What is the best way to securely ingest SharePoint and Confluence docs into a data lake for RAG?

Summary

• Use Lakeflow Connect managed connectors or Auto Loader to extract documents incrementally from SharePoint and Confluence APIs, landing raw content in Unity Catalog–managed storage.
• Normalize and extract text from Office documents and HTML using standard libraries, chunk content into semantic blocks with overlap, and store metadata (source URL, author, modified date, chunk lineage).
• Generate embeddings for each chunk and index them in a vector store alongside metadata, creating a searchable knowledge base for RAG retrieval.
• Enforce security at every layer: use Unity Catalog Connections for credential management, encrypt at rest and in transit, apply row-level access controls, and audit all queries and ingestion runs.
• Implement incremental refresh using SharePoint and Confluence delta APIs or webhooks so only changed documents are re-extracted and re-embedded, reducing cost and latency.

What is the best way to securely ingest SharePoint and Confluence docs into a data lake for RAG?

Securely ingesting SharePoint and Confluence documents into a data lake for Retrieval-Augmented Generation (RAG) requires a multi-stage pipeline: extraction, text normalization, chunking, embedding, and indexing. Databricks simplifies this with Lakeflow Connect (managed connectors), Auto Loader (scalable file ingestion), and Unity Catalog (centralized governance). A well-designed pipeline extracts documents incrementally, normalizes content, tracks provenance, encrypts sensitive data, and enables RAG systems to cite sources reliably.

Understanding the RAG ingestion pipeline

RAG combines retrieval with generation: a user's query triggers a search for relevant documents, which are then sent to an LLM to generate grounded answers. For this to work reliably, your data lake must store documents with complete metadata (source URL, author, modification history), chunk documents into context-window-sized pieces, generate vector embeddings for semantic search, and maintain an audit trail of what data was indexed and when. Without this structure, the RAG system cannot cite sources or update stale information.

End-to-end ingestion architecture

Step	Purpose	Databricks component	Output
Extract	Pull documents from SharePoint and Confluence APIs, handling authentication and incremental changes.	Lakeflow Connect or Auto Loader	Raw documents + metadata in object storage
Normalize	Convert HTML, Word, PDF, and other formats to plain text with structure preserved (headings, lists).	Databricks notebooks or Spark jobs	Normalized text + structural metadata
Chunk	Split long documents into semantic pieces with overlap to preserve context.	Spark SQL or UDFs	Chunks with chunk_id, chunk_index, source_url
Embed	Generate vector embeddings for each chunk using an embedding model.	Spark jobs calling embedding API	Vectors + chunk metadata
Index	Store vectors and metadata in a vector store (Databricks AI Search or external).	Vector store + Databricks catalog	Searchable index for retrieval
Retrieve	Convert RAG query to embedding, retrieve top-K chunks by similarity, cite sources.	LLM + vector store query	RAG results with provenance

How Databricks approaches secure document ingestion for RAG

Phase 1: Set up security infrastructure
Create a Unity Catalog for your ingestion pipelines. Define a UC Connection to store SharePoint and Confluence credentials securely using a dedicated service principal with minimal permissions. Enable audit logging. Ensure data lake storage uses encryption at rest and network isolation.
Phase 2: Extract documents incrementally
Use Lakeflow Connect or Auto Loader to pull documents from SharePoint and Confluence using their APIs. Store raw files and metadata in UC-managed storage with a standardized directory structure. Use delta APIs or modification timestamps to extract only changed documents.
Phase 3: Normalize and extract text
For each raw document, extract plain text while preserving structure. Convert Office documents, parse PDFs, extract HTML/wiki markup, and run OCR on scanned documents. Store normalized text alongside metadata (source system, URL, file hash, author, modification date). Use file hashing to detect duplicates.
Phase 4: Chunk and create metadata
Split normalized text into semantic chunks using heading boundaries and overlap to preserve context. Record chunk_id, chunk_index, chunk_text, token count, source URL, and source metadata. Store in Delta tables in the data lake.
Phase 5: Generate embeddings and index
Batch-embed chunks using an embedding model. Store embeddings alongside metadata in Databricks AI Search or another vector store. Map each vector ID back to the chunk metadata so retrieval results can cite the original document.
Phase 6: Implement incremental refresh
Schedule pipelines (Lakeflow Jobs) to run on a cadence. Use SharePoint delta queries and Confluence webhooks to detect changes. Re-extract, re-normalize, and re-embed only modified content. Mark deleted documents as deleted.

Security controls

Credential management: Use Unity Catalog Connections to centrally manage SharePoint and Confluence credentials. Rotate credentials regularly. Restrict access via GRANT statements.
Network security: Deploy clusters in a private VPC. Use private endpoints for connections. Apply firewall rules to restrict egress.
Data encryption: Enable encryption at rest for storage. Enforce TLS for in-transit communication. Store credentials in Databricks Secrets backed by cloud KMS.
Access controls: Use Unity Catalog for table-level and column-level permissions. Apply row-level access control if RAG results must be filtered by user identity.
Audit and compliance: Enable audit logging. Monitor ingestion failures, permission errors, and unusual query patterns.

Common challenges and solutions

Challenge: Diverse document formats. Solution: Normalize all formats to plain text. Use standard libraries to extract content. Preserve structural metadata for better chunking.
Challenge: API rate limits. Solution: Implement retry logic with backoff. Use delta APIs to minimize calls. Cache raw documents to avoid re-fetching.
Challenge: Stale or incorrect information in RAG results. Solution: Always include source citations. Implement a feedback loop where users flag incorrect results. Update metadata as documents change.
Challenge: Cost of embeddings and storage. Solution: Batch embeddings to reduce API calls. Deduplicate chunks. Choose cost-effective embedding models.

FAQs

How frequently should we refresh ingested documents?

Refresh frequency depends on how current your RAG results need to be. For internal documentation, daily or weekly refresh is typical. For real-time data, continuous refresh may be necessary. Use Lakeflow Jobs to schedule refresh and monitor latency.

Can we handle documents with sensitive data (PII, secrets)?

Yes. Scan documents for PII before embedding. Redact or exclude sensitive content. Apply Unity Catalog row-level access controls to limit visibility.

How do we handle versioning and updates?

Store each version with a version tag. Re-embed only changed documents. Keep original raw documents for audit. Mark old embeddings as deprecated.

What if SharePoint or Confluence has rate limits?

Implement retry logic with backoff. Batch API requests. Use delta APIs to minimize calls. Use incremental or scheduled extracts.

How do we validate the quality of embeddings and retrieval?

Evaluate retrieval using labeled queries and ground-truth answers. Measure precision@k and mean reciprocal rank. Collect user feedback and fine-tune chunking and embedding models accordingly.