-host        <TARGET_HOST>  \
  --pg-port        5432           \
  --pg-database    <TARGET_DB>    \
  --pg-user        <TARGET_USER>  \
  --pg-password    <TARGET_PASS>  \
  --schema         public

⁠Tags

⁠Networking

⁠Behaviour & Defaults

• Migration mode: drop and recreate target tables — ensure the target database exists and is empty before running.
• Memory settings: work_mem = 16 MB, maintenance_work_mem = 512 MB (tunable via CLI flags).
• Validation: required fields and port ranges are validated before any connection attempt.
• Cleanup: all temporary configuration files are removed automatically after each run.

⁠Important Notes

• Always test on a non-production copy of the data before running against a live database.
• The target PostgreSQL database must already exist; DataBridge will create the schema and all objects inside it.
• For very large databases consider increasing Docker memory limits and adjusting batch sizes.

⁠Image Details