RAG is Just ETL with Extra Steps: A Data Engineering Perspective

If you are a data engineer, you have likely heard the buzz around Retrieval-Augmented Generation (RAG), the technology that powers many of the generative AI applications we see today. At first glance, RAG can seem like a completely new paradigm, a mysterious black box that somehow makes Large Language Models (LLMs) smarter. However, I am here to argue that for a data engineer, RAG is a familiar concept in a new guise: RAG is just an ETL pipeline with a few extra steps.

This post is for data engineers who are new to RAG, AI, and Generative AI. My goal is to demystify RAG by mapping its concepts to the data engineering principles you already know and understand. By the end of this article, you will not only grasp the theory but also see how to build a production-ready RAG system using the ETL framework you are comfortable with. We will walk through a complete, open-source portfolio project that puts these ideas into practice.

The RAG-as-ETL Mapping: A Familiar Framework

The core function of an ETL pipeline is to move data from a source, transform it into a usable format, and load it into a target system for analysis and querying. RAG does exactly the same thing, but its “data” is unstructured text, and its “querying” is semantic search.

Let’s break down the direct mapping between a traditional data engineering workflow and a RAG pipeline.

This analogy is more than just a convenient mental model; it provides a robust framework for designing, building, and maintaining reliable RAG systems. By applying the same rigor to a RAG pipeline as you would to a critical financial data pipeline, you can avoid the common pitfalls that cause many AI projects to fail in production.

Building a Production-Ready RAG Pipeline: The rag-etl-pipeline Project

To make this concrete, we will explore the architecture of a fully working RAG system, rag-etl-pipeline, designed from the ground up using ETL principles. This project is not just a toy example; it is a production-ready blueprint that you can adapt for your own use cases.

Here is the high-level architecture, which should look familiar to any data engineer:

EXTRACT → TRANSFORM → LOAD → QUERY → MONITOR
  ↓          ↓          ↓       ↓       ↓
Docs    Chunking    Vector   Hybrid  Quality
        Embedding    DB      Search  Metrics

Now, let’s dive into each stage of the pipeline, looking at the key data engineering concepts and the corresponding code from our project.

1. Extract: More Than Just Loading Files

In ETL, the Extract stage is about reliably ingesting data from various sources. In RAG, this means loading documents of different formats (PDFs, text files, Markdown, etc.) and, crucially, validating them.

Just as you would validate incoming JSON against a schema, you must validate incoming documents for quality. A corrupted PDF or a file with garbled text is the unstructured-data equivalent of a malformed record. Our document_loader.py handles this by providing a UniversalDocumentLoader that not only loads various formats but also captures essential metadata.

# src/extract/document_loader.py

class UniversalDocumentLoader:
    def __init__(self):
        self.loaders = {
            '.pdf': PDFLoader(),
            '.txt': TextLoader(),
            '.md': MarkdownLoader(),
            '.json': JSONLoader(),
        }

    def load(self, file_path: str) -> List[Document]:
        # ... implementation to load based on file extension

    def get_metadata(self, file_path: str) -> DocumentMetadata:
        # ... implementation to extract metadata like file size, word count

Data Engineering Principle: Source data validation and metadata tracking are non-negotiable. Every document loaded is a source record that needs to be traceable throughout the pipeline.

2. Transform: The Heart of the RAG Pipeline

This is where the “magic” of RAG happens, and it is also where the ETL analogy is most powerful. The Transform stage in RAG involves two key steps: chunking and embedding.

Chunking as Transformation: Chunking is the process of breaking down large documents into smaller, manageable pieces. A naive approach, like splitting by a fixed number of characters, is like splitting a CSV file in the middle of a row. It destroys context and leads to poor retrieval quality. This is one of the biggest reasons why many RAG projects fail.

A production-grade RAG system uses semantic chunking, which is a data transformation that aims to keep complete ideas or concepts within each chunk. Our chunker.py implements several strategies, with SemanticChunker being the most robust.

# src/transform/chunker.py

class SemanticChunker(ChunkingStrategy):
    def chunk(self, documents: List[Document]) -> List[Chunk]:
        # ... implementation that splits text based on semantic similarity
        # between sentences, keeping ideas intact.

Embedding as Enrichment: Embedding is the process of converting text chunks into numerical vectors using an embedding model. This is analogous to data enrichment in ETL, where you might add geographical data based on an IP address. Here, we are adding a semantic representation that allows for similarity-based querying.

# src/transform/embedder.py

class Embedder:
    def __init__(self, provider: EmbeddingProvider):
        self.provider = provider

    def embed_chunks(self, chunks: List[Chunk]) -> List[Chunk]:
        texts = [chunk.content for chunk in chunks]
        embeddings = self.provider.embed_texts(texts)
        # Attach embeddings to chunks
        # ...
        return chunks

Data Engineering Principle: Transformations must be context-aware and preserve the integrity of the data. Just as you would not split a JSON object randomly, you should not split a document without understanding its semantic structure.

3. Load: The Vector Database as Your Data Warehouse

In ETL, the Load stage is where you store your transformed data in a data warehouse for efficient querying. In RAG, your “warehouse” is a vector database. This specialized database is optimized for storing and querying high-dimensional vectors.

Our vector_db.py provides an abstraction layer that allows us to switch between different vector databases (like ChromaDB for local development or Pinecone/Weaviate for production) without changing the pipeline logic.

# src/load/vector_db.py

class VectorDBAdapter(ABC):
    @abstractmethod
    def add_chunks(self, chunks: List[Chunk]) -> List[str]:
        pass

    @abstractmethod
    def search(self, query_embedding: np.ndarray, top_k: int) -> List[Tuple[Chunk, float]]:
        pass

class ChromaDBAdapter(VectorDBAdapter):
    # ... implementation for ChromaDB

Data Engineering Principle: The storage layer should be abstracted and scalable. The choice of a data warehouse depends on the use case, and the same is true for vector databases.

4. Query: Retrieval as the New SQL

This is where your end-users interact with the system. In a data warehouse, analysts run SQL queries to retrieve structured data. In a RAG system, the application runs a “semantic query” to retrieve relevant chunks of text.

A simple vector search is often not enough. Just as a good SQL query might involve joins and filters, a good retrieval strategy often involves a hybrid approach. This combines semantic (vector) search with traditional keyword (lexical) search, like BM25. This ensures you get the best of both worlds: retrieving documents that are conceptually similar and those that contain exact keyword matches.

Our retriever.py implements this hybrid strategy.

# src/query/retriever.py

class HybridRetriever(RetrieverStrategy):
    def retrieve(self, query: str, top_k: int) -> List[RetrievalResult]:
        # Get results from both vector and BM25 retrievers
        vector_results = self.vector_retriever.retrieve(query, top_k)
        bm25_results = self.bm25_retriever.retrieve(query, top_k)

        # Combine and rerank results
        # ...
        return combined_results

Once the relevant chunks are retrieved, they are passed to an LLM, along with the original query, to generate a human-readable answer. This is the “Generation” part of RAG.

Data Engineering Principle: Querying should be robust and multi-faceted. Relying on a single query method can lead to incomplete or inaccurate results.

5. Monitor: If You Don’t Measure It, It’s Broken

This is perhaps the most overlooked but most critical stage. You would never run a production ETL pipeline without extensive monitoring, data quality checks, and alerting. The same must be true for RAG.

A RAG system can fail in two main ways: the retrieval can be wrong (it fails to find the right information), or the generation can be wrong (it hallucinates or misinterprets the retrieved information). You must monitor both.

Our evaluator.py module provides tools to measure retrieval quality and response quality.

• Retrieval Metrics: We calculate metrics like Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (NDCG) to evaluate how well the retriever is ranking relevant documents.
• Response Metrics: We check for “hallucinations” by ensuring the generated response is grounded in the retrieved sources.

# src/monitor/evaluator.py

class RetrieverEvaluator:
    @staticmethod
    def evaluate(results: List[RetrievalResult], relevant_indices: List[int]) -> RetrievalMetrics:
        # Calculate MRR, NDCG, Precision@K, etc.
        # ...

class ResponseEvaluator:
    @staticmethod
    def detect_hallucination(response: str, retrieved_chunks: List[Chunk]) -> bool:
        # Check if the response is supported by the source chunks
        # ...

Data Engineering Principle: Monitoring and data quality are not optional. A pipeline without monitoring is a pipeline that is destined to fail silently.

Real-World Case Studies: RAG in Production

To see how these principles apply in the real world, let’s look at how leading companies are implementing RAG systems. These case studies highlight the data engineering challenges and solutions involved.

DoorDash: High-Quality Customer Support Automation

DoorDash uses a RAG-based chatbot to provide support to its delivery drivers (“Dashers”). Their system is a prime example of a mature, monitored RAG pipeline.

• ETL Challenge: Handling a high volume of real-time support conversations and a large knowledge base of articles and past cases.
• RAG Solution:
  • Extract: Ingests live chat data and a knowledge base of support articles.
  • Transform: Summarizes conversations to get to the core issue before retrieval.
  • Load: Uses a standard vector database for the knowledge base.
  • Monitor: This is where DoorDash excels. They have an LLM Guardrail system for real-time quality checks and an LLM Judge for offline evaluation of metrics like retrieval correctness and response accuracy. This is a perfect parallel to data quality monitoring in a traditional ETL pipeline.

LinkedIn: Knowledge Graphs for Customer Service

LinkedIn improved its customer service by using a RAG system that leverages a knowledge graph instead of just plain text.

• ETL Challenge: The context of customer support tickets is often lost when they are treated as isolated text documents.
• RAG Solution:
  • Transform: Instead of just chunking text, LinkedIn constructs a knowledge graph from historical support tickets. This captures the relationships between issues, users, and solutions.
  • Query: The system retrieves relevant sub-graphs from the knowledge graph, providing much richer context to the LLM.
• Result: A 28.6% reduction in the median time to resolve an issue, demonstrating the power of a more sophisticated transformation stage.

Bell: Modular and Scalable Knowledge Management

Bell, a Canadian telecommunications company, built a RAG system to manage its internal company policies. Their approach highlights the importance of a modular and scalable architecture.

• ETL Challenge: Keeping the knowledge base up-to-date with constantly changing company policies from various sources.
• RAG Solution:
  • Architecture: They adopted a modular design with separate services for document ingestion, embedding, and indexing. This is akin to a microservices architecture for an ETL pipeline.
  • Load: The system supports both batch and incremental updates, automatically re-indexing when a document is changed. This is a classic data warehousing problem that they have solved for their RAG system.

Common Pitfalls for Data Engineers Building RAG Systems

Because the RAG-as-ETL analogy is so strong, it also means that the same mistakes that plague data pipelines can bring down a RAG system. Here are some of the most common pitfalls and how to avoid them, viewed through a data engineering lens.

Pitfall 1: Naive Chunking (The “Split-in-the-Middle-of-a-Row” Problem)

• The Mistake: Splitting documents by a fixed number of characters or tokens without regard for the content.
• The ETL Analogy: This is like reading a 1GB CSV file and splitting it every 1MB, right in the middle of a row. The resulting data is corrupt and unusable.
• The Solution: Use semantic chunking. This transformation technique is context-aware and tries to keep whole ideas or paragraphs together. It is more computationally expensive, but it is essential for good retrieval quality.

Pitfall 2: Stale Embeddings (The “Out-of-Sync Dimension Table” Problem)

• The Mistake: Documents in the source of truth are updated, but the embeddings in the vector database are not.
• The ETL Analogy: This is like having a dimension table of customer data that is not updated when a customer changes their address in the source system. All downstream analysis becomes incorrect.
• The Solution: Implement a versioning and refresh strategy for your vector database. When a document is updated, you must trigger a pipeline to re-chunk, re-embed, and re-index that document. This requires a robust data lineage and dependency management system, just like in a modern data stack.

Pitfall 3: No Monitoring (The “Blind Pipeline” Problem)

• The Mistake: Deploying a RAG system without any way to measure its performance.
• The ETL Analogy: This is like running a critical financial data pipeline with no data quality tests, no logging, and no alerting. It is not a matter of if it will fail, but when, and you will have no idea why.
• The Solution: Implement a comprehensive monitoring framework. Track retrieval metrics (MRR, NDCG) to ensure you are finding the right information, and response metrics (hallucination detection) to ensure the LLM is using that information correctly.

Pitfall 4: Treating All Documents the Same (The “One-Size-Fits-All Schema” Problem)

• The Mistake: Using the same extraction and chunking strategy for all document types.
• The ETL Analogy: This is like trying to apply the same flat schema to deeply nested JSON APIs and relational database tables. You lose valuable information.
• The Solution: Develop document-specific transformation logic. A highly structured document like a product specification sheet should be chunked differently from a long-form narrative article. Preserve metadata from the source to guide the transformation process.

The Future of RAG and Data Engineering

The convergence of RAG and data engineering is only just beginning. As RAG systems become more critical to business operations, the need for data engineering rigor will become even more apparent. We will see the rise of “RAG Ops,” a discipline that mirrors DevOps and MLOps, focused on the reliable and scalable deployment of RAG systems.

Data engineers are uniquely positioned to lead this charge. Your skills in building robust, scalable, and maintainable data pipelines are exactly what is needed to turn RAG from a promising prototype into a production-grade system. The tools and concepts may have new names, but the underlying principles are the same.

Conclusion: Your Data Engineering Skills are Your Superpower in the Age of AI

If you have been hesitant to dive into the world of generative AI, I hope this post has shown you that you are already equipped with the right mindset and skills. RAG is not a mysterious black box; it is a data pipeline. It has its own unique set of transformations and storage systems, but the fundamental challenges of data quality, scalability, and monitoring are the same.

By approaching RAG with the discipline of a data engineer, you can build systems that are not only powerful but also reliable, trustworthy, and maintainable. The rag-etl-pipeline project is a starting point, a practical guide to applying your existing expertise to this exciting new field. I encourage you to explore the code, run the examples, and start thinking about how you can build your own production-ready RAG systems.

References

1. Orkes.io. “Best Practices for Production-Scale RAG Systems.”
2. Evidently AI. “10 RAG examples and use cases from real companies.”
3. Towards Data Science. “Six Lessons Learned Building RAG Systems in Production.”
4. Dataquest. “Document Chunking Strategies for Vector Databases.”
5. dbt Labs. “ETL Pipeline best practices for reliable data workflows.”