Streaming Intelligence: Real-Time Business Beyond the Data Warehouse

Why Traditional Business Intelligence Falls Short

In many enterprises, business intelligence is still built on top of data warehouses or data lakes, designed primarily for batch-oriented reporting rather than real-time insight.. Sure, for historical data this is very important. Data is collected, transformed, and processed in batches — often hours or even days after it was created - and even if event streaming is used in the ETL jobs. CDC gives us the illusion of doing event driven business.

Can this approach perform real-time risk detection and anomaly detection in daily operations? Can it drive dynamical process optimizations, and is it adaptive? The answer is: No, they can’t–yet.

This is where Streaming Intelligence comes in. It enables you to directly link business-relevant events and operational IT data — in real time, without the detour through a data warehouse.

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What Is Streaming Intelligence? (Real-Time Analytics Explained)

Streaming Intelligence goes beyond the hype — it enables analytics and decisions to happen as events unfold, not hours later.

It is the practice of processing event streams and data streams continuously, as they happen, and deriving insights, actions, and alerts on the fly. Most important is the ability to cover business related events and operational events together in real time.

Instead of storing data first and analyzing it later, stream processors (such as Apache Flink or Apache Spark jobs) run over your data streams and generate actionable results immediately:

• Autoscaling workloads based on real-time demand
  
• Quality control on live data
  
• Risk assessment and anomaly detection as events occur
  
  

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Beyond ERP Data: Enabling streaming metadata for adaptive systems

The real breakthrough comes from making use of dynamic stream metadata: such as throughput, latency, error rates, inter event time distributions, etc.

Traditionally, this information is either ignored or built as a custom feature for some very critical applications.

With Streaming Intelligence, systems can adapt automatically:

• Train machine-learning models directly on the in flight data
  
• Adjust parameters in real time during event processing
  
• Build a live, dynamic view of event streams, not just their schema and lineage
  
  

This creates a feedback loop between business logic and infrastructure. Systems learn and react as conditions change — without manual intervention. Sure, a human in the loop will still have a final word, when it comes to critical decisions, but with a dynamic approach, we don’t have to stop, change, and restart the applications.

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Shift-Left for ERP and Operational Databases with streaming-first intelligence

ERP systems are traditionally static and slow to change.

Streaming Intelligence brings the Shift-Left mindset into the ERP world, an essential and very expensive part of enterprise IT. Instead of expensive and time consuming ERP adoptions, we can use the complementary Streaming Intelligence approach:

• Insights are generated directly in the data stream, visible to business stakeholders
  
• Operators and developers see bottlenecks instantly and the can connect them to the business layer for better impact analysis
  
• Product managers and decision-makers get KPIs, ROI, and risk metrics in real time — no new data warehouse project required - this allows you to set up new metrics in hours, not weeks.

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Databricks + Scalytics: Extending the Lakehouse to Streaming and Federated Data

Our partnership with Databricks expands the Lakehouse paradigm beyond its traditional domain. While Databricks unifies batch analytics, ML, and AI in a single platform, it remains primarily centered on managed data inside the Lakehouse. Scalytics Streaming Intelligence extends this reach to live operational systems and distributed data pools that Databricks cannot directly access — without data movement or duplication.

Through federated data processing, Scalytics connects to heterogeneous sources — ERP systems, message brokers, IoT platforms, or edge event hubs — and executes streaming transformations directly where the data resides. These streams are seamlessly integrated into Databricks via Delta Live Tables, Structured Streaming, and MLflow pipelines, enabling end-to-end real-time analytics and adaptive decision logic.

This architecture allows data engineers to use familiar Spark and Delta semantics while leveraging Scalytics’ dynamic federation layer to process and correlate events across transactional and operational data in flight. The result: continuous intelligence that extends the Lakehouse to every edge of the enterprise — from the factory floor to the financial ledger, with consistent governance, lineage, and security.

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Bridging Business Intelligence and Operational Technology

A mature Streaming Intelligence platform offers standardized components:

• Events, actions, and alerts are the element of application systems, which cary the information.
• Robust and reusable building blocks of our Streaming Intelligence system are:
  
  • Smart-Topics
  • Event-Group and Transaction-Tracking,
  • Connectivity analysis
    
    
• Dashboards and reports generated directly from the stream - no additional BI needed
• Secure gateways (e.g., MCP-compatible) for controlled access and workload isolation
  
  

The result: isolation where necessary — and transparency where it counts.

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What’s next: From Passive BI to Active, Streaming Intelligence

Streaming Intelligence takes business intelligence to the next level. Enterprises that integrate streaming intelligence platforms with private AI gain a competitive edge in real-time decision-making.

It transforms passive reporting into an active, adaptive control layer that links business and technical data in real time.

Organizations that adopt this early gain a decisive edge: faster decision-making, lower risk, less manual intervention, and better resource utilization.

And even if the organization is not jumping on that train, for individual applications, which power a company’s SaaS offering, Streaming Intelligence will become a critical competency. By bringing adaptive but standardized components with direct AI integration will be a game changer.

The future of business intelligence is streaming — and it has been started already. Adding secure private AI to these scenes is our mission.

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