Complete DataAI Function Coverage for Tableau

The dataai-spark-tableau Maven artifact depends on dataai-spark-functions, so adding the Tableau artifact makes the complete DataAI computational catalog available transitively. Algorithms are not copied into Tableau code; they remain tested once in the portable functions module.

Every DataAI function result is already a Spark Dataset<Row> or a small record containing DataFrames. Pass any result DataFrame to TableauFunctionOutputs.withRunMetadata(...), persist it under a governed table/view, and connect Tableau through Spark SQL or Databricks.

Coverage

Function family	DataAI APIs available to Tableau pipelines	Suggested result name
ETL pipeline	`DataAiPipeline`, declarative `RuleSpec` validation	`pipeline_*`
Data quality	`DataQualityFunctions` automatic, missing, duplicate, date, outlier, category, and text checks	`quality_*`
General analytics	`AnalyticsFunctions` summaries, pivot, variance, ranking, correlations, regression	`analytics_*`
Time analysis	`TimeSeriesFunctions` summaries, moving averages, rolling totals, period changes	`time_*`
Business analytics	`BusinessFunctions` outliers, anomalies, drift, Pareto, cohort, funnel, KPI	`business_*`
Market models	`MarketFunctions` demand, pricing, elasticity, basket, segments, churn, risk, inventory, profit, scenario	`market_*`
Geographic	`MapFunctions.readiness(...)` and all returned readiness DataFrames	`map_*`
Matrix	`MatrixFunctions.crossTab(...)` and `MatrixFunctions.balance(...)`	`matrix_cross_tab`, `matrix_balance`
Insights	`InsightFunctions` dictionary, chart recommendations, narratives, and alerts	`insight_*`

Universal adapter

Dataset<Row> tableauResult = TableauFunctionOutputs.withRunMetadata(
        dataAiFunctionResult,
        "market_demand",
        runId,
        completedAt,
        libraryVersion);

tableauResult.write()
        .format("delta")
        .mode(SaveMode.Overwrite)
        .saveAsTable("analytics.dataai_market_demand");

The added reserved columns are:

_dataai_result_name
_dataai_run_id
_dataai_completed_at
_dataai_library_version

The adapter rejects a result that already contains one of these names instead of silently overwriting customer data.

Matrix balancing

Matrix balancing is explicitly supported. Use TableauFunctionOutputs.matrixBalance(...) to expose balancedCells() plus:

balance_iterations
balance_maximum_error
balance_converged

The output retains the DataAI matrix fields: original value, balanced value, row and column targets, balanced totals, differences, and coefficients. The complete persistence example is ../examples/java/TableauMatrixBalancingExample.java.

Tableau can visualize the balanced values as a heat map, highlight target differences, filter to non-converged runs, and compare original versus balanced values. The customer's Spark job remains responsible for convergence policy and must decide whether a non-converged result may be published.