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Connecting the Data: Data Integration Techniques for Building an Operational Data Store (ODS)

Connecting the Data: Data Integration Techniques for Building an Operational Data Store (ODS)

Angelo Bobak

Kup pozycję na ebookpoint.pl

When organizations change or enhance their internal structures, business data integration is a complex problem that they must resolve. This book describes the common hurdles you might face while working with data integration and shows you various ways to overcome these challenges.

The book begins by explaining the foundational concepts of ODS. Once familiar with schema integration, you'll learn how to reverse engineer each data source for creating a set of data dictionary reports. These reports will provide you with the metadata necessary to apply the schema integration process. As you progress through the chapters, you will learn how to write scripts for populating the source databases and spreadsheets, as well as how to use reports to create Extract, Transform, and Load (ETL) specifications.

By the end of the book, you will have the knowledge necessary to design and build a small ODS.

  • Google Cloud for DevOps Engineers
  • Contributors
  • About the author
  • About the reviewers
  • Preface
    • Who this book is for
    • What this book covers
    • To get the most out of this book
    • Download the example code files
    • Download the color images
    • Conventions used
    • Get in touch
    • Reviews
  • Section 1: Site Reliability Engineering A Prescriptive Way to Implement DevOps
  • Chapter 1: DevOps, SRE, and Google Cloud Services for CI/CD
    • Understanding DevOps, its evolution, and life cycle
      • Revisiting DevOps evolution
      • DevOps life cycle
      • Key pillars of DevOps
    • SREs evolution; technical and cultural practices
      • The evolution of SRE
      • Understanding SRE
      • SRE's approach toward DevOps' key pillars
      • Introducing SRE's key concepts
      • SRE's technical practices
      • SRE's cultural practices
    • Cloud-native approach to implementing DevOps using Google Cloud
      • Focus on microservices
      • Cloud-native development
      • Continuous integration in GCP
      • Continuous delivery/deployment in GCP
      • Continuous monitoring/operations on GCP
      • Bringing it all together building blocks for a CI/CD pipeline in GCP
    • Summary
    • Points to remember
    • Further reading
    • Practice test
    • Answers
  • Chapter 2: SRE Technical Practices Deep Dive
    • Defining SLAs
      • Key jargon
      • Blueprint for a well-defined SLA
      • SLIs drive SLOs, which inform SLAs
    • Defining reliability expectations via SLOs
      • SLOs drive business decisions
      • Setting SLOs the guidelines
    • Exploring SLIs
      • Categorizing user journeys
      • SLI equation
      • Sources to measure SLIs
      • SLI best practices (Google-recommended)
    • Understanding error budgets
      • Error budget policy and the need for executive buy-in
      • Making a service reliable
      • Summarizing error budgets
    • Eliminating toil through automation
    • Illustrating the impact of SLAs, SLOs, and error budgets relative to SLI
      • Scenario 1 New service features introduced; features are reliable; SLO is met
      • Scenario 2 New features introduced; features are not reliable; SLO is not met
    • Summary
    • Points to remember
    • Further reading
    • Practice test
    • Answers
  • Chapter 3: Understanding Monitoring and Alerting to Target Reliability
    • Understanding monitoring
      • Monitoring as a feedback loop
      • Monitoring misconceptions to avoid
      • Monitoring sources
      • Monitoring strategies
      • Monitoring types
      • The golden signals
    • Alerting
      • Alerting strategy key attributes
      • Alerting strategy potential approaches
      • Handling service with low traffic
      • Steps to establish an SLO alerting policy
      • Alerting system desirable characteristics
    • Time series
      • Time series structure
      • Time series cardinality
      • Time series data metric types
    • Summary
    • Points to remember
    • Further reading
    • Practice test
    • Answers
  • Chapter 4: Building SRE Teams and Applying Cultural Practices
    • Building SRE teams
      • Staffing SRE engineers (SREs)
      • SRE team implementations procedure and strategy
      • SRE engagement model
    • Incident management
      • Incident life cycle
      • Elements of effective incident management
    • Being on call
      • Paging versus non-paging events
      • Single-site versus multi-site production teams
      • Recommended practices while being on call
    • Psychological safety
      • Factors to overcome in order to foster psychological safety
    • Sharing vision and knowledge and fostering collaboration
      • Unified vision
      • Communication and collaboration
    • Summary
    • Points to remember
    • Further reading
    • Practice test
    • Answers
  • Section 2: Google Cloud Services to Implement DevOps via CI/CD
  • Chapter 5: Managing Source Code Using Cloud Source Repositories
    • Technical requirements
    • Introducing the key features
      • Creating a repository via Google Cloud Console
      • Creating a repository via the CLI
      • Adding files to a repository in CSR
    • One-way sync from GitHub/Bitbucket to CSR
    • Common operations in CSR
      • Browsing repositories
      • Performing a universal code search
      • Detecting security keys
      • Assigning access controls
    • Hands-on lab integrating with Cloud Functions
      • Adding code to an existing repository through the Cloud Shell Editor
      • Pushing code from the Cloud Shell Editor (local repository) into CSR
      • Creating a cloud function and deploying code from the repository in CSR
    • Summary
    • Further reading
    • Practice test
    • Answers
  • Chapter 6: Building Code Using Cloud Build, and Pushing to Container Registry
    • Technical requirements
    • Key terminology (prerequisites)
    • Understanding the need for automation
    • Building and creating container images Cloud Build
      • Cloud Build essentials
      • Building code using Cloud Build
      • Storing and viewing build logs
      • Managing access controls
      • Cloud Build best practices optimizing builds
    • Managing build artifacts Container Registry
      • Container Registry key concepts
    • Hands-on lab building, creating, pushing, and deploying a container to Cloud Run using Cloud Build triggers
      • Creating an empty repository in Source Repositories
      • Creating a Cloud Build trigger
      • Adding code and pushing it to the master branch
      • Code walk-through
      • Viewing the results
    • Summary
    • Points to remember
    • Further reading
    • Practice test
    • Answers
  • Chapter 7: Understanding Kubernetes Essentials to Deploy Containerized Applications
    • Technical requirements
    • Kubernetes a quick introduction
      • Container orchestration
      • Kubernetes features
    • Kubernetes cluster anatomy
      • Master components Kubernetes control plane
      • Node components
      • Using kubectl
    • Kubernetes objects
      • Pod
      • Deployment
      • StatefulSets
      • DaemonSets
      • Service
    • Scheduling and interacting with Pods
      • Summarizing master plane interactions on Pod creation
      • Critical factors to consider while scheduling Pods
    • Kubernetes deployment strategies
      • Recreate strategy
      • Rolling update strategy
      • Blue/Green strategy
      • Canary deployment
    • Summary
    • Points to remember
    • Further reading
    • Practice test
    • Answers
  • Chapter 8: Understanding GKE Essentials to Deploy Containerized Applications
    • Technical requirements
    • Google Kubernetes Engine (GKE) introduction
      • Creating a GKE cluster
      • GKE cluster deploying and exposing an application
      • GKE Console
    • GKE core features
      • GKE node pools
      • GKE cluster configuration
      • AutoScaling in GKE
      • Networking in GKE
      • Storage options for GKE
      • Cloud Operations for GKE
    • GKE Autopilot hands-on lab
    • Summary
    • Points to remember
    • Further reading
    • Practice test
    • Answers
  • Chapter 9: Securing the Cluster Using GKE Security Constructs
    • Technical requirements
    • Essential security patterns in Kubernetes
      • Authentication
      • Authorization
      • Control plane security
      • Pod security
    • Hardening cluster security in GKE
      • GKE private clusters
      • Container-optimized OS
      • Shielded GKE nodes
      • Network Policies restricting traffic among pods
      • Workload Identity
    • Points to remember
    • Further reading
    • Practice test
    • Answers
  • Chapter 10: Exploring GCP Cloud Operations
    • Cloud Monitoring
      • Workspaces
      • Dashboards
      • Metrics explorer
      • Uptime checks
      • Alerting
      • Monitoring agent
      • Cloud Monitoring access controls
    • Cloud Logging
      • Audit Logs
      • Logs ingestion, routing, and exporting
      • Summarizing log characteristics across log buckets
      • Logs Explorer UI
      • Logs-based metrics
      • Network-based log types
      • Logging agent
    • Cloud Debugger
      • Setting up Cloud Debugger
      • Using Cloud Debugger
      • Access control for Cloud Debugger
    • Cloud Trace
      • Trace Overview
      • Trace List
      • Analysis Reports
    • Cloud Profiler
      • Access control for Cloud Profiler
    • Binding SRE and Cloud Operations
      • SLO monitoring
      • Hands-on lab tracking service reliability using SLO monitoring
    • Summary
    • Points to remember
    • Further reading
    • Practice test
    • Answers
  • Appendix: Getting Ready for Professional Cloud DevOps Engineer Certification
    • Cloud Deployment Manager
    • Cloud Tasks
    • Spinnaker
  • Mock Exam 1
    • Test Duration: 2 hours
    • Total Number of Questions: 50
    • Answers
  • Mock Exam 2
    • Test Duration: 2 hours
    • Total Number of Questions: 50
    • Answers
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