Building Real-Time Analytics Systems: From Events to Insights with Apache Kafka and Apache Pinot by Mark Needham

Building Real-Time Analytics Systems: From Events to Insights with Apache Kafka and Apache Pinot by Mark Needham

The core philosophy of Building Real-Time Analytics Systems is that modern business value is highly time-sensitive—the faster you derive insights from event data, the quicker you can respond to critical market mutations. Classic data lakes and cloud warehouses (like Snowflake or BigQuery) excel at running deep, historical analysis over massive, static datasets, but they collapse or become prohibitively expensive when forced to handle hundreds of thousands of low-latency analytical queries per second from end-users. Needham maps out the exact infrastructure required to bridge this structural gap.

The text is divided into two distinct components. The first half establishes the theoretical and structural foundation of real-time analytics, explaining the core differences between simple event processing and deep real-time OLAP analysis. The second half drops the reader into a comprehensive, end-to-end practical project: building a real-time tracking, dashboarding, and operations application for a simulated pizza delivery service. Through this scenario, readers learn how to orchestrate multiple distributed systems to process moving orders, compute delivery latencies, and serve instant metrics.

As data organizations face mounting pressure to deliver instant, live analytics inside customer applications, many engineers run into a major roadblock: they try to force legacy batch warehouses or standard relational databases to act like real-time engines, resulting in skyrocketing cloud costs and sluggish query times.

Building Real-Time Analytics Systems provides the clear, hands-on, and architectural guide that data engineering teams need. Mark Needham strips away the complexity of modern stream processing by providing real-world code configurations and clear systems logic. By taking readers through the process of building a fully functional streaming application from the ground up, this O'Reilly volume ensures that your team can deploy scalable pipelines that stay fast under massive user traffic. It is an essential asset for any data platform workstation.

Language: English.

Genre: Computer Science

Binding: সেলাই করা বাইন্ডিং

Quality: Premium Quality Books.

Printing: High Quality Printing.

Paper: Eye Friendly paper (Cream White)

Cover: Matt cover (Paperback).