github.com/bruin-data/ingestr @v1.0.64 sqlite

<img src="https://github.com/bruin-data/ingestr/blob/main/resources/ingestr.svg?raw=true" width="500" />

Copy data from any source to any destination without any code

<img src="https://github.com/bruin-data/ingestr/blob/main/resources/demo.gif?raw=true" width="750" />

ingestr is a command-line app 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

You can install ingestr using the install script:

curl -LsSf https://getbruin.com/install/ingestr | sh

Alternatively, you can install it with pip:

pip install ingestr

The pip package can also be used from Python. Install the SDK extra for Python data ingestion:

pip install 'ingestr[sdk]'

Python rows, generators, and DataFrames are sent to the bundled ingestr binary as Arrow IPC streams by default:

import ingestr

ingestr.ingest(
    [{"id": 1, "name": "Ada"}, {"id": 2, "name": "Grace"}],
    dest_uri="duckdb:///tmp/warehouse.duckdb",
    dest_table="main.people",
)

DataFrames and yielded data use the same Arrow stream transport:

ingestr.ingest(df, dest_uri="duckdb:///tmp/warehouse.duckdb", dest_table="main.events")

def events():
    yield [{"id": 1, "event": "signup"}]
    yield [{"id": 2, "event": "purchase"}]

ingestr.ingest(events, dest_uri="postgresql://...", dest_table="public.events")

For push-style code, omit the data argument and use ingest as a context manager. The context value accepts the same shapes as ingestr.ingest(data, ...):

with ingestr.ingest(dest_uri="postgresql://...", dest_table="public.events") as ingest:
    for response in client.list_events():
        ingest(response["items"])

For very large already-materialized data, use the existing mmap Arrow IPC file transport:

ingestr.ingest(df, dest_uri="duckdb:///tmp/warehouse.duckdb", dest_table="main.events", transport="mmap")

For full CLI pass-through, use ingestr.run(["ingest", "--source-uri", "...", "--dest-uri", "...", "--source-table", "..."]), or ingestr.run_cli(...) for keyword arguments that map to CLI flags.

Quickstart

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

That's it.

This command:

• gets the table public.some_data from the Postgres instance.
• uploads 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.

Contributing

Pull requests are welcome. However, please open an issue first to discuss what you would like to change. We maybe able to offer you help and feedback regarding any changes you would like to make.

[!NOTE] After cloning ingestr make sure to run make setup to install githooks.

Supported sources & destinations