At EdgeRed, we believe that great data solutions come from staying closely connected to the platforms and communities shaping the modern data landscape. As a data analytics and consulting company in Australia, we work with organisations to design scalable data platforms, modern analytics solutions, and cloud-based data architectures.

Members of our team attended Snowflake SPN Connect Sydney, an exclusive event designed for partners within the Snowflake partner ecosystem. Our data analysts and consultants joined industry peers in Sydney to explore the latest innovations in the Snowflake Data Cloud, discuss emerging data platform trends, and learn how organisations are leveraging Snowflake to unlock greater value from their data.

What is Snowflake SPN Connect?

SPN Connect is a partner-focused event that brings together organisations that build, implement, and support solutions on the Snowflake platform.

The event focuses on strengthening collaboration across the Snowflake Partner Network while sharing insights into:

• The latest developments in the Snowflake Data Cloud
• New capabilities supporting data engineering, analytics, and AI
• Real-world customer success stories
• Best practices for building scalable cloud data platforms

For companies delivering Snowflake consulting services in Australia, events like this provide valuable opportunities to hear directly from the teams building the platform and from other partners implementing Snowflake solutions in the field.

Why Events Like This Matter for Data Consulting Teams

The data and analytics ecosystem is evolving rapidly. Organisations are investing heavily in modern data platforms, AI-powered analytics, and cloud-native data architectures to become more data-driven.

Attending partner events like SPN Connect helps consulting teams stay ahead of these changes by connecting with:

• Product experts shaping the future of the Snowflake platform
• Other partners delivering enterprise-scale Snowflake implementations
• Organisations using the Snowflake Data Cloud in innovative ways

For our consultants at EdgeRed, these conversations often translate into practical ideas that help clients design better data architectures, improve data governance, and build more powerful analytics capabilities.

Key Announcements from Snowflake

Product announcements stood out from Snowflake

Cortex Code (or bespoken CoCo) – this is pretty amazing and interesting as Snowflake claimed this is better than Claude code. It looks like this will make snowflake easier to use 

Roadmap updates for the Snowflake Data Cloud

Snowflake is mostly now trying to be a leading player in the AI regiment, and it looks like more capabilities / products will be around this area. 

Trends in Modern Data Platforms

Common themes across sessions and partner discussions

A clear throughline across sessions was AI’s disruption of the data application layer. Tools like Snowflake Intelligence are reshaping how both technical and non-technical users interact with data, putting self-serve analytics and visualisations directly in the hands of the people who need them, without requiring data team involvement. Rather than replacing data teams, this shift amplifies their impact: instead of fielding ad hoc requests, they can focus on building robust data products that the wider organisation can explore autonomously. The overarching message was consistent, AI is compressing the time between a business question and a data-backed answer.

How are organisations approaching AI within their data platforms?

Organisations are finding value in AI at two distinct layers of their data platforms: the development layer and the consumption layer.

On the development side, AI tooling is being adopted to improve the speed and quality of engineering work. From query optimisation to pipeline code review, AI assistants are catching issues earlier in the development cycle; the kind of subtle bugs that historically only surface at 2am when a critical ingestion job fails in production.

On the consumption side, the focus is on democratising data access for business users. Rather than navigating multiple dashboards or raising requests with the data team, users can now interrogate their data through natural language. This directly addresses one of the most persistent goals in data, getting the right information to the right people at the right time and tools like Snowflake Intelligence are making that a practical reality rather than an aspiration.

Common challenges companies are facing when scaling their analytics environments

One of the most significant risks organisations face when scaling is investing in AI before their data foundations are ready to support it. AI does not fix poor data quality, inconsistent schemas, or fragmented pipelines — it amplifies them. Organisations that layer AI tooling onto an immature data platform often find that the outputs are unreliable, trust erodes quickly, and the cost and complexity introduced by AI initiatives outweighs any value delivered. The hard truth is that AI is only as good as the data beneath it, and scaling AI without first scaling data maturity is one of the most common and costly mistakes being made right now.

A related challenge is scaling without anchoring every decision to a clear business outcome. New data sources, near real-time ingestion pipelines, and expanded platform capabilities are only valuable if they are directly tied to a specific business requirement. When teams build infrastructure for its own sake — or in anticipation of hypothetical future needs — platforms quickly become siloed collections of data assets with no clear owners, no coherent purpose, and no measurable impact. At scale, this becomes very difficult to unwind.

Cost management and observability go hand in hand as platforms grow. Cloud-native architectures offer enormous flexibility, but complexity scales faster than most teams anticipate. Without full observability across the platform, hidden costs accumulate — inefficient queries running unnoticed, bottlenecks quietly degrading pipeline performance, poorly designed workloads consuming disproportionate compute, and entire datasets sitting unused. As platforms mature, observability is no longer a nice-to-have; it is the mechanism by which teams retain control of a system that has grown too large to manage by intuition alone.

Practical Takeaways for Snowflake Users

Advice for organisations currently using Snowflake

As Snowflake continues to introduce new native AI capabilities, organisations are presented with multiple ways to build and deploy solutions whether thats directly within Snowflake or even through other external tools / platforms. Before implementing any new capability, it is important to carefully evaluate the pros and cons of each approach to determine the most appropriate solution.

Best practices for building scalable data platforms

Organisations should also remain mindful of credit consumption and associated costs. Monitoring usage throughout the building phase before implementation helps ensure resources are being used efficiently and allows teams to optimise both cost and performance.

It is also critical to establish strong data governance practices before rolling out new AI tools. This includes implementing data classification and tagging for sensitive data, as well as enforcing controls such as masking policies and row level security to protect sensitive information to ensure proper access management.