Apache Spark to Azure Synapse Integration

How Datrise loads Apache Spark into Azure Synapse

Datrise syncs Apache Spark's jobs, stages, tasks, executors, SQL queries, and application metrics into Azure Synapse as a typed table per source entity. Flexible or custom fields land in NVARCHAR(MAX) JSON columns, and timestamps such as created, updated, and status changes are typed as datetime2.

Sync is incremental: Datrise uses COPY into staging, then a MERGE on stable id, so re-runs update only what changed. Hash distribution on the join id with date partitioning on facts. Synapse dedicated pools reward good hash-distribution choices, so Datrise distributes on entity ids to avoid data-movement-heavy joins.

Ideal for Azure analytics estates feeding Power BI.

Endpoints

Apache Spark: Distributed engine for large-scale data processing.

Azure Synapse: Microsoft analytics workspace with SQL pools.

How Apache Spark entities map to Azure Synapse

Apache Spark entity	Azure Synapse object	Notes
jobs	`apache_spark_jobs`	id PK · custom fields → NVARCHAR(MAX) JSON columns
stages	`apache_spark_stages`	id PK · linked to apache_spark_jobs
tasks	`apache_spark_tasks`	id PK · linked to apache_spark_jobs
executors	`apache_spark_executors`	id PK · linked to apache_spark_jobs

FAQ

How does Datrise handle Apache Spark's custom fields in Azure Synapse?

Flexible values are stored as NVARCHAR(MAX) JSON columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Azure Synapse types.

How does the Apache Spark to Azure Synapse sync stay up to date?

It runs incrementally — Datrise uses COPY into staging, then a MERGE on stable id.

Related pipelines

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