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[2026] AI_PARSE_DOCUMENT of Databricks OR Snowflake? Enabling RAG on your Data
As RAG (Retrieval-Augmented Generation) pipelines mature, one thing is becoming increasingly clear:
Document parsing quality matters more than model choice.
Both Databricks and Snowflake now offer a state-of-the-art, LLM-powered function called AI_PARSE_DOCUMENT. I spent time experimenting with both to understand where each one shines — and where trade-offs appear.
This newsletter is a hands-on, use-case-driven comparison, not a marketing pitch.
This edition is part of 2nd of 15 AI Services Comparison offered by both - Databricks and Snowflake
🔍 Output Representation: HTML vs Markdown
One of the most important differences lies in how parsed content is represented.
Why does this matter?
For LLM-based workflows:
HTML can be useful for rendering, but for LLM comprehension and downstream parsing, Markdown often wins.
📄 Supported File Types
Databricks AI_PARSE_DOCUMENT
Snowflake AI_PARSE_DOCUMENT
Snowflake currently supports a broader range of document formats, which can be helpful in enterprise ingestion pipelines.
✂️ Page-Level Parsing (A Practical Advantage)
Snowflake provides native support for page-level splitting, which is extremely useful for large documents:
'page_filter': [{ 'start': 0, 'end': 1 }]
This allows:
I wasn’t able to achieve the same level of clean page filtering in Databricks using the documented SQL syntax.
🤔 So… Which One Is Better?
That depends on what you optimize for.
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If your priority is:
👉 Snowflake’s Markdown output feels more natural
Markdown works seamlessly inside Snowflake notebooks, other IDEs, and even when consumed downstream in Databricks-based pipelines.
🤨 Does That Mean Databricks Isn’t Good Enough?
Absolutely not.
Databricks offers capabilities that Snowflake currently doesn’t expose natively:
These are valuable if:
That said, the real architectural question is:
Do you actually need rendered images for RAG when answering primarily from text-heavy documents?
If you know the answer, your platform choice becomes much clearer.
Also worth noting: both platforms provide multiple ways to extract and process images, either natively or via custom pipelines.
About the Writer
As a recognized Snowflake Data SuperHero (2023–Present) and seasoned Cloud Data Engineering Leader, I bring 8+ years of experience delivering enterprise-grade data platforms across BFSI, Manufacturing, Aviation, and Pharma sectors. My journey has been defined by building scalable data lakes, optimizing cloud performance, and enabling strategic business outcomes through modern data architectures.
At KPMG India, I led the setup of the firm’s Snowflake capability from the ground up—developing 3 reusable assets (ETL Framework, Access Management Studio, Cost Containment Guide), training 150+ professionals, and enabling 50+ certifications. I’ve driven multi-million-dollar engagements, including a Cybersecurity Data Lake for a Fortune 100 manufacturer, integrating Kafka, Snowflake, and Python for real-time streaming and governance.
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The trade-offs between AI_PARSE_DOCUMENT across platforms are fascinating. Curious to see how this evolves as native vector and governance features mature.