• 16 May 2025

Using Apache Spark as a Data Source in Amazon QuickSight

What is Apache Spark?

Apache Spark is a powerful unified analytics engine designed for large-scale data processing. It supports various components, including:

• Spark SQL : SQL interface for structured data processing
• MLlib : Machine learning library with scalable algorithms
• GraphX : Graph computation engine for network analysis
• Structured Streaming : Real-time data processing framework

For data analysts using Amazon QuickSight, Apache Spark provides exceptional capabilities for processing massive datasets before visualization.

Connection Methods

Amazon QuickSight offers two primary methods to connect to Apache Spark:

1. Direct Connection

Establish a direct JDBC connection between QuickSight and your Apache Spark cluster:

• Requires proper network configuration
• Uses Spark's Thrift Server
• Supports real-time query execution on your data

2. Spark SQL Connection

Connect via Spark SQL as an intermediary layer:

• Leverages Spark SQL's optimization capabilities
• Provides access to Spark's SQL dialect features
• Often results in better performance for complex analytical queries

Security Requirements

Amazon QuickSight enforces strict security for Apache Spark connections:

• LDAP Authentication : Required for all Spark connections (Spark 2.0+)
• Secure Connection : TLS/SSL encryption must be enabled
• Access Validation : QuickSight will refuse connections to improperly secured Spark servers
• Authorization : Proper user permissions must be configured in Spark

Configuration Process

To set up Apache Spark as a QuickSight data source :

1. Prepare your Spark environment:

• Ensure you're running Spark 2.0 or later
• Configure the Thrift Server with LDAP authentication
• Enable SSL/TLS encryption
• Set appropriate access controls

2. Configure QuickSight connection :

• Select "New Data Set" in QuickSight
• Choose "Spark" as your data source
• Enter connection parameters, including server, port, and credentials
• Test the connection before finalizing

Performance Considerations

For optimal performance when using Apache Spark with QuickSight:

• Pre-aggregate large datasets when possible
• Use appropriate partitioning strategies
• Consider caching frequently accessed data
• Optimize join operations within Spark
• Monitor query execution plans for inefficiencies

Choosing Between Direct Query and SPICE

QuickSight offers two query modes with Apache Spark:

• Direct Query : Best for real-time data needs and vast datasets
• SPICE Import : Provides faster dashboard performance but with slight data latency

Common Challenges and Solutions

When to Use Apache Spark with QuickSight

Apache Spark is ideal for QuickSight when:

• Processing multi-terabyte datasets
• Performing complex data transformations
• Requiring machine learning integration
• Working with streaming data sources

Consider alternatives for smaller datasets or when deep Spark expertise is unavailable.

Implementation Example

A retail analytics team uses Apache Spark with QuickSight to analyze customer purchase patterns across billions of transactions. Their implementation:

1. Uses Spark for initial data cleansing and aggregation
2. Creates optimized Spark SQL views for QuickSight
3. Implements partitioning by date and region
4. Utilizes direct query for real-time sales dashboards

Do you need expert assistance configuring Apache Spark with Amazon QuickSight? DataTerrain's data integration specialists have helped over 300 US-based organizations implement effective BI solutions with flexible support options.