Apache Spark to MySQL Integration
AI-first ETL from Apache Spark into MySQL. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apache Spark into MySQL
Datrise syncs Apache Spark's jobs, stages, tasks, executors, SQL queries, and application metrics into MySQL as a typed table per source entity. Flexible or custom fields land in JSON columns, and timestamps such as created, updated, and status changes are typed as DATETIME/TIMESTAMP.
Sync is incremental: Datrise uses a watermark on updated-at, applied with INSERT … ON DUPLICATE KEY UPDATE, so re-runs update only what changed. Optional RANGE partitioning by load date. MySQL collation matters for CRM text, so Datrise lands utf8mb4 to preserve emoji and non-Latin characters.
Ideal for operational reporting and app databases already standardized on MySQL.
Endpoints
Apache Spark: Distributed engine for large-scale data processing.
MySQL: Widely used OSS relational engine (InnoDB).
How Apache Spark entities map to MySQL
| Apache Spark entity | MySQL object | Notes |
|---|---|---|
| jobs | apache_spark_jobs | id PK · custom fields → 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 MySQL?
Flexible values are stored as JSON columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native MySQL types.
How does the Apache Spark to MySQL sync stay up to date?
It runs incrementally — Datrise uses a watermark on updated-at, applied with INSERT … ON DUPLICATE KEY UPDATE.
Related pipelines
More destinations for Apache Spark
- Apache Spark → Microsoft SQL Server
- Apache Spark → Oracle Database
- Apache Spark → Snowflake
- Apache Spark → Google BigQuery
- Apache Spark → Amazon Redshift
- Apache Spark → Databricks SQL Warehouse
- Apache Spark → ClickHouse
- Apache Spark → DuckDB
- Apache Spark → Amazon Athena
- Apache Spark → Amazon S3 Data Lake
- Apache Spark → Azure Data Lake Storage
- Apache Spark → Azure Synapse
Early access
Connect Apache Spark to MySQL 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.