OpenMetadata/openmetadata-docs/content/v1.9.x-SNAPSHOT/how-to-guides/data-quality-observability/profiler/spark-engine/prerequisites.md

title	description	slug	collate
Spark Engine Prerequisites | OpenMetadata Spark Profiling Setup	Learn about the required infrastructure, network connectivity, and setup for using Spark Engine in OpenMetadata.	/how-to-guides/data-quality-observability/profiler/spark-engine/prerequisites	true

Spark Engine Prerequisites

Required Infrastructure

Spark Cluster

• Spark Connect available (versions 3.5.2 to 3.5.6 supported)
• Network access from the pipeline execution environment to the Spark Connect endpoint
• Network access from the pipeline execution environment to the OpenMetadata server

Database Drivers in Spark Cluster

Depending on your source database, ensure the appropriate driver is installed in your Spark cluster:

• PostgreSQL: org.postgresql.Driver
• MySQL: com.mysql.cj.jdbc.Driver

{% note %} The specific driver versions should match your Spark version and database version for optimal compatibility. {% /note %}

Network Connectivity

The pipeline execution environment must have:

• Outbound access to your Spark Connect endpoint (typically port 15002)
• Outbound access to your OpenMetadata server (typically port 8585)
• Inbound access from Spark workers to your source database

Verification Steps

1. Test Spark Connect: Verify connectivity from your pipeline environment to Spark Connect
2. Test OpenMetadata: Ensure your pipeline environment can reach the OpenMetadata API
3. Test Database: Confirm Spark workers can connect to your source database
4. Verify Drivers: Check that the appropriate database driver is available in your Spark cluster

Example Verification Commands

Test Spark Connect Connectivity

# Test basic connectivity to Spark Connect
telnet your_spark_connect_host 15002

# Or using curl if available
curl -X GET http://your_spark_connect_host:15002

Test OpenMetadata Connectivity

# Test OpenMetadata API connectivity
curl -X GET http://your_openmetadata_host:8585/api/v1/version

Test Database Connectivity from Spark

# Test database connectivity using Spark
from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("DatabaseConnectivityTest") \
    .remote("<SPARK_CONNECT_HOST>:<SPARK_CONNECT_PORT>") \
    .config("spark.jars", "/path/to/your/database/driver.jar") \
    .getOrCreate()

# Test connection to your database
df = spark.read \
    .format("jdbc") \
    .option("url", "jdbc:your_database_url") \
    .option("dbtable", "your_test_table") \
    .option("user", "your_username") \
    .option("password", "your_password") \
    .load()

df.head()

{% inlineCalloutContainer %} {% inlineCallout color="violet-70" bold="Partitioning Requirements" icon="MdOutlineSchema" href="/how-to-guides/data-quality-observability/profiler/spark-engine/partitioning" %} Learn about the partitioning requirements for Spark Engine. {% /inlineCallout %} {% inlineCallout icon="MdAnalytics" bold="Configuration" href="/how-to-guides/data-quality-observability/profiler/spark-engine/configuration" %} Configure your profiler pipeline to use Spark Engine. {% /inlineCallout %} {% /inlineCalloutContainer %}