SegmentStream pipeline SDK

segmentstream-pipeline is the small runtime contract between a workspace's Dagster definitions and the warehouse provisioned by SegmentStream. Dagster continues to own assets and jobs, while Ibis continues to own relational expressions. This package supplies lazy runtime configuration and durable warehouse I/O.

The initial connector supports BigQuery, automatic dataset creation, and full-table replacement. It reads the following non-secret configuration when a pipeline first accesses the warehouse:

• SEGMENTSTREAM_WAREHOUSE_ENGINE
• SEGMENTSTREAM_WAREHOUSE_CATALOG
• SEGMENTSTREAM_WAREHOUSE_DEFAULT_NAMESPACE
• SEGMENTSTREAM_WAREHOUSE_LOCATION (optional)

Configuration and authentication are deliberately lazy. Importing and validating definitions.py during a deployment build does not connect to a warehouse. In Cloud Run, the BigQuery connector uses the attached workload identity through Application Default Credentials.

import dagster as dg
import ibis
import ibis.expr.types as ir

from segmentstream import WAREHOUSE_IO_MANAGER_KEY, warehouse_resources


BRONZE_ORDERS = dg.AssetKey(["bronze", "orders"])
SILVER_ORDERS = dg.AssetKey(["silver", "orders"])


@dg.asset(
    key=BRONZE_ORDERS,
    io_manager_key=WAREHOUSE_IO_MANAGER_KEY,
    kinds={"ibis"},
)
def orders() -> ir.Table:
    return ibis.memtable(
        [{"order_id": "o-1", "amount": 100.0}],
        schema={"order_id": "string", "amount": "float64"},
    )


@dg.asset(
    key=SILVER_ORDERS,
    ins={"orders": dg.AssetIn(key=BRONZE_ORDERS)},
    io_manager_key=WAREHOUSE_IO_MANAGER_KEY,
    kinds={"ibis"},
)
def normalized_orders(orders: ir.Table) -> ir.Table:
    return orders.filter(orders.amount > 0)


defs = dg.Definitions(
    assets=[orders, normalized_orders],
    resources=warehouse_resources(),
)

Asset keys map to relations using a small convention:

• ["orders"] uses the configured default namespace.
• ["bronze", "orders"] uses the explicit bronze dataset.
• Other key shapes are rejected.

The workspace project is always supplied by SegmentStream and cannot be overridden by an asset. Before writing an asset, the IO manager creates its validated dataset with CREATE SCHEMA IF NOT EXISTS in the configured location. This lets pipeline authors organize one workspace project into datasets such as bronze, silver, and gold without provisioning them separately.

For local package development, install this project in editable mode rather than adding a relative path dependency to a deployable pipeline.

Published workspace pipelines will install the provider extra:

python -m pip install "segmentstream-pipeline[bigquery]"

Releases

Releases use the version declared in pyproject.toml and are published from the pipeline-sdk-v<version> Git tag by the protected pipeline-sdk-release.yml workflow. The workflow builds the wheel and source distribution in a job without publishing credentials, then uses PyPI Trusted Publishing from the pypi GitHub environment. No long-lived PyPI token is stored in GitHub.

PyPI releases are immutable. Increment the package version before creating a new release tag; do not reuse a version that has already been uploaded.