No Data Left Behind: How Splunk Federated Search Helps Teams Build AI-Ready Data Without Moving Everything

Platform July 22, 2026 Beverly Smart

Key takeaways

Splunk Federated Search lets teams analyze data across multiple platforms without moving it into Splunk, helping reduce complexity and cost.
Teams can search data where it lives using one interface, giving AI and analysts a more complete and trusted view of their environment.
As part of Cisco Data Fabric, Federated Search helps organizations build an AI ready data foundation with connected, governed, and auditable data.

AI adoption is rapidly accelerating, yet many organizations struggle with scattered operational data—logs, traces, events, identity data, vulnerabilities, asset context, and business signals that reside across multiple platforms and storage layers. This fragmentation makes it difficult for AI to have a complete, trustworthy view of the environment—a gap that becomes critical as agentic AI moves from pilot to production.

Federated Search addresses this challenge by enabling teams to analyze data where it lives—across Amazon S3, Azure Blob, Azure Data Lake, Databricks, Snowflake, and Splunk archive solutions—without the need to move or ingest all data into Splunk. This capability maintains a unified analyst experience and consistent workflow across distributed environments. During a recent webinar, "No Data Left Behind: Next-Generation Splunk Federated Search," Splunk product experts, Aditya Tammana Leader, Product Management and Paul Davies, Director, Global Platform Specialists discussed why connected, governed data matters for resilience, visibility, and safer AI adoption as machine data grows across hybrid environments.

Traditional data access falls short due to siloed data lakes and archives with inconsistent schemas, formats, and access controls. This results in limited visibility, uneven data readiness, and organizational complexity that hinder observability, security, and AI use cases.

Splunk Federated Search transforms this landscape by:

Querying across diverse data stores using a single query language (SPL2) and interface.
Extending Splunk analytics to external data lakes and warehouses without ETL or data duplication.
Supporting schema-on-read to handle raw and structured data dynamically.

Enabling organizations to apply different data strategies: keeping high-value data in Splunk for fast operational use, while querying lower-value or infrequently accessed data in cost-effective storage.

This flexible, scalable approach ensures AI has access to decision-ready, connected, governed, and auditable data—empowering confident AI-driven insights and actions without the overhead of moving all data into a single repository.

Available today on the Splunk Cloud Platform, the next generation of Federated Search—a key component of Cisco Data Fabric—builds an AI-ready data foundation that balances performance, cost, and completeness across distributed environments. By serving as a trusted intelligence layer for the agentic enterprise, it enables teams to analyze data where it resides, reducing duplication, migration efforts, and costs while fostering a shared operational view; you can explore the full insights from the recent Federated Search webinar to learn more.