Enterprises are adopting LLMs and AI systems to extract value from distributed datasets stored across clouds, on premises systems, and regulated environments. Centralizing this data increases cost, creates security exposure, and often violates regulatory constraints. In situ federated data processing provides a practical alternative by executing computation directly at the data source. This enables AI workloads to run across heterogeneous systems without moving sensitive data or duplicating infrastructure.
Key takeaways:
- In situ processing allows computation to run where the data resides, reducing movement, improving security, and lowering operational cost.
- Cost based optimization selects the most efficient execution plan across distributed systems based on performance and economic factors.
- Enterprise grade federated learning becomes feasible because data remains in place while models and pipelines operate across existing infrastructure.
What Is In Situ Federated Data Processing?
In situ federated data processing executes analytical and machine learning workloads directly on each data source rather than aggregating raw data into a central system. This model provides:
Data privacy by design
Sensitive information remains in its original environment. Organizations maintain full control over access, storage, and governance.
Reduced security exposure
Centralized repositories attract attacks. In situ processing avoids creating new concentration points for sensitive data.
Stronger governance
Regional, departmental, or platform specific policies remain intact because the data never leaves its source system.
Operational efficiency
Eliminating data transfers reduces network overhead, latency, and duplication. Workloads complete faster and at lower cost.
Why In Situ Processing Matters for LLMs and AI
For LLMs, embeddings, classification, and other AI workloads, in situ execution provides:
Higher data fidelity
Models operate on data in its native environment, preserving richness that is often lost through ETL transformations.
Shorter training and processing time
Training does not depend on central aggregation. Local nodes contribute updates directly, reducing bottlenecks.
Scalability across diverse environments
As data volumes grow, scaling becomes a matter of adding local participants rather than expanding a central cluster.
Lower operational cost
Data movement is one of the most expensive components of enterprise AI pipelines. In situ execution reduces it significantly.
Industry Examples
Healthcare
Hospitals analyze clinical data locally while supporting research models without exposing patient information.
Finance
Banks evaluate transactions at the branch or regional level while contributing risk indicators to central models.
Retail
Retailers process behavioral and sales data within each region to support personalization and inventory forecasting.
Manufacturing
Factories run analytics on machine and sensor data at the edge, enabling predictive maintenance and real time monitoring.
Scalytics: Enabling Enterprise Grade Federated Execution
Scalytics Federated provides a platform for executing distributed pipelines, AI workloads, and federated learning directly at the data source. The system builds on the team’s experience behind Apache Wayang, a widely adopted cross platform data processing engine.
Key differentiator: Cost based query optimization
Traditional federated learning systems rely on fixed or manually tuned execution strategies. Scalytics introduces a machine learning driven optimizer that evaluates runtime cost, data location, platform capabilities, and infrastructure constraints to select the most efficient execution plan dynamically.
This provides measurable improvements in performance, predictability, and scalability for enterprise federated learning and AI pipelines.
Additional enterprise capabilities
Secure data handling
Scalytics ensures that organizations retain full control over data access. Only compliant intermediate results or model updates are exchanged.
Flexible deployment
The platform supports cloud, on premises, and hybrid environments, allowing organizations to leverage their existing infrastructure.
Scalable architecture
Scalytics operates across large numbers of participants, heterogeneous systems, and high volume workloads.
Seamless integration
The system connects to existing data platforms, warehouses, lakes, operational systems, and pipelines without requiring reengineering.
About Scalytics
Scalytics Federated provides federated data processing across Spark, Flink, PostgreSQL, and cloud-native engines through a single abstraction layer. Our cost-based optimizer selects the right engine for each operation, reducing processing time while eliminating vendor lock-in.
Scalytics Copilot extends this foundation with private AI deployment: running LLMs, RAG pipelines, and ML workloads entirely within your security perimeter. Data stays where it lives. Models train where data resides. No extraction, no exposure, no third-party API dependencies.
For organizations in healthcare, finance, and government, this architecture isn't optional, it's how you deploy AI while remaining compliant with HIPAA, GDPR, and DORA.Explore our open-source foundation: Scalytics Community Edition
Questions? Reach us on Slack or schedule a conversation.
