Synopsis
Traditional data pipelines move data. Agentic analytics pipelines think, act, and decide.
The data engineering and analytics architecture that most enterprises rely on today was designed for a world where humans interrogate data and machines move it. That architecture is being made obsolete — not by faster processing or better dashboards, but by a fundamentally different paradigm: systems that reason over data, generate their own hypotheses, take autonomous actions, and continuously improve without waiting for a human to ask a question.
AGENTIC ANALYTICS is the definitive technical guide for data engineers, analytics architects, ML engineers, and data platform leaders who need to design, build, and govern enterprise-grade autonomous intelligence pipelines — systems that do not just process data, but think, act, and decide for themselves.
This is not a business overview. This is the deep practitioner's guide to the architecture, implementation, and governance of the next generation of enterprise data intelligence.
The shift that this book is built around:
By 2028, 33% of enterprise software applications will include agentic AI capabilities — up from less than 1% in 2024. Gartner has named agentic AI the number one strategic technology trend for two consecutive years. The agentic AI market is expanding from $7.06 billion in 2025 to $93.20 billion by 2032 at a 44.6% CAGR. And 95% of enterprise generative AI pilots are currently failing to deliver measurable value — largely because they are being built on data architectures that were not designed for agentic operation. This book is the architecture guide that closes that gap.
What makes agentic analytics fundamentally different from everything that came before:
Traditional BI: collect data → build a report → human reviews → human decides → human acts.
Agentic analytics: agents observe live data streams → reason over patterns → generate hypotheses → test them autonomously → trigger actions → execute workflows → learn from outcomes → repeat.
What you will build and master across seven core modules:
MODULE 1 — FOUNDATIONS OF AGENTIC ANALYTICS
MODULE 2 — AUTONOMOUS DATA PIPELINE ARCHITECTURE
MODULE 3 — MULTI-AGENT ORCHESTRATION FOR DATA SYSTEMS
MODULE 4 — AUTONOMOUS DECISION SYSTEMS
MODULE 5 — AGENTIC RAG AND KNOWLEDGE SYSTEMS
MODULE 6 — GENAIOPS FOR AGENTIC ANALYTICS
MODULE 7 — GOVERNANCE, TRUST, AND ENTERPRISE COMPLIANCE
The production technology stack — covered in full depth:
Apache Kafka, Apache Flink, Databricks, Snowflake, dbt, Apache Airflow, AWS Strands Agents, LangGraph, CrewAI, Qdrant, Pinecone, Weaviate, OpenTelemetry, Prometheus, Great Expectations, Monte Carlo, Atlan, and custom agent orchestration frameworks.
Who this book is written for:
Data engineers and analytics engineers building the next generation of intelligent pipelines. ML engineers deploying agentic models in production data environments. Analytics architects designing autonomous BI and decision intelligence platforms. Data platform leaders and CDOs modernising enterprise data infrastructure for the agentic era. MLOps and DataOps engineers operationalising autonomous data systems at scale.
The gap between traditional data architecture and agentic analytics architecture is the gap between organisations that react to data and organisations that operate from data intelligence — continuously, autonomously, and at scale. This is the complete engineering guide to closing that gap.
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