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feat(java-sdk): Add Java SDK V2 with fluent builder API and entity support (#15307 )

2025-12-03 05:54:02 -08:00

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DataJob Entity

The DataJob entity represents a unit of work in a data processing pipeline (e.g., an Airflow task, a dbt model, a Spark job). DataJobs belong to DataFlows (pipelines) and can have lineage to datasets and other DataJobs. This guide covers comprehensive DataJob operations in SDK V2.

Creating a DataJob

Minimal DataJob

Orchestrator, flowId, and jobId are required:

DataJob dataJob = DataJob.builder()
    .orchestrator("airflow")
    .flowId("my_dag")
    .jobId("my_task")
    .build();

With Cluster

Specify cluster (default is "prod"):

DataJob dataJob = DataJob.builder()
    .orchestrator("airflow")
    .flowId("analytics_pipeline")
    .cluster("staging")
    .jobId("transform_data")
    .build();
// URN: urn:li:dataJob:(urn:li:dataFlow:(airflow,analytics_pipeline,staging),transform_data)

With Metadata

Add description and name at construction (requires both name AND type):

DataJob dataJob = DataJob.builder()
    .orchestrator("dagster")
    .flowId("customer_etl")
    .cluster("prod")
    .jobId("load_customers")
    .description("Loads customer data from PostgreSQL to Snowflake")
    .name("Load Customers to DWH")
    .type("BATCH")
    .build();

With Custom Properties

Include custom properties in builder (requires name and type when using customProperties):

Map<String, String> props = new HashMap<>();
props.put("schedule", "0 2 * * *");
props.put("retries", "3");
props.put("timeout", "3600");

DataJob dataJob = DataJob.builder()
    .orchestrator("airflow")
    .flowId("daily_pipeline")
    .jobId("my_task")
    .name("My Daily Task")
    .type("BATCH")
    .customProperties(props)
    .build();

URN Construction

DataJob URNs follow the pattern:

urn:li:dataJob:(urn:li:dataFlow:({orchestrator},{flowId},{cluster}),{jobId})

Automatic URN creation:

DataJob dataJob = DataJob.builder()
    .orchestrator("airflow")
    .flowId("finance_reporting")
    .cluster("prod")
    .jobId("aggregate_transactions")
    .build();

DataJobUrn urn = dataJob.getDataJobUrn();
// urn:li:dataJob:(urn:li:dataFlow:(airflow,finance_reporting,prod),aggregate_transactions)

Description Operations

Setting Description

dataJob.setDescription("Processes daily customer transactions");

Reading Description

Get description (lazy-loaded from DataJobInfo):

String description = dataJob.getDescription();

Display Name Operations

Setting Name

dataJob.setName("Process Customer Transactions");

Reading Name

String name = dataJob.getName();

Owners

Adding Owners

import com.linkedin.common.OwnershipType;

// Technical owner
dataJob.addOwner(
    "urn:li:corpuser:data_team",
    OwnershipType.TECHNICAL_OWNER
);

// Data steward
dataJob.addOwner(
    "urn:li:corpuser:compliance",
    OwnershipType.DATA_STEWARD
);

// Business owner
dataJob.addOwner(
    "urn:li:corpuser:product_team",
    OwnershipType.BUSINESS_OWNER
);

Removing Owners

dataJob.removeOwner("urn:li:corpuser:data_team");

Owner Types

Available ownership types:

TECHNICAL_OWNER - Maintains the technical implementation
BUSINESS_OWNER - Business stakeholder
DATA_STEWARD - Manages data quality and compliance
DATAOWNER - Generic data owner
DEVELOPER - Software developer
PRODUCER - Data producer
CONSUMER - Data consumer
STAKEHOLDER - Other stakeholder

Glossary Terms

Adding Terms

dataJob.addTerm("urn:li:glossaryTerm:DataProcessing");
dataJob.addTerm("urn:li:glossaryTerm:ETL");

Removing Terms

dataJob.removeTerm("urn:li:glossaryTerm:DataProcessing");

Term Chaining

dataJob.addTerm("urn:li:glossaryTerm:DataProcessing")
       .addTerm("urn:li:glossaryTerm:ETL")
       .addTerm("urn:li:glossaryTerm:FinancialReporting");

Domain

Setting Domain

dataJob.setDomain("urn:li:domain:Engineering");

Removing Domain

dataJob.removeDomain();

Custom Properties

Adding Individual Properties

dataJob.addCustomProperty("schedule", "0 2 * * *");
dataJob.addCustomProperty("retries", "3");
dataJob.addCustomProperty("timeout", "3600");

Setting All Properties

Replace all custom properties:

Map<String, String> properties = new HashMap<>();
properties.put("schedule", "0 2 * * *");
properties.put("retries", "3");
properties.put("timeout", "3600");
properties.put("priority", "high");

dataJob.setCustomProperties(properties);

Removing Properties

dataJob.removeCustomProperty("timeout");

Lineage Operations

DataJob lineage defines the relationship between data jobs and the datasets they operate on. Lineage enables impact analysis, data provenance tracking, and understanding data flows through your pipelines.

The DataJob SDK supports four types of lineage:

Dataset-level lineage - Track which datasets a job reads from and writes to
DataJob dependencies - Track which jobs depend on other jobs (task dependencies)
Field-level lineage - Track specific columns consumed and produced
Fine-grained lineage - Track column-to-column transformations

Understanding Input and Output Datasets

Input Datasets - Datasets that the job reads from:

Represent source data for the job
Create upstream lineage: Dataset → DataJob

Output Datasets - Datasets that the job writes to:

Represent destination data from the job
Create downstream lineage: DataJob → Dataset

Input Datasets

Adding Single Inlet

// Using string URN
dataJob.addInputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,raw.transactions,PROD)");

// Using DatasetUrn object for type safety
DatasetUrn datasetUrn = DatasetUrn.createFromString(
    "urn:li:dataset:(urn:li:dataPlatform:snowflake,raw.transactions,PROD)"
);
dataJob.addInputDataset(datasetUrn);

Adding Multiple Inlets

// Chain multiple calls
dataJob.addInputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,raw.transactions,PROD)")
       .addInputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,raw.customers,PROD)")
       .addInputDataset("urn:li:dataset:(urn:li:dataPlatform:kafka,events.purchases,PROD)");

Setting All Inlets at Once

List<String> inletUrns = Arrays.asList(
    "urn:li:dataset:(urn:li:dataPlatform:snowflake,raw.orders,PROD)",
    "urn:li:dataset:(urn:li:dataPlatform:snowflake,raw.customers,PROD)",
    "urn:li:dataset:(urn:li:dataPlatform:kafka,events.clicks,PROD)"
);
dataJob.setInputDatasets(inletUrns);

Removing Inlets

// Remove single inlet
dataJob.removeInputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,raw.transactions,PROD)");

// Or using DatasetUrn
DatasetUrn datasetUrn = DatasetUrn.createFromString(
    "urn:li:dataset:(urn:li:dataPlatform:snowflake,raw.transactions,PROD)"
);
dataJob.removeInputDataset(datasetUrn);

Reading Inlets

// Get all inlets (lazy-loaded)
List<DatasetUrn> inlets = dataJob.getInputDatasets();
for (DatasetUrn inlet : inlets) {
    System.out.println("Input: " + inlet);
}

Output Datasets (Outlets)

Adding Single Outlet

// Using string URN
dataJob.addOutputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.sales_summary,PROD)");

// Using DatasetUrn object
DatasetUrn datasetUrn = DatasetUrn.createFromString(
    "urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.sales_summary,PROD)"
);
dataJob.addOutputDataset(datasetUrn);

Adding Multiple Outlets

dataJob.addOutputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.daily_summary,PROD)")
       .addOutputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.monthly_summary,PROD)")
       .addOutputDataset("urn:li:dataset:(urn:li:dataPlatform:s3,reports/summary.parquet,PROD)");

Setting All Outlets at Once

List<String> outletUrns = Arrays.asList(
    "urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.customer_metrics,PROD)",
    "urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.product_metrics,PROD)"
);
dataJob.setOutputDatasets(outletUrns);

Removing Outlets

// Remove single outlet
dataJob.removeOutputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.sales_summary,PROD)");

// Or using DatasetUrn
DatasetUrn datasetUrn = DatasetUrn.createFromString(
    "urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.sales_summary,PROD)"
);
dataJob.removeOutputDataset(datasetUrn);

Reading Outlets

// Get all outlets (lazy-loaded)
List<DatasetUrn> outlets = dataJob.getOutputDatasets();
for (DatasetUrn outlet : outlets) {
    System.out.println("Output: " + outlet);
}

DataJob Dependencies

DataJob dependencies model task-to-task relationships within workflows. This enables DataHub to track which jobs depend on other jobs completing first.

Use cases:

Airflow task dependencies (task A → task B → task C)
Cross-DAG dependencies (jobs in different pipelines)
Workflow orchestration visualization

Adding Job Dependencies

// Using string URN
dataJob.addInputDataJob("urn:li:dataJob:(urn:li:dataFlow:(airflow,pipeline,prod),upstream_task)");

// Using DataJobUrn object for type safety
DataJobUrn upstreamJob = DataJobUrn.createFromString(
    "urn:li:dataJob:(urn:li:dataFlow:(airflow,pipeline,prod),upstream_task)"
);
dataJob.addInputDataJob(upstreamJob);

Chaining Job Dependencies

// Multiple dependencies (task runs after all complete)
dataJob.addInputDataJob("urn:li:dataJob:(urn:li:dataFlow:(airflow,pipeline,prod),task_1)")
       .addInputDataJob("urn:li:dataJob:(urn:li:dataFlow:(airflow,pipeline,prod),task_2)")
       .addInputDataJob("urn:li:dataJob:(urn:li:dataFlow:(dagster,other_pipeline,prod),external_task)");

Removing Job Dependencies

// Remove single dependency
dataJob.removeInputDataJob("urn:li:dataJob:(urn:li:dataFlow:(airflow,pipeline,prod),task_1)");

// Or using DataJobUrn
DataJobUrn jobUrn = DataJobUrn.createFromString(
    "urn:li:dataJob:(urn:li:dataFlow:(airflow,pipeline,prod),task_1)"
);
dataJob.removeInputDataJob(jobUrn);

Reading Job Dependencies

// Get all upstream job dependencies (lazy-loaded)
List<DataJobUrn> dependencies = dataJob.getInputDataJobs();
for (DataJobUrn dependency : dependencies) {
    System.out.println("Depends on: " + dependency);
}

Example: Airflow Task Dependencies

// Model a typical Airflow DAG task chain
DataJob extractTask = DataJob.builder()
    .orchestrator("airflow")
    .flowId("etl_pipeline")
    .jobId("extract_data")
    .build();

DataJob validateTask = DataJob.builder()
    .orchestrator("airflow")
    .flowId("etl_pipeline")
    .jobId("validate_data")
    .build();

// validate_data depends on extract_data
validateTask.addInputDataJob(extractTask.getUrn().toString());

DataJob transformTask = DataJob.builder()
    .orchestrator("airflow")
    .flowId("etl_pipeline")
    .jobId("transform_data")
    .build();

// transform_data depends on validate_data
transformTask.addInputDataJob(validateTask.getUrn().toString());

// Save all tasks
client.entities().upsert(extractTask);
client.entities().upsert(validateTask);
client.entities().upsert(transformTask);

// Result: extract_data → validate_data → transform_data

Field-Level Lineage

Field-level lineage tracks which specific columns (fields) a job consumes and produces. This provides finer granularity than dataset-level lineage.

Use cases:

Track which columns are read/written by transformations
Understand field-level dependencies
Validate that jobs only access necessary columns

Field URN Format:

urn:li:schemaField:(DATASET_URN,COLUMN_NAME)

Adding Input Fields

// Track which columns the job reads
dataJob.addInputField("urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:snowflake,db.orders,PROD),order_id)");
dataJob.addInputField("urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:snowflake,db.orders,PROD),customer_id)");
dataJob.addInputField("urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:snowflake,db.orders,PROD),total_amount)");

Adding Output Fields

// Track which columns the job writes
dataJob.addOutputField("urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.sales,PROD),order_id)");
dataJob.addOutputField("urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.sales,PROD),customer_id)");
dataJob.addOutputField("urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.sales,PROD),revenue)");

Removing Fields

// Remove field lineage
dataJob.removeInputField("urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:snowflake,db.orders,PROD),order_id)");
dataJob.removeOutputField("urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.sales,PROD),revenue)");

Reading Fields

// Get all input fields (lazy-loaded)
List<Urn> inputFields = dataJob.getInputFields();
for (Urn field : inputFields) {
    System.out.println("Reads field: " + field);
}

// Get all output fields (lazy-loaded)
List<Urn> outputFields = dataJob.getOutputFields();
for (Urn field : outputFields) {
    System.out.println("Writes field: " + field);
}

Example: Column-Level Tracking

DataJob aggregateJob = DataJob.builder()
    .orchestrator("airflow")
    .flowId("analytics")
    .jobId("aggregate_sales")
    .description("Aggregates sales data by customer")
    .name("Aggregate Sales by Customer")
    .type("BATCH")
    .build();

// Dataset-level lineage
aggregateJob.addInputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,raw.transactions,PROD)");
aggregateJob.addOutputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.customer_sales,PROD)");

// Field-level lineage - specify exact columns used
aggregateJob.addInputField("urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:snowflake,raw.transactions,PROD),customer_id)");
aggregateJob.addInputField("urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:snowflake,raw.transactions,PROD),amount)");
aggregateJob.addInputField("urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:snowflake,raw.transactions,PROD),transaction_date)");

aggregateJob.addOutputField("urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.customer_sales,PROD),customer_id)");
aggregateJob.addOutputField("urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.customer_sales,PROD),total_sales)");
aggregateJob.addOutputField("urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.customer_sales,PROD),transaction_count)");

client.entities().upsert(aggregateJob);

Fine-Grained Lineage

Fine-grained lineage captures column-to-column transformations, showing exactly which input columns produce which output columns and how they're transformed.

Use cases:

Document transformation logic (e.g., "SUM(amount)")
Track column-level impact analysis
Understand data derivations
Compliance and audit trails

Adding Fine-Grained Lineage

// Basic transformation (no confidence score)
dataJob.addFineGrainedLineage(
    "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:snowflake,raw.orders,PROD),customer_id)",
    "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.sales,PROD),customer_id)",
    "IDENTITY",
    null
);

// Transformation with confidence score (0.0 to 1.0)
dataJob.addFineGrainedLineage(
    "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:snowflake,raw.orders,PROD),amount)",
    "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.sales,PROD),revenue)",
    "SUM",
    1.0f  // High confidence
);

Common Transformation Types

// IDENTITY - direct copy
dataJob.addFineGrainedLineage(upstream, downstream, "IDENTITY", 1.0f);

// Aggregations
dataJob.addFineGrainedLineage(upstream, downstream, "SUM", 1.0f);
dataJob.addFineGrainedLineage(upstream, downstream, "COUNT", 1.0f);
dataJob.addFineGrainedLineage(upstream, downstream, "AVG", 1.0f);
dataJob.addFineGrainedLineage(upstream, downstream, "MAX", 1.0f);
dataJob.addFineGrainedLineage(upstream, downstream, "MIN", 1.0f);

// String operations
dataJob.addFineGrainedLineage(upstream, downstream, "CONCAT", 0.9f);
dataJob.addFineGrainedLineage(upstream, downstream, "UPPER", 1.0f);
dataJob.addFineGrainedLineage(upstream, downstream, "SUBSTRING", 0.95f);

// Date operations
dataJob.addFineGrainedLineage(upstream, downstream, "DATE_TRUNC", 1.0f);
dataJob.addFineGrainedLineage(upstream, downstream, "EXTRACT", 1.0f);

// Custom transformations
dataJob.addFineGrainedLineage(upstream, downstream, "CUSTOM_FUNCTION", 0.8f);

Removing Fine-Grained Lineage

// Remove specific transformation
dataJob.removeFineGrainedLineage(
    "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:snowflake,raw.orders,PROD),amount)",
    "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.sales,PROD),revenue)",
    "SUM",
    null  // queryUrn (optional)
);

Reading Fine-Grained Lineage

// Get all fine-grained lineage (lazy-loaded)
List<FineGrainedLineage> lineages = dataJob.getFineGrainedLineages();
for (FineGrainedLineage lineage : lineages) {
    System.out.println("Upstreams: " + lineage.getUpstreams());
    System.out.println("Downstreams: " + lineage.getDownstreams());
    System.out.println("Transformation: " + lineage.getTransformOperation());
    System.out.println("Confidence: " + lineage.getConfidenceScore());
}

Example: Complex Aggregation

DataJob salesAggregation = DataJob.builder()
    .orchestrator("airflow")
    .flowId("analytics")
    .jobId("daily_sales_summary")
    .name("Daily Sales Summary")
    .type("BATCH")
    .build();

// Dataset-level lineage
salesAggregation.addInputDataset("urn:li:dataset:(urn:li:dataPlatform:postgres,sales.transactions,PROD)");
salesAggregation.addOutputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.daily_summary,PROD)");

// Fine-grained transformations
String inputDataset = "urn:li:dataset:(urn:li:dataPlatform:postgres,sales.transactions,PROD)";
String outputDataset = "urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.daily_summary,PROD)";

// Date is copied directly
salesAggregation.addFineGrainedLineage(
    "urn:li:schemaField:(" + inputDataset + ",transaction_date)",
    "urn:li:schemaField:(" + outputDataset + ",date)",
    "IDENTITY",
    1.0f
);

// Revenue is SUM of amounts
salesAggregation.addFineGrainedLineage(
    "urn:li:schemaField:(" + inputDataset + ",amount)",
    "urn:li:schemaField:(" + outputDataset + ",total_revenue)",
    "SUM",
    1.0f
);

// Transaction count
salesAggregation.addFineGrainedLineage(
    "urn:li:schemaField:(" + inputDataset + ",transaction_id)",
    "urn:li:schemaField:(" + outputDataset + ",transaction_count)",
    "COUNT",
    1.0f
);

// Average order value
salesAggregation.addFineGrainedLineage(
    "urn:li:schemaField:(" + inputDataset + ",amount)",
    "urn:li:schemaField:(" + outputDataset + ",avg_order_value)",
    "AVG",
    1.0f
);

client.entities().upsert(salesAggregation);

Example: Multi-Column Derivation

// Model a transformation where output depends on multiple input columns
DataJob enrichmentJob = DataJob.builder()
    .orchestrator("airflow")
    .flowId("enrichment")
    .jobId("enrich_customer_data")
    .build();

String inputDataset = "urn:li:dataset:(urn:li:dataPlatform:postgres,crm.customers,PROD)";
String outputDataset = "urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.customers_enriched,PROD)";

// full_name = CONCAT(first_name, ' ', last_name)
// Both first_name and last_name contribute to full_name
enrichmentJob.addFineGrainedLineage(
    "urn:li:schemaField:(" + inputDataset + ",first_name)",
    "urn:li:schemaField:(" + outputDataset + ",full_name)",
    "CONCAT",
    1.0f
);

enrichmentJob.addFineGrainedLineage(
    "urn:li:schemaField:(" + inputDataset + ",last_name)",
    "urn:li:schemaField:(" + outputDataset + ",full_name)",
    "CONCAT",
    1.0f
);

// email_domain = SUBSTRING(email, POSITION('@', email) + 1)
enrichmentJob.addFineGrainedLineage(
    "urn:li:schemaField:(" + inputDataset + ",email)",
    "urn:li:schemaField:(" + outputDataset + ",email_domain)",
    "SUBSTRING",
    1.0f
);

client.entities().upsert(enrichmentJob);

Confidence Scores

Confidence scores (0.0 to 1.0) indicate how certain you are about the transformation:

1.0 - Exact, deterministic transformation (e.g., IDENTITY, SUM)
0.9-0.99 - High confidence (e.g., simple string operations)
0.7-0.89 - Medium confidence (e.g., complex transformations with some uncertainty)
0.5-0.69 - Low confidence (e.g., ML-derived lineage, heuristic-based)
< 0.5 - Very uncertain (generally not recommended)

// High confidence - exact transformation known
dataJob.addFineGrainedLineage(source, target, "UPPER", 1.0f);

// Medium confidence - inferred from SQL parsing
dataJob.addFineGrainedLineage(source, target, "CASE_WHEN", 0.85f);

// Low confidence - ML-predicted transformation
dataJob.addFineGrainedLineage(source, target, "INFERRED", 0.6f);

Complete Lineage Example

This example demonstrates all four types of lineage working together:

// Create upstream validation job
DataJob validateJob = DataJob.builder()
    .orchestrator("airflow")
    .flowId("analytics_pipeline")
    .cluster("prod")
    .jobId("validate_transactions")
    .name("Validate Transaction Data")
    .type("BATCH")
    .build();

validateJob.addInputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,raw.transactions,PROD)")
           .addOutputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,validated.transactions,PROD)");

client.entities().upsert(validateJob);

// Create main transformation job with comprehensive lineage
DataJob transformJob = DataJob.builder()
    .orchestrator("airflow")
    .flowId("analytics_pipeline")
    .cluster("prod")
    .jobId("aggregate_sales")
    .description("Aggregates daily sales data from multiple validated sources")
    .name("Aggregate Daily Sales")
    .type("BATCH")
    .build();

// 1. Dataset-level lineage - Which tables are read/written
transformJob.addInputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,validated.transactions,PROD)")
            .addInputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,raw.customers,PROD)")
            .addOutputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.daily_sales,PROD)");

// 2. DataJob dependencies - This job depends on the validation job
transformJob.addInputDataJob(validateJob.getUrn().toString());

// 3. Field-level lineage - Which specific columns are accessed
String transactionsDataset = "urn:li:dataset:(urn:li:dataPlatform:snowflake,validated.transactions,PROD)";
String customersDataset = "urn:li:dataset:(urn:li:dataPlatform:snowflake,raw.customers,PROD)";
String outputDataset = "urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.daily_sales,PROD)";

// Input fields
transformJob.addInputField("urn:li:schemaField:(" + transactionsDataset + ",transaction_id)")
            .addInputField("urn:li:schemaField:(" + transactionsDataset + ",customer_id)")
            .addInputField("urn:li:schemaField:(" + transactionsDataset + ",amount)")
            .addInputField("urn:li:schemaField:(" + transactionsDataset + ",transaction_date)")
            .addInputField("urn:li:schemaField:(" + customersDataset + ",customer_id)")
            .addInputField("urn:li:schemaField:(" + customersDataset + ",customer_name)");

// Output fields
transformJob.addOutputField("urn:li:schemaField:(" + outputDataset + ",date)")
            .addOutputField("urn:li:schemaField:(" + outputDataset + ",customer_name)")
            .addOutputField("urn:li:schemaField:(" + outputDataset + ",total_revenue)")
            .addOutputField("urn:li:schemaField:(" + outputDataset + ",transaction_count)");

// 4. Fine-grained lineage - Specific column-to-column transformations
// Date column (identity transformation)
transformJob.addFineGrainedLineage(
    "urn:li:schemaField:(" + transactionsDataset + ",transaction_date)",
    "urn:li:schemaField:(" + outputDataset + ",date)",
    "IDENTITY",
    1.0f
);

// Customer name (join + identity)
transformJob.addFineGrainedLineage(
    "urn:li:schemaField:(" + customersDataset + ",customer_name)",
    "urn:li:schemaField:(" + outputDataset + ",customer_name)",
    "IDENTITY",
    1.0f
);

// Total revenue (aggregation)
transformJob.addFineGrainedLineage(
    "urn:li:schemaField:(" + transactionsDataset + ",amount)",
    "urn:li:schemaField:(" + outputDataset + ",total_revenue)",
    "SUM",
    1.0f
);

// Transaction count (aggregation)
transformJob.addFineGrainedLineage(
    "urn:li:schemaField:(" + transactionsDataset + ",transaction_id)",
    "urn:li:schemaField:(" + outputDataset + ",transaction_count)",
    "COUNT",
    1.0f
);

// Add other metadata
transformJob.addTag("critical")
            .addOwner("urn:li:corpuser:data_team", OwnershipType.TECHNICAL_OWNER);

// Save to DataHub
client.entities().upsert(transformJob);

// Result: Creates comprehensive lineage showing:
// - Job dependency: validate_transactions → aggregate_sales
// - Dataset flow: raw.transactions → validated.transactions → analytics.daily_sales
//                 raw.customers → analytics.daily_sales
// - Column-level: transaction_date → date (IDENTITY)
//                 amount → total_revenue (SUM)
//                 transaction_id → transaction_count (COUNT)
//                 customer_name → customer_name (IDENTITY via JOIN)

Lineage Flow Visualization

The comprehensive lineage example above creates this multi-level lineage graph:

Job-to-Job Level:
┌────────────────────────┐         ┌──────────────────────┐
│ Validate Transactions  │────────→│  Aggregate Sales Job │
└────────────────────────┘         └──────────────────────┘

Dataset Level:
┌─────────────────────┐    ┌─────────────────────────┐    ┌─────────────────────────┐
│ raw.transactions    │───→│ validated.transactions  │───→│                         │
└─────────────────────┘    └─────────────────────────┘    │  analytics.daily_sales  │
                                                           │                         │
┌─────────────────────┐                                   │                         │
│ raw.customers       │──────────────────────────────────→│                         │
└─────────────────────┘                                   └─────────────────────────┘

Column Level (Fine-Grained):
validated.transactions.transaction_date ──[IDENTITY]──→ daily_sales.date
validated.transactions.amount           ──[SUM]──────→ daily_sales.total_revenue
validated.transactions.transaction_id   ──[COUNT]────→ daily_sales.transaction_count
raw.customers.customer_name             ──[IDENTITY]──→ daily_sales.customer_name

ETL Pipeline Example

Model a complete Extract-Transform-Load pipeline:

// Extract job
DataJob extractJob = DataJob.builder()
    .orchestrator("airflow")
    .flowId("etl_pipeline")
    .jobId("extract")
    .build();

extractJob.addInputDataset("urn:li:dataset:(urn:li:dataPlatform:mysql,prod.orders,PROD)")
          .addOutputDataset("urn:li:dataset:(urn:li:dataPlatform:s3,staging/orders_raw,PROD)");

client.entities().upsert(extractJob);

// Transform job
DataJob transformJob = DataJob.builder()
    .orchestrator("airflow")
    .flowId("etl_pipeline")
    .jobId("transform")
    .build();

transformJob.addInputDataset("urn:li:dataset:(urn:li:dataPlatform:s3,staging/orders_raw,PROD)")
            .addOutputDataset("urn:li:dataset:(urn:li:dataPlatform:s3,staging/orders_clean,PROD)");

client.entities().upsert(transformJob);

// Load job
DataJob loadJob = DataJob.builder()
    .orchestrator("airflow")
    .flowId("etl_pipeline")
    .jobId("load")
    .build();

loadJob.addInputDataset("urn:li:dataset:(urn:li:dataPlatform:s3,staging/orders_clean,PROD)")
       .addOutputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.orders,PROD)");

client.entities().upsert(loadJob);

// Creates end-to-end lineage:
// mysql.orders → [Extract] → s3.raw → [Transform] → s3.clean → [Load] → snowflake.analytics

Updating Lineage

// Load existing job
DataJobUrn urn = DataJobUrn.createFromString(
    "urn:li:dataJob:(urn:li:dataFlow:(airflow,my_pipeline,prod),my_task)"
);
DataJob dataJob = client.entities().get(urn);

// Add new inlet (e.g., requirements changed)
dataJob.addInputDataset("urn:li:dataset:(urn:li:dataPlatform:kafka,events.new_source,PROD)");

// Remove old outlet (e.g., deprecated table)
dataJob.removeOutputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,old.deprecated_table,PROD)");

// Apply changes
client.entities().update(dataJob);

Lineage Best Practices

Be Complete - Define both inputs and outputs for accurate lineage
Use Correct URNs - Ensure dataset URNs match existing datasets in DataHub
Update When Changed - Keep lineage current as pipelines evolve
Document Transformations - Use descriptions to explain what the job does
Model All Jobs - Include every step in your pipeline for complete lineage
Use Typed URNs - Prefer DatasetUrn/DataJobUrn objects over strings for compile-time safety
Layer Your Lineage - Start with dataset-level, add field-level and fine-grained as needed
Track Dependencies - Use DataJob dependencies to model task orchestration
Be Precise with Transformations - Use accurate transformation types in fine-grained lineage
Set Confidence Scores - Use appropriate confidence scores to indicate lineage quality

Common Patterns

Multiple Sources to Single Destination

// Data aggregation job
dataJob.addInputDataset("urn:li:dataset:(urn:li:dataPlatform:postgres,sales.orders,PROD)")
       .addInputDataset("urn:li:dataset:(urn:li:dataPlatform:postgres,sales.customers,PROD)")
       .addInputDataset("urn:li:dataset:(urn:li:dataPlatform:postgres,sales.products,PROD)")
       .addOutputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.sales_summary,PROD)");

Single Source to Multiple Destinations

// Data fanout job
dataJob.addInputDataset("urn:li:dataset:(urn:li:dataPlatform:kafka,events.raw,PROD)")
       .addOutputDataset("urn:li:dataset:(urn:li:dataPlatform:s3,archive/events,PROD)")
       .addOutputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,events.processed,PROD)")
       .addOutputDataset("urn:li:dataset:(urn:li:dataPlatform:elasticsearch,events.searchable,PROD)");

Cross-Platform Lineage

// ETL across different platforms
dataJob.addInputDataset("urn:li:dataset:(urn:li:dataPlatform:mysql,production.transactions,PROD)")
       .addInputDataset("urn:li:dataset:(urn:li:dataPlatform:kafka,events.user_activity,PROD)")
       .addInputDataset("urn:li:dataset:(urn:li:dataPlatform:s3,raw/reference_data,PROD)")
       .addOutputDataset("urn:li:dataset:(urn:li:dataPlatform:snowflake,analytics.customer_360,PROD)")
       .addOutputDataset("urn:li:dataset:(urn:li:dataPlatform:bigquery,reporting.customer_metrics,PROD)");

Complete Example

import datahub.client.v2.DataHubClientV2;
import datahub.client.v2.entity.DataJob;
import com.linkedin.common.OwnershipType;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

public class DataJobExample {
    public static void main(String[] args) {
        // Create client
        DataHubClientV2 client = DataHubClientV2.builder()
            .server("http://localhost:8080")
            .build();

        try {
            // Build data job with all metadata
            DataJob dataJob = DataJob.builder()
                .orchestrator("airflow")
                .flowId("customer_analytics")
                .cluster("prod")
                .jobId("process_events")
                .description("Processes customer events from Kafka to warehouse")
                .name("Process Customer Events")
                .type("BATCH")
                .build();

            // Add tags
            dataJob.addTag("critical")
                   .addTag("etl")
                   .addTag("pii");

            // Add owners
            dataJob.addOwner("urn:li:corpuser:data_team", OwnershipType.TECHNICAL_OWNER)
                   .addOwner("urn:li:corpuser:product_team", OwnershipType.BUSINESS_OWNER);

            // Add glossary terms
            dataJob.addTerm("urn:li:glossaryTerm:DataProcessing")
                   .addTerm("urn:li:glossaryTerm:CustomerData");

            // Set domain
            dataJob.setDomain("urn:li:domain:Analytics");

            // Add custom properties
            dataJob.addCustomProperty("schedule", "0 2 * * *")
                   .addCustomProperty("retries", "3")
                   .addCustomProperty("timeout", "7200");

            // Upsert to DataHub
            client.entities().upsert(dataJob);

            System.out.println("Successfully created data job: " + dataJob.getUrn());

        } catch (IOException | ExecutionException | InterruptedException e) {
            e.printStackTrace();
        } finally {
            try {
                client.close();
            } catch (IOException e) {
                e.printStackTrace();
            }
        }
    }
}

Updating Existing DataJobs

Load and Modify

// Load existing data job
DataJobUrn urn = DataJobUrn.createFromString(
    "urn:li:dataJob:(urn:li:dataFlow:(airflow,my_dag,prod),my_task)"
);
DataJob dataJob = client.entities().get(urn);

// Add new metadata (creates patches)
dataJob.addTag("new-tag")
       .addOwner("urn:li:corpuser:new_owner", OwnershipType.TECHNICAL_OWNER);

// Apply patches
client.entities().update(dataJob);

Incremental Updates

// Just add what you need
dataJob.addTag("critical");
client.entities().update(dataJob);

// Later, add more
dataJob.addCustomProperty("priority", "high");
client.entities().update(dataJob);

Builder Options Reference

Method	Required	Description
`orchestrator(String)`	✅ Yes	Orchestrator (e.g., "airflow", "dagster")
`flowId(String)`	✅ Yes	Flow/DAG identifier
`jobId(String)`	✅ Yes	Job/task identifier
`cluster(String)`	No	Cluster name (e.g., "prod", "dev"). Default: "prod"
`description(String)`	No	Job description. Requires both `name()` and `type()` to be set
`name(String)`	No	Display name shown in UI. Required if using `description()`, `type()`, or `customProperties()`
`type(String)`	No	Job type (e.g., "BATCH", "STREAMING"). Required if using `description()`, `name()`, or `customProperties()`
`customProperties(Map)`	No	Map of custom key-value properties. Requires both `name()` and `type()` to be set

Important: The DataJobInfo aspect requires both name and type fields. If you provide any of description, name, type, or customProperties in the builder, you must provide both name and type. Otherwise, you'll get an IllegalArgumentException at build time.

Common Patterns

Creating Multiple DataJobs

String[] tasks = {"extract", "transform", "load"};
for (String taskName : tasks) {
    DataJob dataJob = DataJob.builder()
        .orchestrator("airflow")
        .flowId("etl_pipeline")
        .cluster("prod")
        .jobId(taskName)
        .build();

    dataJob.addTag("etl")
           .addCustomProperty("team", "data-engineering");

    client.entities().upsert(dataJob);
}

Batch Metadata Addition

DataJob dataJob = DataJob.builder()
    .orchestrator("airflow")
    .flowId("my_dag")
    .jobId("my_task")
    .build();

List<String> tags = Arrays.asList("critical", "production", "etl");
tags.forEach(dataJob::addTag);

client.entities().upsert(dataJob);  // Emits all tags in one call

Conditional Metadata

if (isCritical(dataJob)) {
    dataJob.addTag("critical")
           .addTerm("urn:li:glossaryTerm:BusinessCritical");
}

if (processesFinancialData(dataJob)) {
    dataJob.addTag("financial")
           .addOwner("urn:li:corpuser:compliance_team", OwnershipType.DATA_STEWARD);
}

DataJob vs DataFlow

DataFlow represents a pipeline or DAG (e.g., an Airflow DAG):

URN: urn:li:dataFlow:(orchestrator,flowId,cluster)
Contains multiple DataJobs

DataJob represents a task within a pipeline:

URN: urn:li:dataJob:(flowUrn,jobId)
Belongs to one DataFlow
Can have lineage to datasets and other DataJobs

Example hierarchy:

DataFlow: urn:li:dataFlow:(airflow,customer_pipeline,prod)
├── DataJob: urn:li:dataJob:(urn:li:dataFlow:(airflow,customer_pipeline,prod),extract)
├── DataJob: urn:li:dataJob:(urn:li:dataFlow:(airflow,customer_pipeline,prod),transform)
└── DataJob: urn:li:dataJob:(urn:li:dataFlow:(airflow,customer_pipeline,prod),load)

Orchestrator Examples

Common orchestrator values:

airflow - Apache Airflow
dagster - Dagster
prefect - Prefect
dbt - dbt (data build tool)
spark - Apache Spark
glue - AWS Glue
dataflow - Google Cloud Dataflow
azkaban - Azkaban
luigi - Luigi

Next Steps

Dataset Entity - Working with dataset entities
Patch Operations - Deep dive into patches
Migration Guide - Upgrading from V1

Examples

Basic DataJob Creation

{{ inline /metadata-integration/java/examples/src/main/java/io/datahubproject/examples/v2/DataJobCreateExample.java show_path_as_comment }}

Comprehensive DataJob with Metadata and Lineage

{{ inline /metadata-integration/java/examples/src/main/java/io/datahubproject/examples/v2/DataJobFullExample.java show_path_as_comment }}

DataJob Lineage Operations

{{ inline /metadata-integration/java/examples/src/main/java/io/datahubproject/examples/v2/DataJobLineageExample.java show_path_as_comment }}

40 KiB Raw Blame History

DataJob Entity

Creating a DataJob

Minimal DataJob

With Cluster

With Metadata

With Custom Properties

URN Construction

Description Operations

Setting Description

Reading Description

Display Name Operations

Setting Name

Reading Name

Tags

Adding Tags

Removing Tags

Tag Chaining

Owners

Adding Owners

Removing Owners

Owner Types

Glossary Terms

Adding Terms

Removing Terms

Term Chaining

Domain

Setting Domain

Removing Domain

Custom Properties

Adding Individual Properties

Setting All Properties

Removing Properties

Lineage Operations

Understanding Input and Output Datasets

Input Datasets

Adding Single Inlet

Adding Multiple Inlets

Setting All Inlets at Once

Removing Inlets

Reading Inlets

Output Datasets (Outlets)

Adding Single Outlet

Adding Multiple Outlets

Setting All Outlets at Once

Removing Outlets

Reading Outlets

DataJob Dependencies

Adding Job Dependencies

Chaining Job Dependencies

Removing Job Dependencies

Reading Job Dependencies

Example: Airflow Task Dependencies

Field-Level Lineage

Adding Input Fields

Adding Output Fields

Removing Fields

Reading Fields

Example: Column-Level Tracking

Fine-Grained Lineage

Adding Fine-Grained Lineage

Common Transformation Types

Removing Fine-Grained Lineage

Reading Fine-Grained Lineage

Example: Complex Aggregation

Example: Multi-Column Derivation

Confidence Scores

Complete Lineage Example

Lineage Flow Visualization

ETL Pipeline Example

Updating Lineage

Lineage Best Practices

Common Patterns

Multiple Sources to Single Destination

Single Source to Multiple Destinations

Cross-Platform Lineage

Complete Example

Updating Existing DataJobs

Load and Modify

Incremental Updates

40 KiB

Raw Blame History