2025: The Year of AI in the Data Cloud

Happy New Year! As we kick off 2025, the landscape of data engineering and analytics is shifting more rapidly than ever. If 2023 was the year of Generative AI hype, and 2024 was the year of experimentation, 2025 is poised to be the year of production.

For us in the Snowflake ecosystem, the “Data Cloud” is rapidly becoming the “AI Data Cloud”. The convergence of data and compute has always been Snowflake’s superpower, but now that compute includes governed, secure access to state-of-the-art Large Language Models (LLMs) right where our data lives, the game has changed.

In this article, I’ll explore what I believe will be the defining themes for Snowflake in 2025 and how we, as data engineers, should prepare.

The Shift to “AI-First” Data Engineering#

Traditionally, our job as data engineers was to move data from point A to point B, transform it, and model it for BI dashboards. While that core responsibility remains, the destination is no longer just a dashboard—it’s vector stores, feature stores, and inference endpoints.

In 2025, successful data engineers will need to become AI Engineers. We won’t just be building pipelines for numbers; we’ll be building pipelines for meaning. This means:

• Standardising unstructured data (PDFs, images, logs) as a first-class citizen.
• Managing embeddings alongside relational tables.
• Orchestrating model inference as part of our ELT processes.

Snowflake Cortex: No Longer Just a Cool Feature#

Snowflake Cortex has matured significantly. It’s no longer just a wrapper for hosted models; it’s becoming the operating system for enterprise AI.

We are seeing a massive adoption of Cortex functions for everyday data quality tasks. Why write complex Regex to extract information from a messy text field when you can just ask Llama 3?

-- The old way: Complex REGEX (fragile)
SELECT
    REGEXP_SUBSTR(email_body, 'Order ID: ([0-9]+)', 1, 1, 'e') as order_id
FROM support_tickets;

-- The 2025 way: Cortex COMPLETE (resilient)
SELECT
    SNOWFLAKE.CORTEX.COMPLETE(
        'llama3-70b',
        'Extract the Order ID from this email text. Return ONLY the ID number: ' || email_body
    ) as order_id
FROM support_tickets;

This shift from deterministic code to probabilistic AI calls in SQL is huge. It drastically reduces development time for unstructured data processing, but it introduces new challenges in cost management and testing—topics we’ll cover extensively this year.

RAG is the New ETL#

Retrieval-Augmented Generation (RAG) is the architecture of choice for making LLMs useful in the enterprise. In 2025, building a RAG pipeline in Snowflake is becoming native.

With the Vector Data Type and Cortex Search, we don’t need external vector databases like Pinecone or Weaviate for many internal use cases. We can keep the data governance barrier intact.

Expect to spend a lot of time this year optimising “Context Pipelines”—ensuring that the chunks of text you feed into your vector store are high-quality, up-to-date, and strictly governed.

Governance Gets Smarter with Horizon#

As we unleash AI agents on our data, governance becomes terrifyingly important. If an AI agent has access to your data warehouse to answer user questions, how do you ensure it doesn’t accidentally reveal salaries or PII?

Snowflake Horizon is the answer here. In 2025, we’ll see a move towards “Policy-as-Code” where data access policies, masking policies, and row-level security are defined programmatically and applied automatically based on tags.

-- Example of the direction we are heading
CREATE OR REPLACE MASKING POLICY pii_mask AS (val string) RETURNS string ->
  CASE
    WHEN current_role() IN ('FULL_ACCESS') THEN val
    ELSE '***MASKED***'
  END;

-- Auto-tagging and policy application
ALTER TABLE customer_data
MODIFY COLUMN email
SET TAG privacy_category = 'PII';

Conclusion#

2025 is going to be an exciting year. The tools at our disposal are more powerful than ever. Our challenge is no longer “can we do this?” but “should we do this, and how do we do it securely?”

Stay tuned to the blog this year. I’ll be diving deep into these topics with practical, hands-on guides to help you navigate the AI Data Cloud. Next week, we’ll start getting our hands dirty with a “Getting Started” guide for Snowflake Cortex.

Welcome to 2025!