Building Real Time Analytics Systems 5th Edition by Mark Needham, Dunith Dhanushka ISBN 9781098138783 1098138783

Building Real Time Analytics Systems 5th Edition by Mark Needham, Dunith Dhanushka – Ebook PDF Instant Download/Delivery: 9781098138783 ,1098138783
Full download Building Real Time Analytics Systems 5th Edition after payment

Product details:

ISBN 10: 1098138783
ISBN 13: 9781098138783
Author: Mark Needham, Dunith Dhanushka

Building Real Time Analytics Systems 5th Edition Table of contents:

1. Introduction to Real-Time Analytics

What Is an Event Stream?

Making Sense of Streaming Data

What Is Real-Time Analytics?

Benefits of Real-Time Analytics

New Revenue Streams

Timely Access to Insights

Reduced Infrastructure Cost

Improved Overall Customer Experience

Real-Time Analytics Use Cases

User-Facing Analytics

Personalization

Metrics

Anomaly Detection and Root Cause Analysis

Visualization

Ad Hoc Analytics

Log Analytics/Text Search

Classifying Real-Time Analytics Applications

Internal Versus External Facing

Machine Versus Human Facing

Summary

2. The Real-Time Analytics Ecosystem

Defining the Real-Time Analytics Ecosystem

The Classic Streaming Stack

Complex Event Processing

The Big Data Era

The Modern Streaming Stack

Event Producers

Streaming Data Platform

Stream Processing Layer

Serving Layer

Frontend

Summary

3. Introducing All About That Dough: Real-Time Analytics on Pizza

Existing Architecture

Setup

MySQL

Apache Kafka

ZooKeeper

Orders Service

Spinning Up the Components

Inspecting the Data

Applications of Real-Time Analytics

Summary

4. Querying Kafka with Kafka Streams

What Is Kafka Streams?

What Is Quarkus?

Quarkus Application

Installing the Quarkus CLI

Creating a Quarkus Application

Creating a Topology

Querying the Key-Value Store

Creating an HTTP Endpoint

Running the Application

Querying the HTTP Endpoint

Limitations of Kafka Streams

Summary

5. The Serving Layer: Apache Pinot

Why Can’t We Use Another Stream Processor?

Why Can’t We Use a Data Warehouse?

What Is Apache Pinot?

How Does Pinot Model and Store Data?

Schema

Table

Setup

Data Ingestion

Pinot Data Explorer

Indexes

Updating the Web App

Summary

6. Building a Real-Time Analytics Dashboard

Dashboard Architecture

What Is Streamlit?

Setup

Building the Dashboard

Summary

7. Product Changes Captured with Change Data Capture

Capturing Changes from Operational Databases

Change Data Capture

Why Do We Need CDC?

What Is CDC?

What Are the Strategies for Implementing CDC?

Log-Based Data Capture

Requirements for a CDC System

Debezium

Applying CDC to AATD

Setup

Connecting Debezium to MySQL

Querying the Products Stream

Updating Products

Summary

8. Joining Streams with Kafka Streams

Enriching Orders with Kafka Streams

Adding Order Items to Pinot

Updating the Orders Service

Refreshing the Streamlit Dashboard

Summary

9. Upserts in the Serving Layer

Order Statuses

Enriched Orders Stream

Upserts in Apache Pinot

Updating the Orders Service

Creating UsersResource

Adding an allUsers Endpoint

Adding an Orders for User Endpoint

Adding an Individual Order Endpoint

Configuring Cross-Origin Resource Sharing

Frontend App

Order Statuses on the Dashboard

Time Spent in Each Order Status

Orders That Might Be Stuck

Summary

10. Geospatial Querying

Delivery Statuses

Updating Apache Pinot

Orders

Delivery Statuses

Updating the Orders Service

Individual Orders

Delayed Orders by Area

Consuming the New API Endpoints

Summary

11. Production Considerations

Preproduction

Capacity Planning

Data Partitioning

Throughput

Data Retention

Data Granularity

Total Data Size

Replication Factor

Deployment Platform

In-House Skills

Data Privacy and Security

Cost

Control

Postproduction

Monitoring and Alerting

Data Governance

Summary

12. Real-Time Analytics in the Real World

Content Recommendation (Professional Social Network)

The Problem

The Solution

Benefits

Operational Analytics (Streaming Service)

The Problem

The Solution

Benefits

Real-Time Ad Analytics (Online Marketplace)

The Problem

The Solution

Benefits

User-Facing Analytics (Collaboration Platform)

The Problem

The Solution

Benefits

Summary

13. The Future of Real-Time Analytics

Edge Analytics

Compute-Storage Separation

Data Lakehouses

Real-Time Data Visualization

Streaming Databases

Streaming Data Platform as a Service

Reverse ETL

Summary

Index

People also search for Building Real Time Analytics Systems 5th Edition:

building real time analytics systems github
    
what is real time analytics
    
real time analytics examples
    
building real-time analytics applications
    
real-time analytics architecture

Tags: Mark Needham, Dunith Dhanushka, Real Time, Analytics Systems, Building