John Brown
Member
DeltaStream today announced the broad availability of its Fusion platform , a unified streaming and analytics system designed for real-time decision-making and built to power modern data workflows.
This release brings together batch, streaming and real-time analytics into a single, serverless environment enabling organizations to process data from ingestion to insight with fewer tools, lower cost and greater speed.
Key Capabilities & Features
- Single-SQL interface: Teams can write once in SQL and let the platform automatically choose the optimal engine (such as Apache Flink, Spark or ClickHouse) behind the scenes.
- Streaming + Real-time + Batch in one: Fusion unifies previously siloed analytics workloads compared stream processing, real-time dashboards and historical queries all live on the same platform.
- Serverless, infrastructure-free experience: Organizations no longer need to manually manage clusters or tune streaming frameworks. Fusion handles provisioning, scaling and cost-optimization automatically.
- Built-for AI-driven use-cases: The platform includes a Real-Time Context Engine tailored to power GenAI Agents, enabling context-aware applications that react instantly to new data.
- Deployment flexibility: Available now and supported on both AWS Marketplace and Microsoft Azure, giving organizations choice of cloud environment.
Why This Matters
Modern enterprises face mounting pressure to turn data into action in real time whether for personalization, monitoring, anomaly detection or AI-agent orchestration. But legacy approaches often require separate systems for streaming analytics, batch processing and real-time dashboards creating complexity, cost and latency.With Fusion, DeltaStream offers a path to reduce tool-sprawl, accelerate insight, and lower operating costs especially relevant for teams building context-aware AI systems or operational analytics that demand immediacy. Key benefits include:
- Faster time to insight: Real-time pipelines and analytics mean decisions aren't delayed by batch lag.
- Unified workflows: Analysts and engineers operate in one environment rather than switching between tools.
- Cost control: By combining workloads and eliminating redundant platforms, organizations can reduce overhead and cloud spend.
- AI-friendly architecture: With the context engine built in, teams can feed refreshed insights to AI agents and applications easily.
Considerations for Buyers & Implementers
- Data readiness: To get full value, organizations should ensure consistent access to streaming and historical data, and align schema design for unified analytics.
- Skill shift: While SQL interface reduces complexity, teams may still need to adapt to a new paradigm of unified analytics vs. separate siloed tools.
- Use-case definition: Real benefit comes when analyzing feed actions organizations should identify where real-time insight drives value (eg, fraud detection, personalization, AI agents).
- Operational governance: Unified platforms bring new demands on monitoring, cost tracking and query governance organizations should plan for observability and cost-management practices.
- Cloud deployment strategy: With flexibility across clouds, users should evaluate where the platform fits their existing architecture, data locality, compliance and cost conditions.
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