Introduction
As Snowflake Summit 2025 approaches, data engineers have much to be excited about. Snowflake has made substantial strides in developer experience, GenAI integration, unstructured data support, and governance — but several capabilities still have headroom for advancement. Here are eight key areas where we already see strong momentum, and what we might hope to see next, based on current public documentation and platform behavior.
1. Copilot: Moving Beyond SQL Assistance
Snowflake Copilot, introduced in 2024, already supports natural language-to-SQL generation and data understanding within Snowflake Studio. However, its capabilities today are largely confined to query assistance and metadata explanation.
What to watch for: A potential expansion into the full data engineering lifecycle. For example, Copilot might soon help recommend optimal clustering keys, generate dbt-compatible model code, or suggest task graph orchestration based on lineage context. Imagine being able to ask: “Can you help me model this table for daily incremental loads?” and getting runnable code in return.
2. Advanced Workflow Orchestration
Snowflake Tasks and Task Graphs already allow dependency-based orchestration. But today, retry logic, branching, or conditional execution all require manual SQL scripting or workarounds.
What to watch for: Snowflake could introduce visual orchestration tooling or native YAML-based DAG definitions with retry policies and failure alerts — similar to Airflow. This would simplify complex data pipelines and reduce operational overhead, especially for multi-stage ETL workflows.
3. Native Data Contracts
While Snowflake has strong schema management, it lacks native support for data contracts — i.e., formal guarantees between producers and consumers about schema, freshness, and quality.
What to watch for: A native implementation of data contracts could allow users to define expected schema versions, freshness intervals, and validation rules (e.g., “email must not be null”) with enforcement via policies or alerts. For instance, if a pipeline changes a column type, Snowflake could block deployment or notify impacted consumers automatically.
4. Git Integration: Solid Start, but Network Constraints Remain
Snowflake Studio now supports Git integration for importing code into Notebooks, UDFs, stored procedures, and Streamlit apps. However, push operations are limited (primarily to Streamlit), and Git access is only available over public networks.
What to watch for: Support for secure, private network Git access (e.g., VPC-connected or VPN-protected Git servers) would be a major step forward for enterprise DevOps compliance and secure collaboration.
5. Smarter Cost Optimization
The Snowsight UI offers built-in dashboards to monitor warehouse usage, query trends, and spend. It also supports Resource Monitors for setting usage limits and alerts. However, proactive, AI-driven optimization still requires external tools or custom setups.
What to watch for: AI-driven advisors that detect underutilized warehouses, recommend query optimizations, or simulate the impact of warehouse resizing. For example, a data engineer could receive suggestions like: “This materialized view hasn’t been queried in 14 days. Consider suspending refresh.”
6. Deeper Unstructured Data Intelligence
Snowflake supports basic OCR-based text extraction from documents and images (JPEG, PNG, TIFF) via its Document AI feature. However, it currently has limitations around language support, file size (max 50 MB), and document length (up to 125 pages).
What to watch for: Native support for prebuilt unstructured processing models — such as running OCR over stored images, transcribing call recordings, or extracting tables from PDFs — could make Snowflake a full-service platform for document-heavy workflows, particularly in finance, insurance, and legal domains.
7. Visualization Ecosystem Expansion
Snowflake integrates well with tools like Tableau, Power BI, and Looker. However, in-platform data visualization is limited, aside from Notebooks and Streamlit.
What to watch for: Deeper native capabilities — such as reusable dashboard components, real-time monitoring panels, or plug-and-play embeddable charts — could turn Snowflake into a one-stop interface not just for data prep, but for basic consumption.
8. OpenAI Integration and Keynote Signals
One of the most anticipated aspects of this year’s summit is the presence of Sam Altman, CEO of OpenAI, as a keynote speaker. His inclusion hints at potential announcements around deeper integration between Snowflake and foundation model providers like OpenAI.
What to watch for: Enhanced Cortex capabilities that go beyond prebuilt functions, such as embedding OpenAI APIs directly into pipelines, leveraging fine-tuned GPTs for data classification, or building natural language interfaces for querying large enterprise datasets. The keynote may provide insight into how Snowflake envisions partnering with GenAI vendors to make intelligent data operations truly accessible.
Closing Thoughts
Snowflake has taken big steps in platform extensibility and developer friendliness, but 2025 could be the year it shifts from being a warehouse-centric product to a complete, intelligence-driven data engineering platform. If even a few of these enhancements are announced at Summit, they could have immediate impact on how data engineers’ model, deploy, monitor, and collaborate across modern data stacks.
For those attending the summit or following the announcements, these areas are worth keeping an eye on — not just to stay ahead of the curve, but to rethink what’s possible within the Snowflake ecosystem.
