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Big Data vs Fast Data in Agentic Architectures: Batch, Streaming, and AI Decision Loops
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Publisher: Independently Published
Published: Jan 15 2026
Pages: 174
Weight: 0.69
Height: 0.37 Width: 7.00 Depth: 10.00
Language: English
Big Data vs Fast Data in Agentic Architectures: Batch, Streaming, and AI Decision Loops
Most AI systems fail for a simple reason: they act on the past while the business moves in the present. Models get smarter, but decisions still arrive too late. Dashboards look impressive, yet agents approve the wrong transaction, answer with outdated context, or confidently act on information that expired minutes ago. The real bottleneck is not intelligence-it is time.
This book confronts the central problem of modern AI systems: how to design data architectures that serve autonomous agents operating in real time. It explains why traditional Big Data platforms excel at training and analytics, yet collapse when used for live decision-making, and why Fast Data systems enable reflexes but fall apart without historical grounding. Most importantly, it shows how to combine both into coherent agentic decision loops that remain accurate under pressure.
You will learn how batch pipelines produce frozen intelligence for training and long-term memory, how streaming systems deliver sub-second context for inference, and how modern architectures reconcile the two without duplicating logic or exploding costs. The book moves beyond theory to explain how latency becomes a financial variable, how stale context creates hallucinations, and how agent decisions can be traced, audited, and corrected after the fact.
By the end, you will be able to:
Design architectures that align data freshness with business risk
Decide when batch processing is essential and when streaming is non-negotiable
Build hybrid pipelines that feed agents both historical context and real-time signals
Eliminate logic drift between batch and stream systems
Create auditable AI decision loops suitable for regulated environments
Written for architects, data engineers, and AI builders, this is not a book about prompts or model training tricks. It is about the infrastructure that determines whether an agent tells the truth or invents a reality that no longer exists.
If you are ready to move past nightly reports and build systems that think and act in the moment, this book gives you the architectural foundation to do it right. Start building agents that operate on now, not yesterday.
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