Apache Spark → Azure Data Lake Storage
AI-first ETL from Apache Spark into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apache Spark into Azure Data Lake Storage
Datrise syncs Apache Spark's records, events, and configuration objects into Azure Data Lake Storage as partitioned Parquet in ADLS Gen2 per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.
Sync is incremental: Datrise uses writes new date partitions to the container and compacts on a schedule, so re-runs update only what changed. Hive-style partitioning by load date, readable by Synapse and Databricks. ADLS hierarchical namespace makes folder layout matter, so Datrise keeps a predictable entity/date path your Azure engines mount directly.
Ideal for Azure lakehouse storage shared across Synapse and Databricks.
Endpoints
Apache Spark: SaaS or API data source for analytics and warehouse sync.
Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.
How Apache Spark entities map to Azure Data Lake Storage
| Apache Spark entity | Azure Data Lake Storage object | Notes |
|---|---|---|
| records | apache_spark_records | id PK · custom fields → nested struct/map fields in Parquet |
| events | apache_spark_events | ISO-8601 timestamp columns events |
| configuration objects | apache_spark_configuration_objects | id PK · linked to apache_spark_records |
FAQ
How does Datrise handle Apache Spark's custom fields in Azure Data Lake Storage?
Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Azure Data Lake Storage types.
How does the Apache Spark to Azure Data Lake Storage sync stay up to date?
It runs incrementally — Datrise uses writes new date partitions to the container and compacts on a schedule.
Related pipelines
More destinations for Apache Spark
- Apache Spark → Azure Synapse
- Apache Spark → Spreadsheets
- Apache Spark → Airtable
- Apache Spark → CSV Files
- Apache Spark → MongoDB
- Apache Spark → Supabase
- Apache Spark → Neon
- Apache Spark → PlanetScale
- Apache Spark → Amazon DynamoDB
- Apache Spark → Looker
- Apache Spark → Looker Studio
- Apache Spark → Microsoft Power BI
More sources for Azure Data Lake Storage
- Apify → Azure Data Lake Storage
- Appfollow → Azure Data Lake Storage
- Apple Search Ads → Azure Data Lake Storage
- Ashby → Azure Data Lake Storage
- Autopilot → Azure Data Lake Storage
- Autopilot Activities → Azure Data Lake Storage
- Aws Cloudtrail → Azure Data Lake Storage
- Azure Table Storage → Azure Data Lake Storage
- Azureblobstorage → Azure Data Lake Storage
- Babelforce → Azure Data Lake Storage
- Bigcommerce → Azure Data Lake Storage
- Bigquery → Azure Data Lake Storage
Early access
Connect Apache Spark to Azure Data Lake Storage the easy way
Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.