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Dagster + GCP GCS

Dagster Integration:
Dagster + GCP GCS

Integrate with GCP GCS.
API reference

About this integration

This integration allows you to interact with Google Cloud Storage (GCS) using Dagster. It provides resources, IO managers, and utilities to manage and store data in GCS, making it easier to integrate GCS into your data pipelines.

Installation

pip install dagster-gcp

Examples


import pandas as pd
from dagster import Definitions, asset
from dagster_gcp.gcs import GCSResource


@asset
def my_gcs_asset(gcs: GCSResource):
    df = pd.DataFrame({"column1": [1, 2, 3], "column2": ["A", "B", "C"]})

    csv_data = df.to_csv(index=False)

    gcs_client = gcs.get_client()

    bucket = gcs_client.bucket("my-cool-bucket")
    blob = bucket.blob("path/to/my_dataframe.csv")
    blob.upload_from_string(csv_data)


defs = Definitions(
    assets=[my_gcs_asset],
    resources={"gcs": GCSResource(project="my-gcp-project")},
)

About Google Cloud Platform GCS

Google Cloud Storage (GCS), is a scalable and secure object storage service. GCS is designed for storing and accessing any amount of data at any time, making it ideal for data science, AI infrastructure, and frameworks for ML like AutoML. With this integration, you can leverage GCS for efficient data storage and retrieval within your Dagster pipelines.