Distributed data creates operational friction. When data lives across multiple databases, cloud platforms, and on-premises systems, running analytics becomes a multi-step engineering project rather than a straightforward query. Federated search engines solve this by allowing SQL queries that span heterogeneous data sources without requiring data movement.
The data silo problem
Data silos emerge when different business units operate independent technology stacks with separate budgets. A finance team might use a data warehouse while an operations team relies on a data lake. This fragmentation makes cross-functional analysis difficult because the datasets become easily inaccessible to one another.
The problem compounds in organizations with legacy systems from mergers and acquisitions. Each acquired company brings its own data infrastructure. The result is a patchwork of incompatible systems. Regulatory frameworks like GDPR and CPRA can further isolate data by requiring specific handling based on geographic boundaries.
Traditional centralization approaches attempt to solve data silos by copying all data into a single repository. This creates new problems: engineering teams spend months building ETL pipelines, storage costs multiply, and the data becomes stale by the time it reaches the warehouse. Modern data architectures need flexible approaches that balance centralization with distributed access.
Query performance across distributed systems
Running analytics on distributed data has historically been slow. Early federation systems were retrofitted onto engines designed for single-database queries. The poor performance made federation impractical for production workloads.
Modern federated query engines use massively parallel processing to address these limitations. The engine coordinates with multiple data sources simultaneously through connectors, processing queries in parallel and aggregating results. Query engines can push computation down to the underlying systems when beneficial. This approach uses native optimizations in each data source.
Additionally, Starburst’s use of full query passthrough allows queries to take advantage of syntax and performance capabilities specific to each underlying system. Complex queries can execute in the source database rather than pulling all data through the federation layer.
Cost and complexity of copying data
Traditional ETL pipelines consume substantial engineering resources. The reasons for this are due to the data architecture employed by non-federated pipelines. To function, these systems require data to be extracted, staged in temporary storage, transformed, and loaded into the target system.
This process requires several things to go right:
- Additional storage for staging raw data during extraction
- Compute resources for transformation operations
- Network bandwidth to move petabyte-scale datasets
- Off-hours batch windows to avoid impacting production systems
The same data can exist in three places simultaneously:
- The source system
- The staging area
- The destination data warehouse
This duplication increases storage costs and creates governance challenges since multiple copies must be secured and maintained.
Query federation eliminates these costs by leaving data in place. Users query data directly from source systems using SQL, with the federation layer handling the coordination. This reduces infrastructure requirements and removes the need for constant pipeline maintenance.
Compliance and data sovereignty constraints
Regulatory requirements can make data centralization legally problematic. Finance, healthcare, and government organizations face strict rules about where sensitive data can be stored and how it can be accessed. Moving data across geographic boundaries or into centralized repositories may violate these regulations.
Federated queries allow organizations to maintain compliance by keeping data in approved locations while still running cross-system analytics. Access controls can be enforced at the query level, with permissions managed through the federation layer rather than requiring separate security implementations for each copied dataset.
Time-to-insight delays
When every analysis request requires building a new ETL pipeline, data teams face persistent backlogs. New data sources can take weeks or months to onboard because engineers need to design, test, and maintain custom integration code.
This delay affects decision-making speed. Analysts need current data for accurate insights, but batch ETL processes keep warehouse data lagging behind operational systems. By the time data moves through the pipeline, business conditions may have changed.
Federated queries provide immediate access to source data. Connecting a new data source requires configuring a connector rather than building a pipeline. Analysts can write queries against newly added sources within minutes, and results reflect the current state of the operational systems. This approach is particularly valuable for AI and analytics workloads that require access to current data across multiple domains.
Data quality and consistency
Maintaining consistent definitions across multiple independent data sources presents challenges. Different teams often calculate the same metric using different logic or data structures. Two departments analyzing monthly sales might reach different conclusions because they’re working from incompatible versions of the truth.
Federation engines can apply data governance policies at query time through:
- Role-based access control (RBAC) that limits permissions based on user roles
- Attribute-based access control (ABAC) that enforces fine-grained rules
- Column and row-level security applied during query execution
- Single sign-on (SSO) for centralized identity management across sources
These controls ensure that all users see data through the same governance lens, even though the underlying systems remain distributed. Data producers can package governed datasets as data products with consistent quality standards.
Multi-cloud and hybrid architectures
Organizations increasingly operate across multiple cloud providers and maintain on-premises infrastructure. Data might live in Amazon S3, Azure Data Lake Storage, Google Cloud Storage, and legacy HDFS systems simultaneously.
Running analytics across these environments traditionally required choosing one platform and copying data into it. This forced architectural decisions based on vendor preferences rather than technical requirements. It also created vendor lock-in since moving data between cloud platforms is expensive.
Federated query engines with broad connector ecosystems can query across cloud boundaries. Starburst provides 50+ connectors supporting both cloud and on-premises data sources. A single SQL query can join tables from AWS with data in Azure and on-premises databases. This allows cross-cloud analytics without data movement.
Technical implementation considerations
Implementing federated queries requires attention to several technical factors:
Connector configuration: Each data source needs a properly configured connector with appropriate authentication credentials and network access. Connection parameters affect performance, so tuning timeout values and pool sizes matter for production deployments.
Query optimization: Complex queries spanning multiple systems need careful orchestration and optimization. Understanding which operations can be pushed down to source systems helps optimize performance. Using fully-qualified object names (catalog.schema.table) is required when querying across sources to avoid ambiguity.
Security architecture: User impersonation in connectors allows the federation layer to pass user credentials to underlying systems. This uses existing access controls. Alternatively, administrators can grant execute privileges and create views that expose governed subsets of data to broader user groups.
Network topology: Federation engines need network access to all source systems. Firewall rules, VPN configurations, and proxy settings must allow connections from the query engine to each data source. Some implementations use secure tunnels or VPN connections for on-premises systems.
Practical limitations
Federated queries work well for many use cases, but have constraints. Extremely large aggregations may perform better with dedicated data warehouses optimized for analytical workloads. Meanwhile, real-time streaming analytics might require purpose-built workflows.
Ultimately, query performance depends on the underlying systems’ capabilities. A slow source database will bottleneck federated queries that depend on it. Monitoring individual source performance and tuning queries to minimize data transfer improves overall federation performance.
Data governance becomes more complex in federated environments. Security protocols and authorization rules across multiple systems require careful maintenance. Organizations need clear policies about which data can be federated and what access controls apply.
Adoption patterns
Teams typically start federation by connecting a few critical data sources that analysts query frequently. This proves the concept without requiring wholesale infrastructure changes. As confidence grows, additional sources are added incrementally.
Common starting points include:
- Joining cloud object storage with relational databases to combine dimensional data with fact tables
- Connecting data warehouses with real-time systems for current state analysis
- Federating across business unit data stores for cross-functional reporting
Organizations using data mesh architectures rely heavily on federation. Each domain maintains its own data infrastructure. Federation provides the connective tissue for cross-domain analytics without centralizing everything under a single team.
Frequently asked questions
How does federated search differ from data virtualization?
Data federation is a component of data virtualization. Federation specifically handles querying and combining data across disparate sources. Data virtualization is broader, encompassing metadata management, data abstraction, security, and the complete layer that isolates applications from data sources.
Can federated queries match the performance of centralized data warehouses?
Modern federation technology using distributed query engines like Trino can rival centralized systems for many workloads. Performance depends on factors like query complexity, network latency, and the capabilities of underlying source systems. Query passthrough features allow federation engines to leverage native optimizations in source databases.
What happens to data governance in a federated architecture?
Federated systems can enforce governance policies at query time through Role-based Access Control (RBAC), Attribute-based Access Control (ABAC), and row/column-level security. Single sign-on enables centralized identity management. The federation layer applies consistent governance rules even though data remains distributed across multiple systems.
How do I get started with federated queries?
Start by connecting a few frequently accessed data sources using configured connectors. Write queries using fully-qualified object names (catalog.schema.table) to join data across sources.
Want to get started with federated queries? Try our data federation course.
Does federation require moving or copying data between systems?
No. Federation queries data where it lives without copying it. The query engine coordinates requests across multiple sources, processes them in parallel, and aggregates results. Data stays in its original location, eliminating the need for ETL pipelines and duplicate storage.
