Universal Federated Search: Query All Data and Reduce Costs

The data dilemma harassing SOC teams continues to compound as volumes increase, sources diversify and disparate data stores are scattered wide across geographies. But such data abundance brings value to investigation with more context and deeper understanding of your risks. Federated search is quickly becoming a necessity to find harmony, by tempering the complexity of data sprawl and diving deep to unlock comprehensive security insights.

This article will help you first understand the concept of Federated Search, the value it delivers but also the avoidable headwinds associated with it. Which will lead us to the concept of universal federated search—uniquely poised to deliver the most value.

Federated search is a capability that allows users to search across multiple data sources easily through a single search interface. Here are the key points about federated search:
Single Search

  1. Interface: Federated search provides a unified search box or interface where users can enter their queries for all data. This eliminates the need to search each data source individually.
  2. Data Remains Distributed: Unlike a centralized search index, federated search does not require data to be physically consolidated. The data remains in its original distributed locations.
  3. Straightforward Search Connectors: Federated search relies on simple connectors to communicate with each data source.

The main benefits of federated search include:

  • Improved user experience by providing a single search interface
  • Access to information across disparate data silos without data duplication
  • Ability to search new data sources by simply adding connectors
  • Potential cost savings by avoiding data consolidation and transfer fees

Federated search is particularly useful for organizations with large volumes of data scattered across various repositories, databases, cloud environments, and geographies. It provides analysts and hunters a unified search experience without the overhead of manually logging into multiple interfaces or consolidating all data into a single centralized location.

How to Drive Down Data Costs with the Only Cost-Optimized SIEM

What Federated Search Solves

Depending on the vendor there are some key challenges that federated search can solve.

Data Silos and Fragmentation:

Organizations often have data scattered across various databases, repositories, cloud environments, and other siloed sources. Federated search enables users to search and access this distributed information through a single unified interface, eliminating the need to search each source individually.

Inefficient Information Discovery:
Without federated search, users must login to multiple interfaces and switch between different systems and applications to find the information they need, which can be time-consuming and inefficient. Federated search streamlines the process by allowing users to search across multiple sources from a single interface and login, improving productivity and enabling faster access to relevant data.

Redundant Data Storage:
Federated search avoids the need for redundant data storage by querying the original data sources directly, without requiring data transfer, rehydration, consolidation or duplication. This can lead to cost savings and reduced storage requirements.

Integration of New Data Sources:
As an organization’s data landscape evolves, federated search solutions can adapt by integrating new data sources or updating existing connectors. This scalability allows organizations to maintain a comprehensive search capability as their data sources grow or change.

Security and Access Control:
Federated search respects the security policies and access controls defined within each individual data source, ensuring that users can only access the information they are authorized to view. This helps organizations maintain compliance with data privacy regulations and internal policies.

Federated search has superior search capabilities that can cut through various data silos and fragmented sources to pinpoint critical and specific information with less time and effort while saving on costs.

Compliance and Data Queries

Federated search can help you maintain compliance. Data privacy laws like Europe’s GDPR (General Data Protection Regulation), Canada’s PIPEDA (Personal Information Protection and Electronic Documents Act) and California’s CCPA (California Consumer Privacy Act) provide strict rules on how private data is handled in order to stay compliant. With that in mind, data sovereignty requires data to stay within a country’s borders to maintain compliance. With federated search, data is not physically moved or duplicated from its original location. This helps avoid violations of data localization laws when the data is queried from another country. Data masking can be used to stay compliant by replacing sensitive personal information like names, addresses, identification numbers etc. to prevent unauthorized access or accidental exposure of data.

Federated search still faces some challenges. Different data sources may have varying data structures, schemas, and formats, making it difficult to integrate and present search results in a consistent and unified manner. Data can be stored in different eco-systems with different logins required to access and query the data. Normalizing and mapping data fields across heterogeneous sources is a complex task.

Query translations can be a blocker as each data source might support different query languages, syntax and capabilities. Translating a user’s query to the right format for each source while preserving the intended meaning poses a significant challenge.

Federated search needs to be conducive to the security policies, access controls and permissions afforded within each individual data source ensuring only authorized users have access.

Most organizations face these challenges: 

  • Ingestion volume-based licensing models, make it too costly to get the data needed for investigations.
  • Data residency compliance requirements, which requires cross-cloud or restricted cross-region data transfers to a centralized location or manual logins to different applications.
  • High data transfer and duplication costs, which inhibit centralized data and log collection.
  • Disparate data sources, make it challenging to harness valuable insights while increasing the risk of missed detections or timely responses.

Most federated search solutions have some costs associated with it, many that are avoidable (read on to learn how). For instance, sometimes data cannot be queried until it is taken out of cold storage and hydrated—avoidable. This can lead to more costs especially if a large amount of data has to be hydrated to be searched to find the small amount of data that is sought. For example Splunk’s federated search capability for S3, which involves a “Data Scan Unit” license that allows scanning a certain amount of data (e.g., 10TB) from S3. There may be costs associated with scanning or hydrating data from object storage services like S3. Vendors like Splunk only allow federated search within their own ecosystem which can limit visibility and the ability to threat hunt effectively.

Data Transfer Costs

Data transfer costs can add up. While federated search avoids the need for data duplication and consolidation, it still requires transferring data from the original sources to the federated search engine for processing and result aggregation which can incur costs when data sources are distributed across different networks or cloud providers.

Query Costs

Query costs are another potential expenditure associated with querying data. Integrating federated search with various data sources may require the development or licensing of specialized connectors or adapters. These connectors can have associated costs, either in terms of development effort or licensing fees from third-party providers.


Maintaining Compliance Costs

Ensuring that federated search respects data sovereignty, access controls, and compliance requirements across different data sources can add complexity and potential costs. This may involve implementing additional security measures, auditing mechanisms, or compliance monitoring tools.

Why Universal Federated Search Is Better

Gurucul offers universal federated search that empowers users to run queries from a single console across any data source including data lakes, cloud object storage, databases, identity systems, threat intel sources, and SIEMs. Using a familiar syntax and workflow, this universal search capability makes security analysts more efficient by significantly increasing the data available to them and adding context to security investigations. Since universal federated search keeps data in the same location it resides in, users can maintain compliance and ownership of the data, and reduce data transfer and ingestion costs.

Using Gurucul universal federated search solves the challenge of accessing and searching all of the various data storage mechanisms and locations where it might be stored, by allowing you to access and search any of it, regardless of how or where it is located and stored—it even works with cold storage. It also solves the problem of transferring, duplicating or rehydrating terabytes or even petabytes worth of data, this saves time and money when you don’t have to transfer all of it, rehydrate it, or duplicate it in another location. It also saves the time it takes to find, connect, login, and search data in all of these disparate locations, as well as having to find where the data might reside. Universal federated search solves these challenges by providing a single interface and familiar query language to search all data, everywhere from a unified console, with a single login.

Gurucul universal federated search solves these challenges and provides cost-effective, time-saving benefits including: 

  • Faster investigations: Accelerate investigations without the need for upfront data transformation and ingestion.
  • Speed Time to Value: Add new federated data sources in minutes for powerful data insights and fast response times.
  • Ownership and compliance: Make data available for decentralized threat hunting while letting users keep ownership of that data and store it to meet compliance standards and budget needs.
  • Reporting: Build high-powered custom reports on any decentralized data for actionable insights, and leverage extensive reporting capabilities such as scheduling, email, download, and export.
  • Cost Savings: Get more out of your data, with universal federated search you can query all data regardless of form or location without having to pay extra data transfer costs, hydration fees, ingestion fees and duplication fees.
  • Familiar Syntax: Gurucul Federated Search lets you query using familiar syntax of the target system
  • Achieve Compliance: By storing non-critical data in low cost long term storage and querying it using Gurucul federated search
  • Search Across Multiple Data Sources: Including identity sources (Azure AD, Okta), threat intel sources (VirusTotal, ThreatFox) and third-party data lakes and SIEMs (Snowflake, Databrick, S3, BigQuery).
  • Connecting Data Sources: Is as easy as setting up authentication credentials.
  • Large Library: Access the library of supported data sources and new ones are constantly added.

Gurucul Cost Savings

Reduce data transfer costs by avoiding ingestion of large data sets into the Gurucul platform. Data residing in the federated data source is queried on demand directly from the source. Most cloud data lakes charge for outbound data transfers, just query the required data and reduce outbound data transfer cost. Gurucul SIEM stores only a limited set of data and the rest is queried on demand from federated sources, thus reducing costs.

Overall, having the ability to use federated search is important to extracting necessary data and pinpointing potential security issues. Having universal federated search removes much of the complexity and drives down data costs while saving time and effort and allows queries throughout different vendors and disparate architectures all in a unified data platform.

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