​​Sculpting Data for Machine Learning: Beam-Powered GenAI Edition

While Generative AI models capture headlines, the foundation of any successful GenAI implementation remains quality data preparation at scale. Apache Beam provides the ideal framework for constructing robust data pipelines that can seamlessly process batch and streaming data for GenAI applications. This session will guide attendees through leveraging Beam’s unified model to curate, transform, and deliver data ready for modern GenAI systems.

By combining Apache Beam with complementary technologies like Kafka for real-time streaming and Apache Iceberg for efficient data storage, we’ll demonstrate how to build an end-to-end GenAI data ecosystem. Attendees will learn practical techniques for data extraction, transformation, and preparation specifically optimized for GenAI models, all while maintaining scalability and performance across diverse data sources.

This presentation will provide both theoretical frameworks and practical implementations for using Apache Beam to create scalable, efficient data pipelines specifically designed for GenAI applications. Attendees will leave with actionable knowledge on how to leverage Beam’s unified processing model alongside technologies like Kafka and Iceberg to bridge diverse data streams into powerful GenAI solutions.

OUTLINE Introduction (5 minutes)

• The GenAI revolution and data’s critical role
• The unique challenges of GenAI data pipelines: – Handling diverse data formats (text, images, structured/unstructured) – Processing both historical and real-time data – Maintaining data quality at scale
• How Apache Beam’s unified batch/streaming model addresses these challenges

Bridging Data Streams for GenAI (10 minutes)

• The GenAI data ecosystem: – Mapping data requirements to model objectives – Identifying streaming vs. batch data needs – Beam’s role in unifying disparate data sources
• Real-world examples: – Creating Beam pipelines that combine historical data from data lakes with real-time user interactions – Using Kafka integration for real-time data ingestion – Leveraging Iceberg for efficient storage and retrieval of massive training datasets

Building GenAI Data Pipelines with Beam - Hands-On Example (20 minutes)

• Live demonstration implementing a Beam pipeline for GenAI data preparation
• Architecture walkthrough: – Ingesting data from multiple sources (APIs, databases, Kafka streams) – Implementing parallel processing for data extraction and transformation – Using Beam’s windowing capabilities for time-based data aggregation – Writing processed data to Apache Iceberg tables for efficient storage
• Code walkthrough

Optimizing GenAI Data Processing at Scale (10 minutes)

• Performance considerations for large-scale GenAI data processing – Beam techniques for handling high-volume, high-velocity data: – Effective parallelization strategies – Memory management for large datasets – Leveraging runner-specific optimizations
• Integration with modern data lakehouse architecture: – Using Apache Iceberg for optimized storage partitioning – Managing schema evolution for GenAI training datasets – Implementing efficient data versioning and lineage tracking

Real-time ML Inferencing within Data Pipelines (10 minutes)

• Embedding GenAI models directly in Beam pipelines: – Pre-filtering data streams with lightweight ML models – Performing real-time content classification and routing – Implementing feedback loops for continuous model improvement
• Case study: Real-time content moderation system using: – Kafka for event streaming – Beam for processing and ML integration – Iceberg for storing processed results and model artifacts

Conclusion and Future Directions (5 minutes)

• Best practices for implementing GenAI data pipelines with Beam
• Upcoming Beam features that will enhance GenAI workflows
• The future of unified data processing for next-generation AI Resources for continued learning and community engagement