فهرست مطالب

• Cover
• Title page
• Copyright and credits
• Dedication
• Contributors
• Table of Contents
• Preface
• Part 1: Docker and Kubernetes
• Chapter 1: Getting Started with Containers
• Technical requirements
• Container architecture
• Installing Docker
• Getting started with Docker images
• Building your own image
• Summary
• Chapter 2: Kubernetes Architecture
• Technical requirements
• Kubernetes architecture
• Pods
• Deployments
• StatefulSets
• Jobs
• Services
• Ingress and Ingress Controller
• Gateway
• Persistent Volumes
• ConfigMaps and Secrets
• Summary
• Chapter 3: Getting Hands-On with Kubernetes
• Technical requirements
• Installing kubectl
• Deploying a local cluster using Kind
• Deploying an AWS EKS cluster
• Deploying a Google Cloud GKE cluster
• Deploying an Azure AKS cluster
• Running your API on Kubernetes
• Running a data processing job in Kubernetes
• Summary
• Part 2: Big Data Stack
• Chapter 4: The Modern Data Stack
• Data architectures
• Data lake design for big data
• Implementing the lakehouse architecture
• Summary
• Chapter 5: Big Data Processing with Apache Spark
• Technical requirements
• Getting started with Spark
• The DataFrame API and the Spark SQL API
• Working with real data
• Summary
• Chapter 6: Building Pipelines with Apache Airflow
• Technical requirements
• Getting started with Airflow
• Building a data pipeline
• Airflow integration with other tools
• Summary
• Chapter 7: Apache Kafka for Real-Time Events and Data Ingestion
• Technical requirements
• Getting started with Kafka
• Exploring the Kafka architecture
• Streaming from a database with Kafka Connect
• Real-time data processing with Kafka and Spark
• Summary
• Part 3: Connecting It All Together
• Chapter 8: Deploying the Big Data Stack on Kubernetes
• Technical requirements
• Deploying Spark on Kubernetes
• Deploying Airflow on Kubernetes
• Deploying Kafka on Kubernetes
• Summary
• Chapter 9: Data Consumption Layer
• Technical requirements
• Getting started with SQL query engines
• Deploying Trino in Kubernetes
• Deploying Elasticsearch in Kubernetes
• Summary
• Chapter 10: Building a Big Data Pipeline on Kubernetes
• Technical requirements
• Checking the deployed tools
• Building a batch pipeline
• Building a real-time pipeline
• Summary
• Chapter 11: Generative AI on Kubernetes
• Technical requirements
• What generative AI is and what it is not
• Using Amazon Bedrock to work with foundational models
• Building a generative AI application on Kubernetes
• Building RAG with Knowledge Bases for Amazon Bedrock
• Building action models with agents
• Summary
• Chapter 12: Where to Go from Here
• Important topics for big data in Kubernetes
• What about team skills?
• Summary
• Index
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