On September 8, 2025, at Splunk’s annual .conf event, Cisco and Snowflake unveiled an exciting new collaboration: Splunk Federated Search for Snowflake. This partnership represents a major step forward for enterprises operating hybrid and multi-cloud data environments. By enabling Splunk users to query Snowflake data without the need for ETL (extract, transform, load) processes, the integration promises significant performance, cost, and agility benefits.
This spotlight post will break down the announcement, highlight its technical features, discuss real-world use cases, and explore what this means for IT leaders shaping their organization’s data strategies.
Organizations today are challenged by fragmented data ecosystems. Critical operational data may reside in Splunk, while customer and business data lives in Snowflake. Traditionally, connecting these datasets required:
- Exporting data from Snowflake into Splunk.
- Managing ETL pipelines.
- Dealing with delays, duplication, and added storage costs.
The new Splunk Federated Search for Snowflake eliminates these bottlenecks. Instead of moving data, Splunk users can query Snowflake directly, using familiar SPL-like syntax while Snowflake handles the compute-heavy tasks.
Expert insight: Cisco’s VP of Security Strategy commented during the launch, “This partnership is about eliminating silos. Enterprises shouldn’t have to choose between operational insights in Splunk and deep analytics in Snowflake—they should have both, instantly.”
Key Features of Splunk Federated Search for Snowflake
1. Native Federated Queries
Splunk users can now run searches across Snowflake data as if it were part of Splunk’s native environment. This removes the need for costly ETL pipelines.
2. SPL-Like Syntax
For Splunk admins and analysts, the integration feels natural. They can leverage their existing SPL skills to query Snowflake datasets.
3. Compute Distribution
Instead of overloading Splunk, queries are pushed down to Snowflake, where the compute happens. Splunk simply retrieves the results, ensuring scalability and cost efficiency.
4. Hybrid Environment Support
This feature is especially valuable for organizations that operate across on-premise, cloud, and multi-cloud infrastructures.
The Setup Process
Cisco and Snowflake emphasized that setup is designed to be straightforward:
- Connect Splunk to Snowflake through a secure federated connector.
- Authenticate using enterprise identity management (SSO/LDAP).
- Define accessible schemas within Snowflake for federated search.
- Run SPL queries in Splunk that seamlessly pull from Snowflake.
Within hours, enterprises can enable a unified search experience without re-architecting data flows.
Diagram: How the Workflow Operates
+-------------------+ +-------------------+
| Splunk | Query | Snowflake |
| (Federated UI) | -------> | (Data + Compute) |
| | <------- | Results Back |
+-------------------+ +-------------------+
This simple flow shows how Splunk serves as the interface, while Snowflake powers the heavy lifting behind the scenes.
Use Cases: Where It Adds Value
1. Security Analytics
Organizations can enrich Splunk’s operational data with Snowflake’s historical and contextual datasets, leading to:
- Faster threat detection.
- Stronger incident investigation.
- Smarter response automation.
2. Compliance and Governance
Auditors can query compliance data in Snowflake from Splunk without duplicating sensitive records.
3. Business and IT Collaboration
IT teams can blend infrastructure logs (Splunk) with customer and financial datasets (Snowflake) to gain unified insights.
Cost Savings and Efficiency
Federated search has significant financial benefits:
- No ETL Pipelines: Eliminates development and maintenance costs.
- Reduced Data Storage Costs: No need to duplicate Snowflake data in Splunk.
- Optimized Compute: Snowflake’s elastic compute ensures workloads are executed efficiently.
This model aligns with enterprise goals of doing more with less in an era of tighter IT budgets.
Comparing with Traditional Integrations
Traditionally, Splunk-Snowflake integrations relied on:
- Scheduled ETL jobs.
- Data duplication into Splunk indexes.
- Latency in insights.
By contrast, federated search offers:
- Real-time access to Snowflake datasets.
- No duplication or reformatting.
- Lower maintenance overhead.
This marks a shift from data movement to data federation, a trend gaining momentum across the enterprise ecosystem.
Broader Ecosystem Implications
This partnership is more than a product feature—it signals the evolution of enterprise data strategies:
- Convergence of Security and Analytics: IT and security teams can now operate on a single pane of glass.
- Multi-Cloud Normalization: Enterprises no longer have to force data consolidation into a single system.
- Open Collaboration: Cisco, Splunk, and Snowflake’s joint effort shows how cross-vendor integrations drive customer value.
As one Snowflake executive put it, “Federated search is about meeting data where it lives, not forcing it to move.”
Advice for IT Leaders
For IT executives evaluating this integration, here are key takeaways:
- Assess Data Gravity: Keep large datasets in Snowflake where compute is elastic.
- Leverage Existing SPL Skills: Train analysts to extend their expertise into Snowflake without new tooling.
- Prioritize Security and Compliance: Ensure federated connections align with governance frameworks.
- Calculate ROI: Consider both the cost savings from reduced ETL and the productivity gains from faster insights.
Conclusion
The launch of Cisco’s Splunk Federated Search for Snowflake represents a breakthrough for hybrid data environments. By combining Splunk’s real-time operational intelligence with Snowflake’s scalable analytics, enterprises can unlock powerful new insights without sacrificing speed, security, or cost efficiency.
For IT leaders, this integration is not just a technical upgrade—it’s a strategic opportunity to modernize data architectures and align security, analytics, and business intelligence under one unified approach.