فلش‌کارت‌های آموزشی Google Cloud Platform: Batch Application Deployment and Management with Dask (Advanced Distributed Computing)

🎓 دوره آموزشی جامع

📚 اطلاعات دوره

عنوان دوره: دوره Google Cloud Platform: Batch Application Deployment and Management with Dask (Advanced Distributed Computing)

موضوع کلی: برنامه نویسی

موضوع میانی: Google Cloud Platform (GCP)

📋 سرفصل‌های دوره

• 1. Understanding Batch Processing Workloads and Their Evolution
• 2. Key Characteristics of Distributed Computing Architectures
• 3. Common Challenges in Designing and Managing Distributed Systems
• 4. Introduction to Parallelism, Concurrency, and Asynchronous Processing
• 5. The Role of Distributed Computing in Modern Big Data Ecosystems
• 6. Overview of Advanced Distributed Computing Concepts for GCP
• 7. GCP Project Structure, Organization, and Resource Hierarchy
• 8. Identity and Access Management (IAM) for Secure GCP Deployments
• 9. GCP Networking Essentials: VPCs, Subnets, and Firewall Rules for Distributed Workloads
• 10. Google Cloud Storage (GCS): Architecting Data Lakes for Dask
• 11. Compute Engine: Provisioning and Managing Virtual Machines for Custom Clusters
• 12. Kubernetes Engine (GKE): Foundations for Containerized Distributed Applications
• 13. Dataproc: A Managed Service for Apache Spark, Flink, and Dask
• 14. Introduction to Google Cloud Operations Suite for Distributed Systems
• 15. What is Dask? A Scalable Analytics Library Overview
• 16. Understanding Dask's Lazy Evaluation and Task Graph Construction
• 17. Dask Array: Parallel Computing with NumPy-like Interfaces
• 18. Dask DataFrame: Scaling Pandas Workflows for Large Datasets
• 19. Dask Bag: Processing Unstructured and Semi-structured Data
• 20. Dask Futures: Low-Level Asynchronous Task Scheduling
• 21. Deep Dive into Dask Collections and Their Internal Mechanisms
• 22. Comparing Dask with Apache Spark and Other Distributed Frameworks
• 23. Executing Complex Dask Graphs and Understanding Computations
• 24. The Dask Distributed Scheduler: Architecture and Components
• 25. Dask Workers: Managing Compute Resources and Task Execution
• 26. Dask Nannies: Monitoring and Restarting Worker Processes
• 27. The Dask Client: Programmatic Interaction with a Distributed Cluster
• 28. Utilizing the Dask Dashboard for Real-time Monitoring and Diagnostics
• 29. Advanced Task Graph Optimization Techniques in Dask
• 30. Configuring Dask for Specific Workload Characteristics and Resource Needs
• 31. Memory Management Strategies and Best Practices in Dask Distributed
• 32. Designing Compute Engine Instance Templates for Dask Clusters
• 33. Manual Deployment and Configuration of a Dask Cluster on Compute Engine
• 34. Secure Networking Setup for Dask Schedulers and Workers on GCP
• 35. Automating Dask Cluster Initialization with Compute Engine Startup Scripts
• 36. Selecting Optimal Machine Types and Accelerators (GPUs) for Dask Workers
• 37. Leveraging Persistent Disks for Stateful Dask Worker Storage and Caching
• 38. Implementing Autoscaling for Dask Clusters with Managed Instance Groups
• 39. Securing Dask Communication Channels on Compute Engine with TLS/SSL
• 40. Troubleshooting Common Deployment Issues for Dask on Compute Engine
• 41. Kubernetes Fundamentals for Deploying Dask Applications on GKE
• 42. Provisioning and Configuring a GKE Cluster Tailored for Dask Workloads
• 43. The Dask Kubernetes Operator: Declarative Cluster Management
• 44. Deploying Dask Scheduler and Worker Pods on GKE
• 45. Resource Requests, Limits, and Quality of Service for Dask on Kubernetes
• 46. Customizing Dask Worker Docker Images for GKE Deployments
• 47. Using Helm Charts for Standardized Dask Cluster Deployments on GKE
• 48. Integrating Dask with GKE's Cluster Autoscaler for Elasticity
• 49. Advanced GKE Networking Strategies for Dask (e.g., Service Mesh Integration)
• 50. Debugging and Troubleshooting Dask Deployments on GKE
• 51. Introduction to Dataproc as a Managed Dask Service
• 52. Creating Dataproc Clusters with Dask Initialization Actions
• 53. Submitting Dask Jobs and Scripts to Dataproc Clusters
• 54. Managing the Lifecycle of Dask Clusters on Dataproc
• 55. Dataproc Worker Configuration: Machine Types, Scaling, and Preemptible Instances
• 56. Using Custom Images and Containerization for Dataproc Dask Workers
• 57. Accessing the Dask Dashboard and Logs on Dataproc Clusters
• 58. Cost Optimization Strategies for Dask Workloads on Dataproc
• 59. Diagnosing and Resolving Issues with Dask Jobs on Dataproc
• 60. Efficiently Reading and Writing Data from Google Cloud Storage with Dask
• 61. Batch ETL and Analytics with Dask and BigQuery Integration
• 62. Connecting Dask to Managed Databases: Cloud SQL, Spanner, and Firestore
• 63. Triggering Dask Batch Jobs with Pub/Sub Events and Cloud Functions
• 64. Working with Diverse Data Formats: Parquet, ORC, CSV, JSON, NetCDF
• 65. Optimizing Data Access Patterns for Performance in Dask Applications
• 66. Advanced Data Partitioning and Indexing Strategies for Dask Collections
• 67. Handling Large-Scale Geospatial Data with Dask and GCP Storage
• 68. Principles of Workflow Orchestration for Complex Batch Applications
• 69. Introduction to Cloud Composer (Apache Airflow) for Dask Workflows
• 70. Designing and Implementing Dask DAGs in Apache Airflow
• 71. Event-Driven Triggers for Dask Batch Jobs using Pub/Sub and Cloud Functions
• 72. Building CI/CD Pipelines for Dask Applications on GCP
• 73. Infrastructure-as-Code: Deploying Dask Environments with Terraform
• 74. Advanced Deployment Strategies with Google Cloud Deployment Manager
• 75. Automating Dask Cluster Provisioning and De-provisioning
• 76. Centralized Logging for Dask Applications with Google Cloud Logging
• 77. Building Custom Dashboards with Cloud Monitoring for Dask Clusters
• 78. Setting Up Advanced Alerts and Notifications for Dask Performance Metrics
• 79. Analyzing Dask Scheduler and Worker Logs for Performance Insights
• 80. Diagnosing and Resolving Common Dask Performance Bottlenecks
• 81. Troubleshooting Connectivity, Resource Exhaustion, and Task Failures
• 82. Leveraging Dask's Internal Diagnostics and Profiling Tools
• 83. Advanced Techniques for Dask Task Graph Optimization and Fusion
• 84. Memory Optimization Strategies for Extremely Large Dask Datasets
• 85. Evaluating CPU vs. GPU for Accelerating Dask Workloads
• 86. Dynamic Scaling and Resource Elasticity for Dask Clusters on GCP
• 87. Cost Management and Billing Best Practices for GCP Dask Deployments
• 88. Identifying and Eliminating I/O, Network, and Compute Bottlenecks
• 89. Benchmarking and Performance Tuning Your Dask Batch Applications
• 90. Implementing Principle of Least Privilege with IAM Service Accounts for Dask
• 91. Network Security: VPC Service Controls and Private IP for Dask Clusters
• 92. Data Encryption at Rest (GCS, Disks) and In Transit (Dask Communication)
• 93. Secure Credential Management with Google Secret Manager for Dask Applications
• 94. Role-Based Access Control (RBAC) and Namespace Isolation for Dask on GKE
• 95. Auditing and Compliance for Dask Batch Processing on GCP
• 96. Extending Dask: Custom Schedulers, Plugins, and Distributed Algorithms
• 97. Dask-ML: Scaling Machine Learning Workflows on GCP
• 98. Building Complex Data Pipelines with Dask and Apache Beam on GCP
• 99. Real-World Case Study: Large-Scale Genomic Data Processing with Dask
• 100. Future Trends in Distributed Computing and the Dask Ecosystem