Pipeline As Code

Start thinking about your development pipeline as a mission-critical application. Discover techniques for implementing code-driven infrastructure and CI/CD workflows using Jenkins, Docker, Terraform, and cloud-native services. In Pipeline as Code, you will master: Building and deploying a Jenkins cluster from scratch Writing pipeline as code for cloud-native applications Automating the deployment of Dockerized and Serverless applications Containerizing applications with Docker and Kubernetes Deploying Jenkins on AWS, GCP and Azure Managing, securing and monitoring a Jenkins cluster in production Key principles for a successful DevOps culture Pipeline as Code is a practical guide to automating your development pipeline in a cloud-native, service-driven world. You’ll use the latest infrastructure-as-code tools like Packer and Terraform to develop reliable CI/CD pipelines for numerous cloud-native applications. Follow this book's insightful best practices, and you’ll soon be delivering software that’s quicker to market, faster to deploy, and with less last-minute production bugs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Treat your CI/CD pipeline like the real application it is. With the Pipeline as Code approach, you create a collection of scripts that replace the tedious web UI wrapped around most CI/CD systems. Code-driven pipelines are easy to use, modify, and maintain, and your entire CI pipeline becomes more efficient because you directly interact with core components like Jenkins, Terraform, and Docker. About the book In Pipeline as Code you’ll learn to build reliable CI/CD pipelines for cloud-native applications. With Jenkins as the backbone, you’ll programmatically control all the pieces of your pipeline via modern APIs. Hands-on examples include building CI/CD workflows for distributed Kubernetes applications, and serverless functions. By the time you’re finished, you’ll be able to swap manual UI-based adjustments with a fully automated approach! What's inside Build and deploy a Jenkins cluster on scale Write pipeline as code for cloud-native applications Automate the deployment of Dockerized and serverless applications Deploy Jenkins on AWS, GCP, and Azure Grasp key principles of a successful DevOps culture About the reader For developers familiar with Jenkins and Docker. Examples in Go. About the author Mohamed Labouardy is the CTO and co-founder of Crew.work, a Jenkins contributor, and a DevSecOps evangelist. Table of Contents PART 1 GETTING STARTED WITH JENKINS 1 What’s CI/CD? 2 Pipeline as code with Jenkins PART 2 OPERATING A SELF-HEALING JENKINS CLUSTER 3 Defining Jenkins architecture 4 Baking machine images with Packer 5 Discovering Jenkins as code with Terraform 6 Deploying HA Jenkins on multiple cloud providers PART 3 HANDS-ON CI/CD PIPELINES 7 Defining a pipeline as code for microservices 8 Running automated tests with Jenkins 9 Building Docker images within a CI pipeline 10 Cloud-native applications on Docker Swarm 11 Dockerized microservices on K8s 12 Lambda-based serverless functions PART 4 MANAGING, SCALING, AND MONITORING JENKINS 13 Collecting continuous delivery metrics 14 Jenkins administration and best practices

A step-by-step guide to implementing Continuous Integration and Continuous Delivery (CICD) for Mobile, Hybrid, and Web applications DESCRIPTION The main objective of the book is to create Declarative Pipeline for programming languages such as Java, Android, iOS, AngularJS, NodeJS, Flutter, Ionic Cordova, and .Net. The book starts by introducing all the areas which encompass the field of DevOps Practices. It covers definition of DevOps, DevOps history, benefits of DevOps culture, DevOps and Value Streams, DevOps practices, different Pipeline types such as Build Pipeline, Scripted Pipeline, Declarative Pipeline, and Blue Ocean. Each chapter focuses on Pipeline that includes Static Code Analysis using SonarQube or Lint tools, Unit tests, calculating code coverage, publishing unit tests and coverage reports, verifying the threshold of code coverage, creating build/package, and distributing package to a specific environment based on the type of programming language. The book will also teach you how to use different deployment distribution environments such as Azure App Services, Docker, Azure Container Services, Azure Kubernetes Service, and App Center. By the end, you will be able to implement DevOps Practices using Jenkins effectively and efficiently. KEY FEATURESÊÊ _ Understand how and when Continuous Integration makes a difference _ Learn how to create Declarative Pipeline for Continuous Integration and Continuous Delivery _ Understand the importance of Continuous Code Inspection and Code Quality _ Learn to publish Unit Test and Code Coverage in Declarative Pipeline _ Understand theÊ importance of Quality Gates and Build Quality WHAT YOU WILL LEARNÊ _ Use Multi-Stage Pipeline (Pipeline as a Code) to implement Continuous Integration and ContinuousÊ Ê Ê Ê Delivery. _ Create and configure Cloud resources using Platform as a Service Model _ Deploy apps to Azure App Services, Azure Kubernetes and containers _ Understand how to distribute Mobile Apps (APK and IPA) to App Center _ Improve Code Quality and Standards using Continuous Code Inspection WHO THIS BOOK IS FORÊÊ This book is for DevOps Consultants, DevOps Evangelists, DevOps Engineers, Technical Specialists, Technical Architects, Cloud Experts, and Beginners. Having a basics knowledge of Application development and deployment, Cloud Computing, and DevOps Practices would be an added advantage. TABLE OF CONTENTS 1. Introducing DevOps 2. Introducing Jenkins 2.0 and Blue Ocean 3. Building CICD Pipeline for Java Web Application 4. Building CICD Pipeline for Android App 5. Building CICD Pipeline for iOS App 6. Building CICD Pipeline for Angular Application 7. Building CICD Pipeline NodeJS Application 8. Building CICD Pipeline for Hybrid Mobile Application 9. Building CICD Pipeline for Python Application 10. Building CICD Pipeline for DotNet Application 11. Best Practices

Design, implement, and execute continuous delivery pipelines with a level of flexibility, control, and ease of maintenance that was not possible with Jenkins before. With this practical book, build administrators, developers, testers, and other professionals will learn how the features in Jenkins 2 let you define pipelines as code, leverage integration with other key technologies, and create automated, reliable pipelines to simplify and accelerate your DevOps environments. Author Brent Laster shows you how Jenkins 2 is significantly different from the more traditional, web-only versions of this popular open source automation platform. If you’re familiar with Jenkins and want to take advantage of the new technologies to transform your legacy pipelines or build new modern, automated continuous delivery environments, this is your book. Create continuous delivery pipelines as code with the Jenkins domain-specific language Get practical guidance on how to migrate existing jobs and pipelines Harness best practices and new methods for controlling access and security Explore the structure, implementation, and use of shared pipeline libraries Learn the differences between declarative syntax and scripted syntax Leverage new and existing project types in Jenkins Understand and use the new Blue Ocean graphical interface Take advantage of the capabilities of the underlying OS in your pipeline Integrate analysis tools, artifact management, and containers

Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques

For many organizations, a big part of DevOps’ appeal is software automation using infrastructure-as-code techniques. This book presents developers, architects, and infra-ops engineers with a more practical option. You’ll learn how a container-centric approach from OpenShift, Red Hat’s cloud-based PaaS, can help your team deliver quality software through a self-service view of IT infrastructure. Three OpenShift experts at Red Hat explain how to configure Docker application containers and the Kubernetes cluster manager with OpenShift’s developer- and operational-centric tools. Discover how this infrastructure-agnostic container management platform can help companies navigate the murky area where infrastructure-as-code ends and application automation begins. Get an application-centric view of automation—and understand why it’s important Learn patterns and practical examples for managing continuous deployments such as rolling, A/B, blue-green, and canary Implement continuous integration pipelines with OpenShift’s Jenkins capability Explore mechanisms for separating and managing configuration from static runtime software Learn how to use and customize OpenShift’s source-to-image capability Delve into management and operational considerations when working with OpenShift-based application workloads Install a self-contained local version of the OpenShift environment on your computer

This book teaches you how to build and maintain effective data pipelines. Youll explore the most common usage patterns, including aggregating multiple data sources, connecting to and from data lakes, and cloud deployment. --

Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects. You'll learn what a pipeline is and how it works so you can build a full application easily and rapidly. Then troubleshoot and overcome basic Tensorflow obstacles to easily create functional apps and deploy well-trained models. Step-by-step and example-oriented instructions help you understand each step of the deep learning pipeline while you apply the most straightforward and effective tools to demonstrative problems and datasets. You'll also develop a deep learning project by preparing data, choosing the model that fits that data, and debugging your model to get the best fit to data all using Tensorflow techniques. Enhance your skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution or entering a Kaggle contest, Deep Learning Pipeline is for you! What You'll LearnDevelop a deep learning project using dataStudy and apply various models to your dataDebug and troubleshoot the proper model suited for your data Who This Book Is For Developers, analysts, and data scientists looking to add to or enhance their existing skills by accessing some of the most powerful recent trends in data science. Prior experience in Python or other TensorFlow related languages and mathematics would be helpful.

Implement, run, operate, and test data processing pipelines using Apache Beam Key FeaturesUnderstand how to improve usability and productivity when implementing Beam pipelinesLearn how to use stateful processing to implement complex use cases using Apache BeamImplement, test, and run Apache Beam pipelines with the help of expert tips and techniquesBook Description Apache Beam is an open source unified programming model for implementing and executing data processing pipelines, including Extract, Transform, and Load (ETL), batch, and stream processing. This book will help you to confidently build data processing pipelines with Apache Beam. You'll start with an overview of Apache Beam and understand how to use it to implement basic pipelines. You'll also learn how to test and run the pipelines efficiently. As you progress, you'll explore how to structure your code for reusability and also use various Domain Specific Languages (DSLs). Later chapters will show you how to use schemas and query your data using (streaming) SQL. Finally, you'll understand advanced Apache Beam concepts, such as implementing your own I/O connectors. By the end of this book, you'll have gained a deep understanding of the Apache Beam model and be able to apply it to solve problems. What you will learnUnderstand the core concepts and architecture of Apache BeamImplement stateless and stateful data processing pipelinesUse state and timers for processing real-time event processingStructure your code for reusabilityUse streaming SQL to process real-time data for increasing productivity and data accessibilityRun a pipeline using a portable runner and implement data processing using the Apache Beam Python SDKImplement Apache Beam I/O connectors using the Splittable DoFn APIWho this book is for This book is for data engineers, data scientists, and data analysts who want to learn how Apache Beam works. Intermediate-level knowledge of the Java programming language is assumed.