Chapter 4: Data Ingestion and Processing Pipelines at Scale

Retrieval-Augmented Generation systems, particularly at scale, depend on efficiently managed data. The accuracy and timeliness of the information available to your system are foundational to its output. This chapter details the construction and operation of data pipelines engineered for the volume and velocity demands of large, distributed RAG deployments.

You will learn to:

Build high-throughput data ingestion systems using distributed frameworks like Spark or Kafka.
Apply effective strategies for document chunking and preprocessing tailored for massive datasets.
Handle embedding generation and its management in a distributed manner.
Utilize Change Data Capture (CDC) techniques to ensure your RAG system reflects data updates with minimal delay.
Administer and tune vector databases to support extensive RAG operations.
Establish data governance practices and maintain data lineage within these multifaceted systems.

The chapter includes a hands-on practical section where you will construct a scalable data ingestion pipeline, reinforcing the principles discussed.

Sections

4.1 Distributed Data Ingestion Frameworks
4.2 Scalable Document Chunking and Preprocessing Strategies
4.3 Distributed Embedding Generation and Management
4.4 Change Data Capture for Real-time RAG Updates
4.5 Vector Database Management and Optimization at Scale
4.6 Data Governance and Lineage in Distributed RAG Systems
4.7 Hands-on Practical: Building a Scalable Data Ingestion Pipeline