Most AI initiatives stall because the underlying data architecture cannot support distributed execution, governance, and locality requirements. Data sits in operational systems, warehouses, streams, object stores, and regulated domains that cannot be centralized without cost, delay, or compliance risk. The result is slow deployment cycles, fragmented pipelines, and limited visibility into how models behave in production.
A modern AI stack addresses this by aligning compute with data. Instead of forcing data movement into a single platform, the stack provides unified access, distributed processing, and consistent execution across heterogeneous environments. This is not a collection of tools. It is an architectural approach rooted in data locality, interoperability, and cross-platform processing.
Core Principles of the Modern Federated Data Stack
1. Execution moves to the data
Federated execution is now a foundational requirement. Workloads must run in situ across databases, warehouses, streaming systems, and edge environments. This reduces data movement, improves compliance, and enables predictable performance.
2. Unified processing across heterogeneous systems
Modern pipelines span multiple engines. Spark handles heavy batch operations. Flink handles streaming. Databases provide efficient pushdown. Vector stores support retrieval. A modern stack must coordinate these systems through a cross-platform layer rather than forcing workloads into a single engine.
3. Privacy and governance by design
Data locality enforces regulatory boundaries naturally. When pipelines run inside the systems that own the data, auditability and permission models remain intact without central replication.
4. Cloud-native elasticity without lock-in
Cloud provides scale, but the execution model must be portable across cloud, on-prem systems, and regulated environments. Open abstractions avoid being tied to any single ecosystem.
5. Interoperability through open standards
APIs, data formats, and execution semantics must remain stable across platforms. This ensures portability and reduces integration cost.
Where Scalytics Federated Fits
Scalytics Federated implements these principles by adding a federated data processing execution layer built on Apache Wayang, created by the Scalytics team before becoming an Apache project. It unifies execution across Spark, Flink, SQL engines, and local runtimes through a cost-based optimizer that selects the best backend per operator.
This enables:
- Data processing without centralization
- AI workloads that execute in situ across distributed sources
- Reduced operational cost by minimizing data movement
- Consistent governance for regulated data
- A single logical pipeline that spans multiple physical systems
No other modern AI stack provider has this cross-platform execution foundation.
Why Current AI Initiatives Struggle
Many enterprises still rely on data architectures that require centralizing inputs before analysis. This creates several issues:
- Long delays between data arrival and model readiness
- High cloud storage and egress costs
- Lack of observability due to fragmented pipelines
- Compliance constraints that block movement of sensitive data
- Difficulty deploying AI to operational systems or edge environments
These challenges are execution-centric, not model-centric. Foundation models do not fix them. A federated architecture does.
Real-World Impact of a Modern Federated Data Stack
Operational AI on distributed data
A retailer processing demand signals across stores cannot rely on a cloud-only architecture. Federated execution allows local inference and in situ analytics for real-time shelf management, while global training runs across aggregated signals without exposing raw data.
Regulated financial environments
Banks operating under strict locality rules can execute risk scoring or fraud detection models where the data resides. Data does not leave the environment, but the model logic is executed consistently across all systems.
Healthcare AI without centralization
Patient data remains in hospital systems. Models are trained or fine-tuned through federated orchestration. Governance is preserved by design, and throughput is not limited by data movement.
These examples illustrate that the modern AI stack is defined by execution locality, governance integrity, and cross-platform processing, not the accumulation of yet another pipeline tool.
Why This Architecture Matters Now
AI workloads amplify existing data problems. If a pipeline cannot operate across fragmented systems today, it will not support advanced AI tomorrow. Unifying execution across distributed systems raises data maturity, improves observability, and reduces engineering load by eliminating duplicate pipelines across Spark, Flink, and internal systems.
This is what Scalytics Federated delivers: an execution layer capable of running AI and analytics where the data resides, with a unified optimizer for distributed systems.
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.
