DevOps (Vijayakumaran, Sujeevan) (Z-Library)

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Sujeevan Vijayakumaran DevOps Frameworks, Techniques, and Tools

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Table of Contents Notes on Usage Table of Contents 1   Introduction 1.1   Culture 1.2   Technology 1.3   My Path to DevOps and to This Book 1.4   Target Audience 1.5   The Structure of the Book 1.6   Feedback 1.7   Thank You! 2   What Is DevOps? 2.1   DevOps: The Big Picture 2.1.1   CALMS 2.1.2   The Three Ways 2.1.3   Conclusion on the Three Ways and the

CALMS Model 2.2   Misunderstandings about DevOps 2.2.1   Too Strong a Focus on Automation 2.2.2   With DevOps, but without Tests! 2.2.3   Incorrect Understanding of Team Structuring 2.2.4   Not Tearing Down All the Walls 2.2.5   Tools over Processes over People 2.2.6   One to One Copies of Working Methods from Other Companies 2.3   The DevOps Software Development Lifecycle 2.4   Summary 3   The Example Company 3.1   nicely-dressed.com 3.2   The Development Model 3.3   The Business Team: Requirements Analysis 3.4   The Architecture Team: Design of the Application 3.5   The Development Teams 3.5.1   The Development Process 3.5.2   Integrations with Obstacles 3.6   The Quality Assurance Team

3.7   The Operations Team: The Ops in DevOps 3.7.1   Manual Build of the Project 3.7.2   Deployment with Obstacles 3.7.3   Alarm from the Monitoring System 3.8   The Infrastructure Team 3.9   The Security Team 3.10   Summary 4   Project Management and Planning 4.1   The First Step: The Agile Mindset 4.2   Project Management for Everyone? 4.2.1   Jira Can Do (Almost) Everything 4.2.2   People over Processes 4.2.3   Good Project Management outside of Jira 4.2.4   More than Just a Project Management Tool 4.2.5   Project Management at nicely-dressed.com 4.3   Summary 5   Collaboration when Coding

5.1   Typical Problems with Managing the Source Code 5.1.1   Organization of the Code 5.1.2   Isolation for Supposed Security Reasons 5.1.3   Long Development Times Hindering Quick Security Fixes 5.1.4   Development Workflow without a Proper Structure 5.1.5   Big Bang Integrations 5.1.6   Code Reviews Could Help 5.1.7   Technical Debt 5.1.8   High Learning Curve due to Lack of Documentation 5.2   Improve the Organization of the Code 5.3   There Is No Way around Git 5.3.1   Git Solutions at a Glance 5.3.2   Development Workflows with Git 5.3.3   Source Code Management at nicely- dressed.com 5.4   Code Reviews and Pair Programming 5.4.1   Code Reviews 5.4.2   Simplify Code Reviews 5.4.3   Pair Programming 5.5   Inner Sourcing: Sharing Code within the Company 5.5.1   Open Source 5.5.2   The Path to Inner Sourcing

5.5.3   Advantages of Inner Sourcing 5.5.4   Monorepositories 5.6   Summary 6   Continuous Integration and the Build Process 6.1   Typical Problems in the Build Process 6.1.1   Onboarding with Stumbling Blocks 6.1.2   Build Difficulties Due to Infrequent Integrations 6.1.3   Only a Few Tests 6.1.4   A Build Server behind Closed Doors 6.2   Modern Build Management 6.3   Continuous Integration 6.4   The Continuous Integration Server and the Pipelines 6.4.1   The Basic Structure of a Pipeline 6.4.2   Scaling and Reproducibility 6.4.3   Declarative Pipelines versus Scripted Pipelines 6.5   Efficient Pipeline Authoring 6.5.1   Avoid Central Pipelines 6.5.2   Provide Pipeline Building Blocks 6.5.3   Create Visibility

6.6   Overview of Continuous Integration Servers 6.6.1   Jenkins 6.6.2   GitLab CI/CD 6.6.3   GitHub Actions 6.6.4   Other Continuous Integration Servers and Tools 6.6.5   Continuous Integration at nicely- dressed.com 6.7   Summary 7   Quality Assurance 7.1   Typical Problems with Testing 7.1.1   Teams in Their Silos 7.1.2   Different Understandings of Requirements 7.1.3   The Number of Bugs as a Metric 7.1.4   Is It Fixed Yet?

7.2   Testing as Part of the DevOps Process 7.2.1   Tests in the Build Pipeline 7.2.2   Different Tests for Different Tasks 7.2.3   Automate Tests 7.2.4   Test-Driven Development 7.3   Summary 8   Continuous Delivery and Deployment 8.1   Typical Release Management Problems 8.1.1   Separate Handling of Changes and Documentation 8.1.2   Lengthy Release Process 8.1.3   Automations That Are Not Worthwhile 8.1.4   Hostilities between the Teams 8.1.5   Deployment on Production Systems with Obstacles 8.1.6   Conclusion 8.2   Implementing Continuous Delivery and Deployment 8.2.1   Bringing Development and Operations Together 8.2.2   QA, Staging, and Production Environments

8.2.3   Deployment on Fridays 8.3   Build Management for Deployments 8.3.1   The Question of Version Numbers 8.3.2   Packaging 8.3.3   Containerization 8.3.4   Container Registry and Package Registry 8.4   Rollbacks, Canaries, and Feature Flags 8.4.1   Rollbacks 8.4.2   Step-by-Step Activation Using Blue-Green and Canary Deployments 8.4.3   Feature Flags 8.5   Deployment Targets 8.5.1   Orchestrating Deployments with Kubernetes 8.5.2   Orchestrating Deployments at nicely- dressed.com 8.6   Summary 9   Operating the Service 9.1   Typical Problems with Operating Services 9.1.1   Lengthy Infrastructure Planning 9.1.2   Hardware Exchange with Obstacles 9.1.3   Unfavorable Server Utilization 9.1.4   Common Outages during the Night

9.2   Breaking Up the Highly Coupled Infrastructure Architecture 9.2.1   Cattle, not Pets 9.2.2   Abstracting the Infrastructure 9.2.3   Containers for Faster Deployments 9.3   Cloud Computing 9.3.1   What Is the Cloud? 9.3.2   Cloud Models 9.3.3   Service Models 9.3.4   Cloud Native 9.3.5   The Cloud at nicely-dressed.com 9.4   Stronger Collaboration between Development and Operations 9.4.1   Everyone Should Be Ready 9.4.2   Blameless Post-Mortems 9.4.3   Communication Solutions and ChatOps 9.5   Configuration Management: Everything as Code 9.5.1   Infrastructure as Code with Terraform 9.5.2   Ansible versus Puppet 9.6   Chaos Engineering 9.6.1   Making Systems Fail 9.6.2   Chaos Engineering without Chaos, but with a Plan 9.7   Reliability Engineering 9.7.1   Site Reliability Engineering 9.7.2   Database Reliability Engineering

9.8   Summary 10   From Monitoring to Observability 10.1   No Visibility at nicely-dressed.com 10.1.1   Service Outages Happen Every Day 10.1.2   Performance, Performance! 10.1.3   Logs 10.2   With Insight Comes Foresight 10.2.1   Observability Engineering 10.2.2   Insights into Processes with Tracing 10.2.3   A/B Tests 10.2.4   Business Monitoring 10.3   Tools for Monitoring, Observability, and Tracing 10.3.1   Monitor Systems with Icinga and Nagios 10.3.2   Monitoring with Metrics and Time Series Databases 10.3.3   Data Visualization with Grafana 10.3.4   Error Tracking 10.3.5   Distributed Tracing 10.3.6   Logging 10.3.7   Service Meshes 10.3.8   Observability Platforms

10.3.9   Monitoring and Observability at nicely- dressed.com 10.4   Availability 10.4.1   Service Level Agreements 10.4.2   Service Level Objectives 10.4.3   Service Level Indicators 10.4.4   Error Budgets 10.5   Summary 11   Security and Compliance 11.1   Security Disrupts the Agile Waterfall 11.2   DevOps with a Separate Security Team 11.2.1   To Deploy or Not to Deploy? 11.2.2   The Search for Undocumented Dependencies 11.2.3   Frustration and Blocking 11.3   DevSecOps: Building Security into DevOps 11.3.1   The DevSecOps Team Structure 11.3.2   Shift Left: Find Errors Earlier 11.3.3   Inner Sourcing Ensures Formal Security 11.3.4   Security as an Integral Part of the Development Process 11.3.5   Dealing with Mistakes

11.4   Tools for Higher Security 11.4.1   Dashboards and Reporting 11.4.2   Pull and Merge Requests 11.4.3   Security Scanners in Detail 11.5   Supply Chain Security 11.5.1   Attacks on the Supply Chain 11.5.2   Software Bill of Materials 11.5.3   Security of the Build and Deployment Server 11.5.4   Secure User Accounts 11.5.5   No Code Is Good Code 11.5.6   Security at nicely-dressed.com 11.6   Compliance 11.6.1   Define Compliance Guidelines 11.6.2   Manual Compliance 11.6.3   Fully Automated Compliance 11.6.4   Compliance at nicely-dressed.com 11.7   Summary 12   Successfully Implementing the DevOps Transformation 12.1   Introducing a DevOps Culture 12.1.1   Bottom-Up or Top-Down?

12.1.2   First Steps in the DevOps Transformation 12.2   Making DevOps Success Measurable with DORA Metrics 12.2.1   DORA Metric 1: Deployment Frequency 12.2.2   DORA Metric 2: Lead Time 12.2.3   DORA Metric 3: Change Failure Rate 12.2.4   DORA Metric 4: Time to Restore Service 12.2.5   DORA Metric 5: Operational Performance and Reliability 12.2.6   Findings from the State of DevOps Report 12.3   Value Stream Mapping 12.3.1   The Value Stream of a Pizzeria 12.3.2   The Value Stream at nicely-dressed.com 12.3.3   Implementation of Value Stream Mapping 12.4   Summary 13   DevOps Platforms 13.1   Toolchain Complexity 13.1.1   Step 0: Toolchains Grow Historically 13.1.2   Step 1: Multiple Toolchains to Increase Maintainability 13.1.3   Step 2: Standardized Tools, but Still with a Lot of Duct Tape 13.1.4   Step 3: DevOps Platforms 13.2   DevOps Platforms at a Glance

13.2.1   GitLab 13.2.2   GitHub 13.2.3   Azure DevOps 13.2.4   Atlassian 13.2.5   Other Platforms 13.3   Summary 14   Beyond Culture and Tools 14.1   The Role of AI in DevOps 14.1.1   Making Work Easier with AI-Supported Code Generation? 14.1.2   More Code Leads to Higher Review Requirements! 14.1.3   AI-Supporting Features 14.1.4   Data Protection and Privacy 14.1.5   It’s the Overall Concept That Counts! 14.2   DataOps, MLOps, and AIOps 14.2.1   DataOps 14.2.2   MLOps 14.2.3   AIOps 14.3   DevOps as a Job 14.3.1   The Question of DevOps Engineers 14.3.2   Soft Skills 14.3.3   The Technical DevOps Learning Path 14.4   Summary

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