What Splunk Federated Search for Snowflake Means for Data Engineers and Security Teams

Every enterprise today faces a familiar challenge: too much data, in too many places. From hybrid clouds to SaaS platforms and IoT devices, vital business and machine data now live across multiple environments. The result? Silos that make it harder to gain visibility, respond to threats quickly, and make confident, data-driven decisions. 

For data engineers and security professionals, the consequences are serious, delays in triage, blind spots in analysis, and inefficiencies in incident response. 

That’s why the recent collaboration between Cisco, Splunk and Snowflake is making waves. The launch of Splunk Federated Search for Snowflake, announced at .conf25, enables organisations to unify business and operational data without moving it. By bringing together Snowflake’s cloud-scale analytics and Splunk’s real-time observability, teams can achieve faster insights and stronger security outcomes. 

In this blog, we’ll explore how federated search Splunk transforms data workflows, what this integration means for modern security teams and engineers, and how professionals can build the expertise to thrive in this new data landscape. 

The Power of Federated Search: Connecting Splunk and Snowflake 

So, what exactly is federated search? In simple terms, it’s the ability to query data across multiple sources, like Splunk and Snowflake, from one interface, without needing to move or duplicate the data. 

This means a security analyst can use Splunk’s familiar interface to reach into Snowflake’s datasets and combine operational logs with business insights. For example: 

• A SecOps team monitoring login events in Splunk can now merge that data with customer activity in Snowflake to detect unusual behaviour patterns. 

• An IT engineer can correlate infrastructure performance logs with financial data to better understand the impact of outages or inefficiencies. 

  
  

The result is unified visibility, connecting data across silos, teams and tools to power faster, more informed decisions. 

Key Benefits of the Integration 

Unified Analytics 

Combining Splunk and Snowflake delivers a single source of truth for both operational and business data. 

Example: A fraud-prevention engineer can query payment transactions (Snowflake) alongside application logs (Splunk) to detect anomalies missed previously. 

Faster Decision-Making 

With fewer silos and simplified data access, teams can identify and respond to issues faster. 

Example: A security incident that once took hours to analyse can now be resolved in minutes. 

Operational Efficiency 

No more unnecessary data movement or duplication. Federated queries let teams analyse what matters without importing full datasets. 

Example: Engineers can query Snowflake directly from Splunk and only retrieve relevant insights, saving time and cost. 

Open, AI-Ready Ecosystem 

This integration supports hybrid, vendor-neutral architectures ideal for the AI-driven enterprise. It allows Splunk’s real-time capabilities to pair seamlessly with Snowflake’s scalable compute power, setting the stage for AI-assisted automation and predictive analytics. 

These benefits make Splunk Federated Search for Snowflake more than a feature upgrade, it’s a strategic step toward a truly connected, intelligent enterprise. 

At Ingeniq, we help professionals stay ahead of the curve through our industry-recognised Splunk training programs.  

Explore our Splunk training courses today 

Why This Matters in the Age of Agentic AI

 

As organisations embrace AI agents and autonomous workflows, access to unified, high-quality data becomes the foundation. Without it, AI projects stall or deliver sub-par results. Research underscores this risk: fragmented data weakens model performance and undermines automation. 

The Splunk + Snowflake integration supports these agentic AI workflows by giving AI systems the broadest possible view of business and operational data, so AI agents can act with context, accuracy and speed. For professionals, this means specialising not just in managing data but in enabling AI-ready data pipelines. Understanding this evolution is key for Splunk engineers, data engineers and security professionals alike. 

Inside the Integration: How Splunk Federated Search for Snowflake Works 

Here’s a breakdown of what this integration offers: 

• Easy Onboarding: Snowflake is added as a data source in Splunk with minimal friction. 

• Federated Queries: Splunk’s SPL-like queries can target Snowflake tables directly, enabling analysts to use familiar language. 

• Data Joins: Analysts can combine Snowflake datasets with Splunk’s machine data for deeper business-context analytics. 

• Efficiency Gains: Snowflake handles heavy analytics work; Splunk handles real-time correlation and alerting, optimising both platforms. 

  
  

At .conf25, Splunk and Snowflake offered a live demo showing how AWS S3 and Snowflake data could be fed into Splunk’s federated search engine, highlighting how organisations can break down silos and accelerate triage. 

For learners at Ingeniq, mastering how Splunk handles federated queries and hybrid data sources becomes a differentiator in today’s data ecosystem.

The Bigger Picture: Cisco, Splunk, and the Open Data Future

This integration also aligns with Cisco’s vision of an open, connected data ecosystem. By partnering with Snowflake and enabling Splunk to query external data natively, Cisco and Splunk reinforce their leadership in observability, security and data resilience. 

The move complements other Splunk innovations in the AI era, such as federated analytics, Time Series Foundation Models and AI-capable data fabrics. It’s not just about tools, it’s about architecture: the future is hybrid, distributed and AI-first. 

For organisations, that means investing in architectures and skills that support these complex flows. For professionals, it means being proficient in how Splunk connects, correlates and operationalises data across systems. 

What It Means for Splunk Learners and Professionals 

If you're a data engineer, security analyst or Splunk professional, this integration signals a clear trend: data tools are consolidating, AI is rising, and skills must evolve accordingly. 

Security teams gain new capabilities: unified detection with business context, faster incident response and advanced analytics. Analysts trained on Splunk’s federated capabilities will stand out in hybrid, AI-driven SecOps environments. 

At Ingeniq, our authorised Splunk training covers core platforms like Splunk Enterprise Security, SOAR automation, data onboarding and federated analytics. We help learners build the technical frameworks and context to thrive in environments where Splunk and Snowflake are connected, data flows freely and AI agents act with insight. 

Conclusion 

The launch of Splunk Federated Search for Snowflake marks a significant step toward smarter, faster and more connected data ecosystems. Unified data across business and machine domains underpins faster decision-making, smoother operations and robust AI workflows. 

For data professionals and security teams, this is the moment to level up. Understanding the integration, and the skills it demands, sets you apart in a rapidly shifting landscape. 

At Ingeniq, we’re proud to help professionals harness the full potential of Splunk and enable them to lead in this new data era.

 

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