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Craig Johnston - Advanced Platform Development with Kubernetes [ ENG]

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Native language | Родной язык
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Год издания: 2020
Автор: Craig Johnston
Издательство: Apress
ISBN: 978-1-4842-5611-4
Язык: Английский
Формат: PDF/ePub
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 519
Описание:
Leverage Kubernetes for the rapid adoption of emerging technologies. Kubernetes is the future of enterprise platform development and has become the most popular, and often considered the most robust, container orchestration system available today. This book focuses on platforming technologies that power the Internet of Things, Blockchain, Machine Learning, and the many layers of data and application management supporting them.
Advanced Platform Development with Kubernetes takes you through the process of building platforms with these in-demand capabilities. You'll progress through the development of Serverless, CICD integration, data processing pipelines, event queues, distributed query engines, modern data warehouses, data lakes, distributed object storage, indexing and analytics, data routing and transformation, query engines, and data science/machine learning environments. You’ll also see how to implement and tie together numerous essential and trending technologies including: Kafka, NiFi, Airflow, Hive, Keycloak, Cassandra, MySQL, Zookeeper, Mosquitto, Elasticsearch, Logstash, Kibana, Presto, Mino, OpenFaaS, and Ethereum.
The book uses Golang and Python to demonstrate the development integration of custom container and Serverless functions, including interaction with the Kubernetes API. The exercises throughout teach Kubernetes through the lens of platform development, expressing the power and flexibility of Kubernetes with clear and pragmatic examples. Discover why Kubernetes is an excellent choice for any individual or organization looking to embark on developing a successful data and application platform.
What You'll Learn
- Configure and install Kubernetes and k3s on vendor-neutral platforms, including generic virtual machines and bare metal
- Implement an integrated development toolchain for continuous integration and deployment
- Use data pipelines with MQTT, NiFi, Logstash, Kafka and Elasticsearch
- Install a serverless platform with OpenFaaS
- Explore blockchain network capabilities with Ethereum
- Support a multi-tenant data science platform and web IDE with JupyterHub, MLflow and Seldon Core
- Build a hybrid cluster, securely bridging on-premise and cloud-based Kubernetes nodes
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