Skip to content

ingestr is a CLI tool to copy data between any databases with a single command seamlessly.

License

Notifications You must be signed in to change notification settings

svorwerk-flextg/ingestr

 
 

Repository files navigation

Ingest & copy data from any source to any destination without any code


Ingestr is a command-line application that allows you to ingest data from any source into any destination using simple command-line flags, no code necessary.

  • ✨ copy data from your database into any destination
  • ➕ incremental loading: append, merge or delete+insert
  • 🐍 single-command installation

ingestr takes away the complexity of managing any backend or writing any code for ingesting data, simply run the command and watch the data land on its destination.

Installation

pip install ingestr

Quickstart

ingestr ingest \
    --source-uri 'postgresql:https://admin:admin@localhost:8837/web?sslmode=disable' \
    --source-table 'public.some_data' \
    --dest-uri 'bigquery:https://<your-project-name>?credentials_path=/path/to/service/account.json' \
    --dest-table 'ingestr.some_data'

That's it.

This command will:

  • get the table public.some_data from the Postgres instance.
  • upload this data to your BigQuery warehouse under the schema ingestr and table some_data.

Documentation

You can see the full documentation here.

Community

Join our Slack community here.

Supported Sources & Destinations

Database Source Destination
Postgres
BigQuery
Snowflake
Redshift
Databricks
DuckDB
Microsoft SQL Server
Local CSV file
MongoDB
Oracle
SQLite
MySQL

More to come soon!

Acknowledgements

This project would not have been possible without the amazing work done by the SQLAlchemy and dlt teams. We relied on their work to connect to various sources and destinations, and built ingestr as a simple, opinionated wrapper around their work.

About

ingestr is a CLI tool to copy data between any databases with a single command seamlessly.

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Python 98.4%
  • Makefile 1.1%
  • Dockerfile 0.5%