BI Integrations

BI Integrations

Business intelligence and visualization platforms for data exploration, dashboards, and analytics.

Looker

Package: dagster-looker | Support: Dagster-supported

Google's business intelligence platform for creating interactive dashboards and SQL-based analytics.

Use cases:

• Refresh Looker PDTs (persistent derived tables)
• Trigger Looker dashboard regeneration
• Integrate Looker explores with Dagster assets
• Schedule Looker report updates

Quick start:

from dagster_looker import LookerResource

looker = LookerResource(
    base_url="https://company.looker.com",
    client_id=dg.EnvVar("LOOKER_CLIENT_ID"),
    client_secret=dg.EnvVar("LOOKER_CLIENT_SECRET")
)

@dg.asset
def refresh_looker_pdt(looker: LookerResource):
    # Refresh a persistent derived table
    looker.get_client().run_query(
        query_id="123",
        result_format="json"
    )

Docs: https://docs.dagster.io/integrations/libraries/looker

Tableau

Package: dagster-tableau | Support: Dagster-supported

Interactive data visualization and dashboarding platform for business analytics.

Use cases:

• Refresh Tableau data sources
• Publish workbooks to Tableau Server
• Trigger extract refreshes
• Integrate with Tableau prep workflows

Quick start:

from dagster_tableau import TableauResource

tableau = TableauResource(
    server_url="https://tableau.company.com",
    username=dg.EnvVar("TABLEAU_USER"),
    password=dg.EnvVar("TABLEAU_PASSWORD"),
    site_id="my_site"
)

@dg.asset
def refresh_tableau_datasource(tableau: TableauResource):
    # Refresh a Tableau data source
    tableau.refresh_datasource(
        datasource_id="abc-123-def"
    )

Docs: https://docs.dagster.io/integrations/libraries/tableau

PowerBI

Package: dagster-powerbi | Support: Dagster-supported

Microsoft's business intelligence platform for creating reports and dashboards.

Use cases:

• Refresh PowerBI datasets
• Trigger PowerBI report generation
• Update PowerBI dataflows
• Schedule dashboard refreshes

Quick start:

from dagster_powerbi import PowerBIResource

powerbi = PowerBIResource(
    client_id=dg.EnvVar("POWERBI_CLIENT_ID"),
    client_secret=dg.EnvVar("POWERBI_CLIENT_SECRET"),
    tenant_id=dg.EnvVar("POWERBI_TENANT_ID")
)

@dg.asset
def refresh_powerbi_dataset(powerbi: PowerBIResource):
    # Trigger dataset refresh
    powerbi.refresh_dataset(
        dataset_id="abc-123-def",
        workspace_id="workspace-456"
    )

Docs: https://docs.dagster.io/integrations/libraries/powerbi

Sigma

Package: dagster-sigma | Support: Dagster-supported

Cloud-native analytics and BI platform with spreadsheet-like interface for data exploration.

Use cases:

• Refresh Sigma materialized datasets
• Trigger Sigma workbook updates
• Integrate with Sigma workflows
• Schedule data refreshes

Quick start:

from dagster_sigma import SigmaResource

sigma = SigmaResource(
    base_url="https://app.sigmacomputing.com",
    client_id=dg.EnvVar("SIGMA_CLIENT_ID"),
    client_secret=dg.EnvVar("SIGMA_CLIENT_SECRET")
)

@dg.asset
def refresh_sigma_workbook(sigma: SigmaResource):
    # Trigger workbook refresh
    sigma.get_client().refresh_workbook(
        workbook_id="workbook-123"
    )

Docs: https://docs.dagster.io/integrations/libraries/sigma

Hex

Package: dagster-hex | Support: Community-supported

Collaborative data notebooks platform combining SQL, Python, and visualizations.

Use cases:

• Run Hex projects from Dagster
• Schedule Hex notebook execution
• Pass data between Dagster and Hex
• Trigger Hex workflows

Quick start:

from dagster_hex import HexResource

hex = HexResource(
    api_token=dg.EnvVar("HEX_API_TOKEN")
)

@dg.asset
def run_hex_project(hex: HexResource):
    # Trigger Hex project run
    run_id = hex.get_client().run_project(
        project_id="project-123",
        input_params={"date": "2024-01-01"}
    )
    return hex.wait_for_run(run_id)

Docs: https://docs.dagster.io/integrations/libraries/hex

Evidence

Package: dagster-evidence | Support: Community-supported

Markdown-based BI tool for building data reports and dashboards with code.

Use cases:

• Generate Evidence reports from Dagster
• Build data-driven documentation
• Create automated reports
• Version-controlled analytics

Quick start:

from dagster_evidence import EvidenceResource

evidence = EvidenceResource(
    project_dir="path/to/evidence/project"
)

@dg.asset
def generate_evidence_report(evidence: EvidenceResource):
    # Build Evidence project
    evidence.build()

Docs: https://docs.dagster.io/integrations/libraries/evidence

Cube

Package: dagster-cube | Support: Community-supported

Semantic layer and headless BI platform for building consistent metrics across tools.

Use cases:

• Define metrics and dimensions
• Create semantic data models
• Power multiple BI tools from single definition
• API-first analytics

Quick start:

from dagster_cube import CubeResource

cube = CubeResource(
    base_url="http://localhost:4000",
    api_token=dg.EnvVar("CUBE_API_TOKEN")
)

@dg.asset
def query_cube_metrics(cube: CubeResource):
    # Query Cube API
    result = cube.get_client().load({
        "measures": ["Orders.count"],
        "dimensions": ["Orders.status"]
    })
    return result

Docs: https://docs.dagster.io/integrations/libraries/cube

BI Tool Selection

Common Patterns

Data Refresh Pattern

# Transform data in Dagster
@dg.asset
def analytics_table() -> pd.DataFrame:
    return transform_data()

# Load to warehouse
@dg.asset
def warehouse_table(
    analytics_table: pd.DataFrame,
    warehouse: WarehouseResource
):
    warehouse.write_dataframe(analytics_table, "analytics.summary")

# Refresh BI tool
@dg.asset
def refreshed_dashboard(
    warehouse_table,
    tableau: TableauResource
):
    tableau.refresh_datasource("dashboard-source-id")

Scheduled Report Generation

@dg.asset
def daily_report(hex: HexResource):
    # Run Hex notebook that generates report
    run_result = hex.get_client().run_project(
        project_id="daily-report",
        input_params={
            "report_date": datetime.now().strftime("%Y-%m-%d")
        }
    )
    return run_result

# Schedule daily
daily_schedule = dg.ScheduleDefinition(
    job=dg.define_asset_job("daily_report_job"),
    cron_schedule="0 8 * * *"  # 8 AM daily
)

Multi-Tool Refresh

@dg.asset
def core_data() -> pd.DataFrame:
    return load_and_transform_data()

@dg.asset
def refresh_all_bi_tools(
    core_data: pd.DataFrame,
    looker: LookerResource,
    tableau: TableauResource,
    powerbi: PowerBIResource
):
    # Refresh all BI tools after data update
    looker.refresh_pdt("pdt_name")
    tableau.refresh_datasource("datasource_id")
    powerbi.refresh_dataset("dataset_id")

Tips

• Timing: Refresh BI tools after warehouse loads complete
• Incremental: Use incremental refreshes when possible to save time
• Caching: Be aware of BI tool caching - force refresh if needed
• Dependencies: Model BI refreshes as downstream assets
• Testing: Test BI integrations in dev environment first
• Monitoring: Alert on BI refresh failures
• Semantic layer: Consider Cube for consistent metrics across tools
• Self-service: Evidence and Hex enable analysts to own their reports
• Costs: Cloud BI tools can be expensive - monitor usage and seats