Master the technologies reshaping the data landscape in 2026. This book is your end-to-end reference for building, operating, and innovating on the modern data lakehouse, from foundational concepts to production-ready implementations.
What you'll learn:
Data Lakehouse Architecture — Understand why organizations are converging data warehouses and data lakes into a single, open platform built on object storage, open table formats, and high-performance query engines.
Apache Iceberg Deep Dive — Go beyond the basics. Learn snapshot isolation, partition evolution, schema evolution, time travel, and the full spec progression from V1 through V4, including the new Variant, Geometry, and Geography types.
Agentic AI for Data Teams — Discover how autonomous AI agents are transforming analytics. Learn about foundation models, tool use, function calling, the Model Context Protocol (MCP), agent-to-agent protocols (A2A), and how to build agents that query governed lakehouse data in real time.
Hands-On with the Leading Engines and Tools — Detailed chapters with working code examples for Apache Spark, Apache Flink, Dremio, Trino, and the Python ecosystem (Polars, DuckDB, PyIceberg, LangChain, DataFusion, and more).
Streaming and Ingestion at Scale — Practical guidance on Confluent/Kafka, Redpanda, RisingWave, Apache Pulsar, Fivetran, Airbyte, and other platforms for real-time and batch data loading into Iceberg tables.
Agentic Analytics End-to-End — Build AI-powered analytics pipelines that combine semantic layers, governed SQL access, and sub-second query performance to let agents reason over your data autonomously.
Whether you are a data engineer designing pipelines, an architect evaluating lakehouse platforms, or an AI engineer connecting agents to structured data, this book gives you the depth and practical examples to move from concept to production.
