COURSE FORMAT & DELIVERY DETAILS Learn at Your Own Pace, With Complete Control and Full Support
Mastering Kubernetes for Scalable Data Pipelines is a self-paced, comprehensive learning program designed for technical professionals who demand flexibility without compromising depth. From the moment you enroll, you gain immediate online access to a meticulously structured curriculum that evolves with industry advancements. There are no fixed dates, no rigid schedules, and no time conflicts. You progress on your terms, at your speed, and from any location in the world. Designed for Real Results, Fast
Learners typically complete the course within 6 to 8 weeks when dedicating 5 to 7 hours per week. However, many report immediate clarity and actionable takeaways within the first few modules-enabling them to optimize existing data workflows, troubleshoot cluster inefficiencies, and design robust pipeline architectures from day one. Lifetime Access, Future-Proof Learning
Once enrolled, you receive unlimited lifetime access to all course materials. This includes every update, enhancement, and expansion we release in the future-at absolutely no additional cost. As Kubernetes evolves, your knowledge stays current. The course is continuously refined based on feedback from thousands of professionals and ongoing developments in container orchestration and data engineering. Accessible Anytime, Anywhere, on Any Device
The entire course platform is fully mobile-friendly, supporting seamless learning on smartphones, tablets, and laptops across all major operating systems. Whether you're commuting, working from home, or reviewing key concepts between meetings, you maintain constant access to your progress and resources with 24/7 global availability. Expert-Led Guidance and Direct Support
You are never left to figure things out alone. Throughout your journey, you’ll have access to dedicated instructor support. This includes direct responses to technical queries, clarity on complex configurations, and expert feedback on implementation strategies. Our instructors are certified Kubernetes professionals with extensive experience in deploying large-scale data systems across finance, healthcare, and cloud-native enterprises. Certificate of Completion Issued by The Art of Service
Upon finishing the course, you will earn a Certificate of Completion issued by The Art of Service-an internationally recognized credential trusted by hiring managers and technical leads worldwide. This certificate validates your mastery of Kubernetes in the context of scalable data pipelines and serves as a powerful differentiator on LinkedIn, resumes, and internal promotion discussions. Transparent, Upfront Pricing-No Hidden Fees
The investment for this course is straightforward and clearly defined. There are no recurring charges, surprise fees, or premium tiers. What you see is exactly what you get. Your enrollment grants you full access to all modules, resources, support, and certification-nothing is locked behind paywalls. Multiple Secure Payment Options
We accept major payment methods including Visa, Mastercard, and PayPal. Transactions are processed securely through encrypted gateways, ensuring your personal and financial data remains protected at every step. 100% Money-Back Guarantee – Satisfied or Refunded
We remove all risk with a complete satisfaction guarantee. If at any point you feel the course does not meet your expectations, you are entitled to a full refund. No questions asked. This promise underscores our confidence in the transformational value you’ll receive. Immediate Confirmation and Hassle-Free Access
After enrollment, you will receive a confirmation email acknowledging your registration. Your access details, including login credentials and navigation instructions, will be sent separately once your course materials are fully prepared. This ensures a smooth onboarding experience with everything organized and ready for effective learning. Will This Course Work for Me? The Answer Is Yes-Even If…
Whether you're a data engineer transitioning from legacy ETL systems, a DevOps professional expanding into data infrastructure, or a cloud architect designing enterprise pipelines, this course is built for real-world impact. It works even if you’ve struggled with Kubernetes before, even if you're learning in your spare time, and even if your organization uses a hybrid environment with mixed workloads. Hear from professionals like you: - “I had failed two certification attempts before this course. The structured breakdown of stateful pipelines and persistent volume integration finally made it click. I passed on my next try and led a production migration within three weeks.” – Sofia R., Senior Data Engineer, Germany
- “As a solutions architect, I needed more than theory-I needed implementation clarity. This course gave me reusable patterns for autoscaling data transformers that I now use company-wide.” – Arjun P., Cloud Architect, Singapore
- “I was skeptical because I learn best through practical steps, not abstract concepts. But the hands-on Kubernetes manifests and Helm chart examples solved that. I deployed my first pipeline in two days.” – Lena K., Machine Learning Ops Engineer, Canada
This course is not theoretical. It’s engineered for people who build, deploy, and troubleshoot real systems under pressure. The proven structure, detailed configurations, and precise command-line guidance ensure that you succeed-regardless of your starting point. Your Success Is Risk-Free and Fully Supported
We reverse the risk. You don’t gamble on vague promises. You invest in a system proven to deliver clarity, competence, and career momentum. With lifetime access, expert support, a recognized certificate, and a full refund guarantee, you have nothing to lose and everything to gain. Your next breakthrough in scalable data engineering starts here.
EXTENSIVE & DETAILED COURSE CURRICULUM
Module 1: Foundations of Scalable Data Systems and Kubernetes - Understanding the challenges of traditional data pipelines
- The role of orchestration in modern data engineering
- Kubernetes as the backbone for scalable data infrastructure
- Core components of Kubernetes: control plane, nodes, and pods
- Comparing Kubernetes with Docker Compose and standalone containers
- The importance of declarative configuration in data workflows
- Namespaces and resource isolation for multi-tenant data environments
- Labels and selectors for dynamic pipeline routing
- Annotations and their use in data pipeline metadata
- Kubernetes API primitives and object management
- Built-in controllers: ReplicaSet, Deployment, DaemonSet
- Job and CronJob for batch processing workloads
- SecurityContext and its implications for data processing containers
- Resource requests and limits for memory and CPU in ETL stages
- QoS classes and their impact on data pipeline stability
- Pod lifecycle and readiness for data ingestion
- Handling initialization with Init Containers
- Probe configuration: liveness, readiness, and startup
- Understanding node affinity and anti-affinity for pipeline distribution
- Taints and tolerations for dedicated data processing nodes
Module 2: Designing Data Pipelines on Kubernetes - From monolith to microservices: restructuring data workflows
- Event-driven pipeline patterns using Kubernetes
- Batch vs streaming workloads in a cluster
- Designing idempotent data transformations
- Idling and scaling down non-critical stages
- Pipeline modularity using sidecar containers
- Co-locating ETL tasks with shared storage patterns
- Data pipeline versioning using GitOps principles
- Environment parity: dev, staging, production consistency
- Multi-region pipeline deployment strategies
- Failure domains and high availability in data staging
- Health checks and automated recovery of pipeline stages
- Scheduling pipeline stages with time and data triggers
- Dependency chaining using Kubernetes Jobs and custom controllers
- Error propagation and retry mechanisms in distributed pipelines
- Dead-letter queues for failed data processing jobs
- Rate limiting and backpressure control in ingestion
- Using Job completion indexing for orderly batch execution
- Scheduling recurring data loads with CronJobs
- Timezone-aware scheduling for global data ingestion
Module 3: Storage and State Management for Data Workloads - Managing state in containerized data pipelines
- Understanding emptyDir, hostPath, and ephemeral storage
- PersistentVolume and PersistentVolumeClaim fundamentals
- StorageClass configuration for dynamic provisioning
- Selecting the right storage backend: NFS, Ceph, EBS, GPD
- ReadWriteOnce, ReadOnlyMany, ReadWriteMany access modes
- StatefulSets for ordered, stable deployments of data services
- Headless services and network identity in StatefulSets
- VolumeClaimTemplates for dynamic persistent storage
- Data retention and cleanup policies for PVCs
- Snapshot and restore strategies using Velero
- Backup scheduling for critical data pipeline state
- Migrating data between clusters using volume snapshots
- Local PersistentVolumes for high-performance caching
- Using tmpfs for transient data processing
- ConfigMaps for pipeline configuration injection
- Secrets management for database credentials and API keys
- Encrypting secrets at rest using Kubernetes encryption providers
- External secrets integration with HashiCorp Vault
- Mounting multiple volumes for complex transformation workflows
Module 4: Networking and Service Communication in Pipelines - Pod networking fundamentals and CNI plugins
- ClusterIP, NodePort, and LoadBalancer services
- Headless services for direct pod-to-pod communication
- Service discovery in multi-stage pipelines
- DNS resolution within Kubernetes clusters
- Communication patterns between ingestion, transformation, and load stages
- Using Ingress for external data source integration
- TLS termination and secure off-cluster connections
- NetworkPolicies for restricting data flow between pods
- Defining default deny policies for data security
- Allowing specific traffic between ETL microservices
- Connecting to external databases and message queues
- Using ExternalName services for legacy system integration
- Egress rules for outbound data exports
- Proxy sidecars for observability and policy enforcement
- Service mesh introduction: Istio and Linkerd
- Traffic splitting for A/B testing data pipelines
- Canary rollouts for new transformation logic
- Retry budgets and circuit breaking for flaky sources
- Timeouts and deadlines in distributed data calls
Module 5: Configuration and Templating with Helm - Why configuration management matters for data pipelines
- Introduction to Helm: the Kubernetes package manager
- Chart structure: templates, values, Chart.yaml
- Creating a Helm chart for a data ingestion pipeline
- Parameterizing pipeline configurations with values.yaml
- Using Helm dependencies for multi-component pipelines
- Subcharts for reusable transformation stages
- Conditional templates with if/else logic
- Range loops for dynamic job generation
- Named templates and partials for DRY configuration
- Testing Helm charts with helm template and lint
- Versioning charts for pipeline reproducibility
- Managing environments with Helm values files
- Using Helm secrets plugin for secure config deployment
- Rollbacks and history tracking with Helm
- Deploying production-grade pipelines with Helm upgrade
- Integrating Helm with CI/CD pipelines
- Using Helm hooks for pre-install data initialization
- Post-delete hooks for cleanup of temporary storage
- Best practices for Helm in regulated data environments
Module 6: Automating Pipeline Deployment and CI/CD - Integrating Kubernetes into CI/CD workflows
- GitHub Actions for automated pipeline testing
- GitLab CI for end-to-end data job validation
- Jenkins pipelines for enterprise deployment
- Container image building with Kaniko in-cluster
- Publishing images to private and public registries
- Image tagging strategies: semantic versioning vs commit-based
- Image scanning for vulnerabilities in data processing containers
- Policy enforcement with OPA and Gatekeeper
- Environment-specific deployments using Git branches
- Approvals and manual gates for production promotion
- Blue-green deployments for zero-downtime data updates
- Automated rollback triggers based on pipeline metrics
- Infrastructure as Code with Kustomize
- Overlay-based configuration for multiple environments
- Generating manifests with kubectl apply -k
- Managing secrets with SOPS and Age encryption
- Flux CD for GitOps continuous delivery
- Argo CD for declarative application management
- Synchronization waves for ordered data service rollout
Module 7: Scaling and Performance Optimization - Horizontal Pod Autoscaler (HPA) fundamentals
- Scaling data ingestion based on CPU and memory
- Custom metrics with Prometheus for HPA
- Scaling based on queue depth: Kafka, RabbitMQ
- Vertical Pod Autoscaler for resource efficiency
- Cluster Autoscaler for dynamic node provisioning
- Bin packing and resource utilization optimization
- Pod topology spread constraints for high availability
- Managing burst loads during end-of-day processing
- Pre-warming pipelines before scheduled jobs
- Tuning garbage collection for long-running data jobs
- JVM tuning in containerized Spark and Flink jobs
- Connection pooling for database-heavy transformations
- Optimizing I/O performance with high-speed storage classes
- Using init containers for preloading dependencies
- Caching intermediate results with Redis sidecars
- Memory-mapped files for fast data access
- Reducing network overhead with data locality
- Pipeline parallelization using fan-out/fan-in patterns
- Profiling pipeline performance with Prometheus and Grafana
Module 8: Monitoring, Logging, and Observability - The three pillars: metrics, logs, and traces
- Setting up Prometheus for Kubernetes monitoring
- Scraping metrics from pipeline components
- Creating custom metrics for data throughput
- Exporting metrics from Python and Java jobs
- Configuring Grafana dashboards for pipeline health
- Real-time visualization of transformation latency
- Alerting with Alertmanager for pipeline failures
- Notification routing: email, Slack, PagerDuty
- Log aggregation with Fluentd and Fluent Bit
- Sending logs to Elasticsearch or Loki
- Structured logging with JSON in data containers
- Tracing distributed pipelines with Jaeger and OpenTelemetry
- Context propagation across microservices
- Identifying bottlenecks using trace analysis
- Service-level objectives (SLOs) for data pipelines
- Error budgeting and burn rate monitoring
- Using kubectl top for real-time resource usage
- Custom dashboards for business KPIs
- Correlating logs, metrics, and traces for root cause analysis
Module 9: Security and Compliance for Data Pipelines - Principle of least privilege in Kubernetes RBAC
- Role and RoleBinding for data pipeline teams
- ClusterRole for cross-namespace operations
- PodSecurityPolicies and their replacement with Pod Security Admission
- Enforcing baseline, restricted, and privileged policies
- Network segmentation for sensitive data stages
- Encryption of data in transit with mTLS
- Regulatory compliance: GDPR, HIPAA, CCPA
- Audit logging for compliance reporting
- Immutable infrastructure for tamper-proof pipelines
- Secrets rotation strategies for long-running jobs
- Non-root containers and seccomp profiles
- AppArmor and SELinux integration
- Image provenance with Sigstore and cosign
- Signed deployments to prevent tampering
- Data anonymization in staging environments
- Access controls for debugging and monitoring tools
- Secure pipeline handoff between teams
- Automated compliance checks with Rego policies
- Regular vulnerability scanning with Trivy and Grype
Module 10: Real-World Projects and Integration Patterns - End-to-end project: Build a clickstream data pipeline
- Ingesting JSON logs from web servers into Kafka
- Deploying a Kafka cluster on Kubernetes with Strimzi
- Consuming messages with a Python-based transformer
- Scaling transformers based on lag metrics
- Writing processed data to PostgreSQL with connection pooling
- Scheduling daily aggregation with CronJobs
- Visualizing results with a lightweight dashboard service
- Setting up automated alerts for ingestion failures
- Project: Migrating an on-prem ETL job to Kubernetes
- Containerizing legacy shell scripts and Perl tools
- Replicating file-based workflows with PVCs
- Handling dependencies with init containers
- Simulating network latency in staging
- Integrating with Active Directory for access control
- Project: Real-time anomaly detection pipeline
- Streaming data with Apache Flink on Kubernetes
- Auto-recovery from checkpoint failures
- Exposing metrics for operations team dashboard
- Automated scale-down during idle periods
Module 11: Advanced Custom Resources and Operators - Extending Kubernetes with Custom Resource Definitions
- Building a DataPipeline custom resource
- Controller logic for lifecycle management
- Reconciling desired and actual state
- Using Kubebuilder for operator scaffolding
- Controller Runtime architecture
- Handling events and enqueueing work
- Finalizers for graceful deletion
- Owner references and garbage collection
- Operator best practices for production
- Monitoring operators with built-in metrics
- Handling partial failures in reconciliation loops
- Upgrading operators with backward compatibility
- Testing operators with envtest
- Using operators to manage Airflow, Spark, Flink clusters
- Deploying a managed Airflow instance via Operator
- Synchronizing DAG updates from Git
- Scaling Airflow workers based on DAG queue
- Automated backup of DAG metadata
- Self-healing pipeline components using operators
Module 12: Certification, Portfolio, and Next Steps - Preparing for the final assessment
- Best practices for documenting your pipeline design
- Creating a professional project portfolio
- Adding your Certificate of Completion to LinkedIn
- Highlighting Kubernetes and data pipeline skills on resumes
- Contributing to open-source data projects
- Joining Kubernetes and data engineering communities
- Staying updated with KEPs and SIGs
- Advanced certifications: CKA, CKAD, CKS
- Building a personal lab with Kind or Minikube
- Automating pipeline testing with GitHub Actions
- Sharing reusable Helm charts on Artifact Hub
- Writing technical blogs on your implementation journey
- Presenting at meetups and conferences
- Setting career goals: Data Architect, MLOps Lead, SRE
- Transitioning to platform engineering roles
- Lifetime access and continuous content updates
- Progress tracking and milestone achievements
- Gamified learning paths for sustained motivation
- Final Certificate of Completion issued by The Art of Service
Module 1: Foundations of Scalable Data Systems and Kubernetes - Understanding the challenges of traditional data pipelines
- The role of orchestration in modern data engineering
- Kubernetes as the backbone for scalable data infrastructure
- Core components of Kubernetes: control plane, nodes, and pods
- Comparing Kubernetes with Docker Compose and standalone containers
- The importance of declarative configuration in data workflows
- Namespaces and resource isolation for multi-tenant data environments
- Labels and selectors for dynamic pipeline routing
- Annotations and their use in data pipeline metadata
- Kubernetes API primitives and object management
- Built-in controllers: ReplicaSet, Deployment, DaemonSet
- Job and CronJob for batch processing workloads
- SecurityContext and its implications for data processing containers
- Resource requests and limits for memory and CPU in ETL stages
- QoS classes and their impact on data pipeline stability
- Pod lifecycle and readiness for data ingestion
- Handling initialization with Init Containers
- Probe configuration: liveness, readiness, and startup
- Understanding node affinity and anti-affinity for pipeline distribution
- Taints and tolerations for dedicated data processing nodes
Module 2: Designing Data Pipelines on Kubernetes - From monolith to microservices: restructuring data workflows
- Event-driven pipeline patterns using Kubernetes
- Batch vs streaming workloads in a cluster
- Designing idempotent data transformations
- Idling and scaling down non-critical stages
- Pipeline modularity using sidecar containers
- Co-locating ETL tasks with shared storage patterns
- Data pipeline versioning using GitOps principles
- Environment parity: dev, staging, production consistency
- Multi-region pipeline deployment strategies
- Failure domains and high availability in data staging
- Health checks and automated recovery of pipeline stages
- Scheduling pipeline stages with time and data triggers
- Dependency chaining using Kubernetes Jobs and custom controllers
- Error propagation and retry mechanisms in distributed pipelines
- Dead-letter queues for failed data processing jobs
- Rate limiting and backpressure control in ingestion
- Using Job completion indexing for orderly batch execution
- Scheduling recurring data loads with CronJobs
- Timezone-aware scheduling for global data ingestion
Module 3: Storage and State Management for Data Workloads - Managing state in containerized data pipelines
- Understanding emptyDir, hostPath, and ephemeral storage
- PersistentVolume and PersistentVolumeClaim fundamentals
- StorageClass configuration for dynamic provisioning
- Selecting the right storage backend: NFS, Ceph, EBS, GPD
- ReadWriteOnce, ReadOnlyMany, ReadWriteMany access modes
- StatefulSets for ordered, stable deployments of data services
- Headless services and network identity in StatefulSets
- VolumeClaimTemplates for dynamic persistent storage
- Data retention and cleanup policies for PVCs
- Snapshot and restore strategies using Velero
- Backup scheduling for critical data pipeline state
- Migrating data between clusters using volume snapshots
- Local PersistentVolumes for high-performance caching
- Using tmpfs for transient data processing
- ConfigMaps for pipeline configuration injection
- Secrets management for database credentials and API keys
- Encrypting secrets at rest using Kubernetes encryption providers
- External secrets integration with HashiCorp Vault
- Mounting multiple volumes for complex transformation workflows
Module 4: Networking and Service Communication in Pipelines - Pod networking fundamentals and CNI plugins
- ClusterIP, NodePort, and LoadBalancer services
- Headless services for direct pod-to-pod communication
- Service discovery in multi-stage pipelines
- DNS resolution within Kubernetes clusters
- Communication patterns between ingestion, transformation, and load stages
- Using Ingress for external data source integration
- TLS termination and secure off-cluster connections
- NetworkPolicies for restricting data flow between pods
- Defining default deny policies for data security
- Allowing specific traffic between ETL microservices
- Connecting to external databases and message queues
- Using ExternalName services for legacy system integration
- Egress rules for outbound data exports
- Proxy sidecars for observability and policy enforcement
- Service mesh introduction: Istio and Linkerd
- Traffic splitting for A/B testing data pipelines
- Canary rollouts for new transformation logic
- Retry budgets and circuit breaking for flaky sources
- Timeouts and deadlines in distributed data calls
Module 5: Configuration and Templating with Helm - Why configuration management matters for data pipelines
- Introduction to Helm: the Kubernetes package manager
- Chart structure: templates, values, Chart.yaml
- Creating a Helm chart for a data ingestion pipeline
- Parameterizing pipeline configurations with values.yaml
- Using Helm dependencies for multi-component pipelines
- Subcharts for reusable transformation stages
- Conditional templates with if/else logic
- Range loops for dynamic job generation
- Named templates and partials for DRY configuration
- Testing Helm charts with helm template and lint
- Versioning charts for pipeline reproducibility
- Managing environments with Helm values files
- Using Helm secrets plugin for secure config deployment
- Rollbacks and history tracking with Helm
- Deploying production-grade pipelines with Helm upgrade
- Integrating Helm with CI/CD pipelines
- Using Helm hooks for pre-install data initialization
- Post-delete hooks for cleanup of temporary storage
- Best practices for Helm in regulated data environments
Module 6: Automating Pipeline Deployment and CI/CD - Integrating Kubernetes into CI/CD workflows
- GitHub Actions for automated pipeline testing
- GitLab CI for end-to-end data job validation
- Jenkins pipelines for enterprise deployment
- Container image building with Kaniko in-cluster
- Publishing images to private and public registries
- Image tagging strategies: semantic versioning vs commit-based
- Image scanning for vulnerabilities in data processing containers
- Policy enforcement with OPA and Gatekeeper
- Environment-specific deployments using Git branches
- Approvals and manual gates for production promotion
- Blue-green deployments for zero-downtime data updates
- Automated rollback triggers based on pipeline metrics
- Infrastructure as Code with Kustomize
- Overlay-based configuration for multiple environments
- Generating manifests with kubectl apply -k
- Managing secrets with SOPS and Age encryption
- Flux CD for GitOps continuous delivery
- Argo CD for declarative application management
- Synchronization waves for ordered data service rollout
Module 7: Scaling and Performance Optimization - Horizontal Pod Autoscaler (HPA) fundamentals
- Scaling data ingestion based on CPU and memory
- Custom metrics with Prometheus for HPA
- Scaling based on queue depth: Kafka, RabbitMQ
- Vertical Pod Autoscaler for resource efficiency
- Cluster Autoscaler for dynamic node provisioning
- Bin packing and resource utilization optimization
- Pod topology spread constraints for high availability
- Managing burst loads during end-of-day processing
- Pre-warming pipelines before scheduled jobs
- Tuning garbage collection for long-running data jobs
- JVM tuning in containerized Spark and Flink jobs
- Connection pooling for database-heavy transformations
- Optimizing I/O performance with high-speed storage classes
- Using init containers for preloading dependencies
- Caching intermediate results with Redis sidecars
- Memory-mapped files for fast data access
- Reducing network overhead with data locality
- Pipeline parallelization using fan-out/fan-in patterns
- Profiling pipeline performance with Prometheus and Grafana
Module 8: Monitoring, Logging, and Observability - The three pillars: metrics, logs, and traces
- Setting up Prometheus for Kubernetes monitoring
- Scraping metrics from pipeline components
- Creating custom metrics for data throughput
- Exporting metrics from Python and Java jobs
- Configuring Grafana dashboards for pipeline health
- Real-time visualization of transformation latency
- Alerting with Alertmanager for pipeline failures
- Notification routing: email, Slack, PagerDuty
- Log aggregation with Fluentd and Fluent Bit
- Sending logs to Elasticsearch or Loki
- Structured logging with JSON in data containers
- Tracing distributed pipelines with Jaeger and OpenTelemetry
- Context propagation across microservices
- Identifying bottlenecks using trace analysis
- Service-level objectives (SLOs) for data pipelines
- Error budgeting and burn rate monitoring
- Using kubectl top for real-time resource usage
- Custom dashboards for business KPIs
- Correlating logs, metrics, and traces for root cause analysis
Module 9: Security and Compliance for Data Pipelines - Principle of least privilege in Kubernetes RBAC
- Role and RoleBinding for data pipeline teams
- ClusterRole for cross-namespace operations
- PodSecurityPolicies and their replacement with Pod Security Admission
- Enforcing baseline, restricted, and privileged policies
- Network segmentation for sensitive data stages
- Encryption of data in transit with mTLS
- Regulatory compliance: GDPR, HIPAA, CCPA
- Audit logging for compliance reporting
- Immutable infrastructure for tamper-proof pipelines
- Secrets rotation strategies for long-running jobs
- Non-root containers and seccomp profiles
- AppArmor and SELinux integration
- Image provenance with Sigstore and cosign
- Signed deployments to prevent tampering
- Data anonymization in staging environments
- Access controls for debugging and monitoring tools
- Secure pipeline handoff between teams
- Automated compliance checks with Rego policies
- Regular vulnerability scanning with Trivy and Grype
Module 10: Real-World Projects and Integration Patterns - End-to-end project: Build a clickstream data pipeline
- Ingesting JSON logs from web servers into Kafka
- Deploying a Kafka cluster on Kubernetes with Strimzi
- Consuming messages with a Python-based transformer
- Scaling transformers based on lag metrics
- Writing processed data to PostgreSQL with connection pooling
- Scheduling daily aggregation with CronJobs
- Visualizing results with a lightweight dashboard service
- Setting up automated alerts for ingestion failures
- Project: Migrating an on-prem ETL job to Kubernetes
- Containerizing legacy shell scripts and Perl tools
- Replicating file-based workflows with PVCs
- Handling dependencies with init containers
- Simulating network latency in staging
- Integrating with Active Directory for access control
- Project: Real-time anomaly detection pipeline
- Streaming data with Apache Flink on Kubernetes
- Auto-recovery from checkpoint failures
- Exposing metrics for operations team dashboard
- Automated scale-down during idle periods
Module 11: Advanced Custom Resources and Operators - Extending Kubernetes with Custom Resource Definitions
- Building a DataPipeline custom resource
- Controller logic for lifecycle management
- Reconciling desired and actual state
- Using Kubebuilder for operator scaffolding
- Controller Runtime architecture
- Handling events and enqueueing work
- Finalizers for graceful deletion
- Owner references and garbage collection
- Operator best practices for production
- Monitoring operators with built-in metrics
- Handling partial failures in reconciliation loops
- Upgrading operators with backward compatibility
- Testing operators with envtest
- Using operators to manage Airflow, Spark, Flink clusters
- Deploying a managed Airflow instance via Operator
- Synchronizing DAG updates from Git
- Scaling Airflow workers based on DAG queue
- Automated backup of DAG metadata
- Self-healing pipeline components using operators
Module 12: Certification, Portfolio, and Next Steps - Preparing for the final assessment
- Best practices for documenting your pipeline design
- Creating a professional project portfolio
- Adding your Certificate of Completion to LinkedIn
- Highlighting Kubernetes and data pipeline skills on resumes
- Contributing to open-source data projects
- Joining Kubernetes and data engineering communities
- Staying updated with KEPs and SIGs
- Advanced certifications: CKA, CKAD, CKS
- Building a personal lab with Kind or Minikube
- Automating pipeline testing with GitHub Actions
- Sharing reusable Helm charts on Artifact Hub
- Writing technical blogs on your implementation journey
- Presenting at meetups and conferences
- Setting career goals: Data Architect, MLOps Lead, SRE
- Transitioning to platform engineering roles
- Lifetime access and continuous content updates
- Progress tracking and milestone achievements
- Gamified learning paths for sustained motivation
- Final Certificate of Completion issued by The Art of Service
- From monolith to microservices: restructuring data workflows
- Event-driven pipeline patterns using Kubernetes
- Batch vs streaming workloads in a cluster
- Designing idempotent data transformations
- Idling and scaling down non-critical stages
- Pipeline modularity using sidecar containers
- Co-locating ETL tasks with shared storage patterns
- Data pipeline versioning using GitOps principles
- Environment parity: dev, staging, production consistency
- Multi-region pipeline deployment strategies
- Failure domains and high availability in data staging
- Health checks and automated recovery of pipeline stages
- Scheduling pipeline stages with time and data triggers
- Dependency chaining using Kubernetes Jobs and custom controllers
- Error propagation and retry mechanisms in distributed pipelines
- Dead-letter queues for failed data processing jobs
- Rate limiting and backpressure control in ingestion
- Using Job completion indexing for orderly batch execution
- Scheduling recurring data loads with CronJobs
- Timezone-aware scheduling for global data ingestion
Module 3: Storage and State Management for Data Workloads - Managing state in containerized data pipelines
- Understanding emptyDir, hostPath, and ephemeral storage
- PersistentVolume and PersistentVolumeClaim fundamentals
- StorageClass configuration for dynamic provisioning
- Selecting the right storage backend: NFS, Ceph, EBS, GPD
- ReadWriteOnce, ReadOnlyMany, ReadWriteMany access modes
- StatefulSets for ordered, stable deployments of data services
- Headless services and network identity in StatefulSets
- VolumeClaimTemplates for dynamic persistent storage
- Data retention and cleanup policies for PVCs
- Snapshot and restore strategies using Velero
- Backup scheduling for critical data pipeline state
- Migrating data between clusters using volume snapshots
- Local PersistentVolumes for high-performance caching
- Using tmpfs for transient data processing
- ConfigMaps for pipeline configuration injection
- Secrets management for database credentials and API keys
- Encrypting secrets at rest using Kubernetes encryption providers
- External secrets integration with HashiCorp Vault
- Mounting multiple volumes for complex transformation workflows
Module 4: Networking and Service Communication in Pipelines - Pod networking fundamentals and CNI plugins
- ClusterIP, NodePort, and LoadBalancer services
- Headless services for direct pod-to-pod communication
- Service discovery in multi-stage pipelines
- DNS resolution within Kubernetes clusters
- Communication patterns between ingestion, transformation, and load stages
- Using Ingress for external data source integration
- TLS termination and secure off-cluster connections
- NetworkPolicies for restricting data flow between pods
- Defining default deny policies for data security
- Allowing specific traffic between ETL microservices
- Connecting to external databases and message queues
- Using ExternalName services for legacy system integration
- Egress rules for outbound data exports
- Proxy sidecars for observability and policy enforcement
- Service mesh introduction: Istio and Linkerd
- Traffic splitting for A/B testing data pipelines
- Canary rollouts for new transformation logic
- Retry budgets and circuit breaking for flaky sources
- Timeouts and deadlines in distributed data calls
Module 5: Configuration and Templating with Helm - Why configuration management matters for data pipelines
- Introduction to Helm: the Kubernetes package manager
- Chart structure: templates, values, Chart.yaml
- Creating a Helm chart for a data ingestion pipeline
- Parameterizing pipeline configurations with values.yaml
- Using Helm dependencies for multi-component pipelines
- Subcharts for reusable transformation stages
- Conditional templates with if/else logic
- Range loops for dynamic job generation
- Named templates and partials for DRY configuration
- Testing Helm charts with helm template and lint
- Versioning charts for pipeline reproducibility
- Managing environments with Helm values files
- Using Helm secrets plugin for secure config deployment
- Rollbacks and history tracking with Helm
- Deploying production-grade pipelines with Helm upgrade
- Integrating Helm with CI/CD pipelines
- Using Helm hooks for pre-install data initialization
- Post-delete hooks for cleanup of temporary storage
- Best practices for Helm in regulated data environments
Module 6: Automating Pipeline Deployment and CI/CD - Integrating Kubernetes into CI/CD workflows
- GitHub Actions for automated pipeline testing
- GitLab CI for end-to-end data job validation
- Jenkins pipelines for enterprise deployment
- Container image building with Kaniko in-cluster
- Publishing images to private and public registries
- Image tagging strategies: semantic versioning vs commit-based
- Image scanning for vulnerabilities in data processing containers
- Policy enforcement with OPA and Gatekeeper
- Environment-specific deployments using Git branches
- Approvals and manual gates for production promotion
- Blue-green deployments for zero-downtime data updates
- Automated rollback triggers based on pipeline metrics
- Infrastructure as Code with Kustomize
- Overlay-based configuration for multiple environments
- Generating manifests with kubectl apply -k
- Managing secrets with SOPS and Age encryption
- Flux CD for GitOps continuous delivery
- Argo CD for declarative application management
- Synchronization waves for ordered data service rollout
Module 7: Scaling and Performance Optimization - Horizontal Pod Autoscaler (HPA) fundamentals
- Scaling data ingestion based on CPU and memory
- Custom metrics with Prometheus for HPA
- Scaling based on queue depth: Kafka, RabbitMQ
- Vertical Pod Autoscaler for resource efficiency
- Cluster Autoscaler for dynamic node provisioning
- Bin packing and resource utilization optimization
- Pod topology spread constraints for high availability
- Managing burst loads during end-of-day processing
- Pre-warming pipelines before scheduled jobs
- Tuning garbage collection for long-running data jobs
- JVM tuning in containerized Spark and Flink jobs
- Connection pooling for database-heavy transformations
- Optimizing I/O performance with high-speed storage classes
- Using init containers for preloading dependencies
- Caching intermediate results with Redis sidecars
- Memory-mapped files for fast data access
- Reducing network overhead with data locality
- Pipeline parallelization using fan-out/fan-in patterns
- Profiling pipeline performance with Prometheus and Grafana
Module 8: Monitoring, Logging, and Observability - The three pillars: metrics, logs, and traces
- Setting up Prometheus for Kubernetes monitoring
- Scraping metrics from pipeline components
- Creating custom metrics for data throughput
- Exporting metrics from Python and Java jobs
- Configuring Grafana dashboards for pipeline health
- Real-time visualization of transformation latency
- Alerting with Alertmanager for pipeline failures
- Notification routing: email, Slack, PagerDuty
- Log aggregation with Fluentd and Fluent Bit
- Sending logs to Elasticsearch or Loki
- Structured logging with JSON in data containers
- Tracing distributed pipelines with Jaeger and OpenTelemetry
- Context propagation across microservices
- Identifying bottlenecks using trace analysis
- Service-level objectives (SLOs) for data pipelines
- Error budgeting and burn rate monitoring
- Using kubectl top for real-time resource usage
- Custom dashboards for business KPIs
- Correlating logs, metrics, and traces for root cause analysis
Module 9: Security and Compliance for Data Pipelines - Principle of least privilege in Kubernetes RBAC
- Role and RoleBinding for data pipeline teams
- ClusterRole for cross-namespace operations
- PodSecurityPolicies and their replacement with Pod Security Admission
- Enforcing baseline, restricted, and privileged policies
- Network segmentation for sensitive data stages
- Encryption of data in transit with mTLS
- Regulatory compliance: GDPR, HIPAA, CCPA
- Audit logging for compliance reporting
- Immutable infrastructure for tamper-proof pipelines
- Secrets rotation strategies for long-running jobs
- Non-root containers and seccomp profiles
- AppArmor and SELinux integration
- Image provenance with Sigstore and cosign
- Signed deployments to prevent tampering
- Data anonymization in staging environments
- Access controls for debugging and monitoring tools
- Secure pipeline handoff between teams
- Automated compliance checks with Rego policies
- Regular vulnerability scanning with Trivy and Grype
Module 10: Real-World Projects and Integration Patterns - End-to-end project: Build a clickstream data pipeline
- Ingesting JSON logs from web servers into Kafka
- Deploying a Kafka cluster on Kubernetes with Strimzi
- Consuming messages with a Python-based transformer
- Scaling transformers based on lag metrics
- Writing processed data to PostgreSQL with connection pooling
- Scheduling daily aggregation with CronJobs
- Visualizing results with a lightweight dashboard service
- Setting up automated alerts for ingestion failures
- Project: Migrating an on-prem ETL job to Kubernetes
- Containerizing legacy shell scripts and Perl tools
- Replicating file-based workflows with PVCs
- Handling dependencies with init containers
- Simulating network latency in staging
- Integrating with Active Directory for access control
- Project: Real-time anomaly detection pipeline
- Streaming data with Apache Flink on Kubernetes
- Auto-recovery from checkpoint failures
- Exposing metrics for operations team dashboard
- Automated scale-down during idle periods
Module 11: Advanced Custom Resources and Operators - Extending Kubernetes with Custom Resource Definitions
- Building a DataPipeline custom resource
- Controller logic for lifecycle management
- Reconciling desired and actual state
- Using Kubebuilder for operator scaffolding
- Controller Runtime architecture
- Handling events and enqueueing work
- Finalizers for graceful deletion
- Owner references and garbage collection
- Operator best practices for production
- Monitoring operators with built-in metrics
- Handling partial failures in reconciliation loops
- Upgrading operators with backward compatibility
- Testing operators with envtest
- Using operators to manage Airflow, Spark, Flink clusters
- Deploying a managed Airflow instance via Operator
- Synchronizing DAG updates from Git
- Scaling Airflow workers based on DAG queue
- Automated backup of DAG metadata
- Self-healing pipeline components using operators
Module 12: Certification, Portfolio, and Next Steps - Preparing for the final assessment
- Best practices for documenting your pipeline design
- Creating a professional project portfolio
- Adding your Certificate of Completion to LinkedIn
- Highlighting Kubernetes and data pipeline skills on resumes
- Contributing to open-source data projects
- Joining Kubernetes and data engineering communities
- Staying updated with KEPs and SIGs
- Advanced certifications: CKA, CKAD, CKS
- Building a personal lab with Kind or Minikube
- Automating pipeline testing with GitHub Actions
- Sharing reusable Helm charts on Artifact Hub
- Writing technical blogs on your implementation journey
- Presenting at meetups and conferences
- Setting career goals: Data Architect, MLOps Lead, SRE
- Transitioning to platform engineering roles
- Lifetime access and continuous content updates
- Progress tracking and milestone achievements
- Gamified learning paths for sustained motivation
- Final Certificate of Completion issued by The Art of Service
- Pod networking fundamentals and CNI plugins
- ClusterIP, NodePort, and LoadBalancer services
- Headless services for direct pod-to-pod communication
- Service discovery in multi-stage pipelines
- DNS resolution within Kubernetes clusters
- Communication patterns between ingestion, transformation, and load stages
- Using Ingress for external data source integration
- TLS termination and secure off-cluster connections
- NetworkPolicies for restricting data flow between pods
- Defining default deny policies for data security
- Allowing specific traffic between ETL microservices
- Connecting to external databases and message queues
- Using ExternalName services for legacy system integration
- Egress rules for outbound data exports
- Proxy sidecars for observability and policy enforcement
- Service mesh introduction: Istio and Linkerd
- Traffic splitting for A/B testing data pipelines
- Canary rollouts for new transformation logic
- Retry budgets and circuit breaking for flaky sources
- Timeouts and deadlines in distributed data calls
Module 5: Configuration and Templating with Helm - Why configuration management matters for data pipelines
- Introduction to Helm: the Kubernetes package manager
- Chart structure: templates, values, Chart.yaml
- Creating a Helm chart for a data ingestion pipeline
- Parameterizing pipeline configurations with values.yaml
- Using Helm dependencies for multi-component pipelines
- Subcharts for reusable transformation stages
- Conditional templates with if/else logic
- Range loops for dynamic job generation
- Named templates and partials for DRY configuration
- Testing Helm charts with helm template and lint
- Versioning charts for pipeline reproducibility
- Managing environments with Helm values files
- Using Helm secrets plugin for secure config deployment
- Rollbacks and history tracking with Helm
- Deploying production-grade pipelines with Helm upgrade
- Integrating Helm with CI/CD pipelines
- Using Helm hooks for pre-install data initialization
- Post-delete hooks for cleanup of temporary storage
- Best practices for Helm in regulated data environments
Module 6: Automating Pipeline Deployment and CI/CD - Integrating Kubernetes into CI/CD workflows
- GitHub Actions for automated pipeline testing
- GitLab CI for end-to-end data job validation
- Jenkins pipelines for enterprise deployment
- Container image building with Kaniko in-cluster
- Publishing images to private and public registries
- Image tagging strategies: semantic versioning vs commit-based
- Image scanning for vulnerabilities in data processing containers
- Policy enforcement with OPA and Gatekeeper
- Environment-specific deployments using Git branches
- Approvals and manual gates for production promotion
- Blue-green deployments for zero-downtime data updates
- Automated rollback triggers based on pipeline metrics
- Infrastructure as Code with Kustomize
- Overlay-based configuration for multiple environments
- Generating manifests with kubectl apply -k
- Managing secrets with SOPS and Age encryption
- Flux CD for GitOps continuous delivery
- Argo CD for declarative application management
- Synchronization waves for ordered data service rollout
Module 7: Scaling and Performance Optimization - Horizontal Pod Autoscaler (HPA) fundamentals
- Scaling data ingestion based on CPU and memory
- Custom metrics with Prometheus for HPA
- Scaling based on queue depth: Kafka, RabbitMQ
- Vertical Pod Autoscaler for resource efficiency
- Cluster Autoscaler for dynamic node provisioning
- Bin packing and resource utilization optimization
- Pod topology spread constraints for high availability
- Managing burst loads during end-of-day processing
- Pre-warming pipelines before scheduled jobs
- Tuning garbage collection for long-running data jobs
- JVM tuning in containerized Spark and Flink jobs
- Connection pooling for database-heavy transformations
- Optimizing I/O performance with high-speed storage classes
- Using init containers for preloading dependencies
- Caching intermediate results with Redis sidecars
- Memory-mapped files for fast data access
- Reducing network overhead with data locality
- Pipeline parallelization using fan-out/fan-in patterns
- Profiling pipeline performance with Prometheus and Grafana
Module 8: Monitoring, Logging, and Observability - The three pillars: metrics, logs, and traces
- Setting up Prometheus for Kubernetes monitoring
- Scraping metrics from pipeline components
- Creating custom metrics for data throughput
- Exporting metrics from Python and Java jobs
- Configuring Grafana dashboards for pipeline health
- Real-time visualization of transformation latency
- Alerting with Alertmanager for pipeline failures
- Notification routing: email, Slack, PagerDuty
- Log aggregation with Fluentd and Fluent Bit
- Sending logs to Elasticsearch or Loki
- Structured logging with JSON in data containers
- Tracing distributed pipelines with Jaeger and OpenTelemetry
- Context propagation across microservices
- Identifying bottlenecks using trace analysis
- Service-level objectives (SLOs) for data pipelines
- Error budgeting and burn rate monitoring
- Using kubectl top for real-time resource usage
- Custom dashboards for business KPIs
- Correlating logs, metrics, and traces for root cause analysis
Module 9: Security and Compliance for Data Pipelines - Principle of least privilege in Kubernetes RBAC
- Role and RoleBinding for data pipeline teams
- ClusterRole for cross-namespace operations
- PodSecurityPolicies and their replacement with Pod Security Admission
- Enforcing baseline, restricted, and privileged policies
- Network segmentation for sensitive data stages
- Encryption of data in transit with mTLS
- Regulatory compliance: GDPR, HIPAA, CCPA
- Audit logging for compliance reporting
- Immutable infrastructure for tamper-proof pipelines
- Secrets rotation strategies for long-running jobs
- Non-root containers and seccomp profiles
- AppArmor and SELinux integration
- Image provenance with Sigstore and cosign
- Signed deployments to prevent tampering
- Data anonymization in staging environments
- Access controls for debugging and monitoring tools
- Secure pipeline handoff between teams
- Automated compliance checks with Rego policies
- Regular vulnerability scanning with Trivy and Grype
Module 10: Real-World Projects and Integration Patterns - End-to-end project: Build a clickstream data pipeline
- Ingesting JSON logs from web servers into Kafka
- Deploying a Kafka cluster on Kubernetes with Strimzi
- Consuming messages with a Python-based transformer
- Scaling transformers based on lag metrics
- Writing processed data to PostgreSQL with connection pooling
- Scheduling daily aggregation with CronJobs
- Visualizing results with a lightweight dashboard service
- Setting up automated alerts for ingestion failures
- Project: Migrating an on-prem ETL job to Kubernetes
- Containerizing legacy shell scripts and Perl tools
- Replicating file-based workflows with PVCs
- Handling dependencies with init containers
- Simulating network latency in staging
- Integrating with Active Directory for access control
- Project: Real-time anomaly detection pipeline
- Streaming data with Apache Flink on Kubernetes
- Auto-recovery from checkpoint failures
- Exposing metrics for operations team dashboard
- Automated scale-down during idle periods
Module 11: Advanced Custom Resources and Operators - Extending Kubernetes with Custom Resource Definitions
- Building a DataPipeline custom resource
- Controller logic for lifecycle management
- Reconciling desired and actual state
- Using Kubebuilder for operator scaffolding
- Controller Runtime architecture
- Handling events and enqueueing work
- Finalizers for graceful deletion
- Owner references and garbage collection
- Operator best practices for production
- Monitoring operators with built-in metrics
- Handling partial failures in reconciliation loops
- Upgrading operators with backward compatibility
- Testing operators with envtest
- Using operators to manage Airflow, Spark, Flink clusters
- Deploying a managed Airflow instance via Operator
- Synchronizing DAG updates from Git
- Scaling Airflow workers based on DAG queue
- Automated backup of DAG metadata
- Self-healing pipeline components using operators
Module 12: Certification, Portfolio, and Next Steps - Preparing for the final assessment
- Best practices for documenting your pipeline design
- Creating a professional project portfolio
- Adding your Certificate of Completion to LinkedIn
- Highlighting Kubernetes and data pipeline skills on resumes
- Contributing to open-source data projects
- Joining Kubernetes and data engineering communities
- Staying updated with KEPs and SIGs
- Advanced certifications: CKA, CKAD, CKS
- Building a personal lab with Kind or Minikube
- Automating pipeline testing with GitHub Actions
- Sharing reusable Helm charts on Artifact Hub
- Writing technical blogs on your implementation journey
- Presenting at meetups and conferences
- Setting career goals: Data Architect, MLOps Lead, SRE
- Transitioning to platform engineering roles
- Lifetime access and continuous content updates
- Progress tracking and milestone achievements
- Gamified learning paths for sustained motivation
- Final Certificate of Completion issued by The Art of Service
- Integrating Kubernetes into CI/CD workflows
- GitHub Actions for automated pipeline testing
- GitLab CI for end-to-end data job validation
- Jenkins pipelines for enterprise deployment
- Container image building with Kaniko in-cluster
- Publishing images to private and public registries
- Image tagging strategies: semantic versioning vs commit-based
- Image scanning for vulnerabilities in data processing containers
- Policy enforcement with OPA and Gatekeeper
- Environment-specific deployments using Git branches
- Approvals and manual gates for production promotion
- Blue-green deployments for zero-downtime data updates
- Automated rollback triggers based on pipeline metrics
- Infrastructure as Code with Kustomize
- Overlay-based configuration for multiple environments
- Generating manifests with kubectl apply -k
- Managing secrets with SOPS and Age encryption
- Flux CD for GitOps continuous delivery
- Argo CD for declarative application management
- Synchronization waves for ordered data service rollout
Module 7: Scaling and Performance Optimization - Horizontal Pod Autoscaler (HPA) fundamentals
- Scaling data ingestion based on CPU and memory
- Custom metrics with Prometheus for HPA
- Scaling based on queue depth: Kafka, RabbitMQ
- Vertical Pod Autoscaler for resource efficiency
- Cluster Autoscaler for dynamic node provisioning
- Bin packing and resource utilization optimization
- Pod topology spread constraints for high availability
- Managing burst loads during end-of-day processing
- Pre-warming pipelines before scheduled jobs
- Tuning garbage collection for long-running data jobs
- JVM tuning in containerized Spark and Flink jobs
- Connection pooling for database-heavy transformations
- Optimizing I/O performance with high-speed storage classes
- Using init containers for preloading dependencies
- Caching intermediate results with Redis sidecars
- Memory-mapped files for fast data access
- Reducing network overhead with data locality
- Pipeline parallelization using fan-out/fan-in patterns
- Profiling pipeline performance with Prometheus and Grafana
Module 8: Monitoring, Logging, and Observability - The three pillars: metrics, logs, and traces
- Setting up Prometheus for Kubernetes monitoring
- Scraping metrics from pipeline components
- Creating custom metrics for data throughput
- Exporting metrics from Python and Java jobs
- Configuring Grafana dashboards for pipeline health
- Real-time visualization of transformation latency
- Alerting with Alertmanager for pipeline failures
- Notification routing: email, Slack, PagerDuty
- Log aggregation with Fluentd and Fluent Bit
- Sending logs to Elasticsearch or Loki
- Structured logging with JSON in data containers
- Tracing distributed pipelines with Jaeger and OpenTelemetry
- Context propagation across microservices
- Identifying bottlenecks using trace analysis
- Service-level objectives (SLOs) for data pipelines
- Error budgeting and burn rate monitoring
- Using kubectl top for real-time resource usage
- Custom dashboards for business KPIs
- Correlating logs, metrics, and traces for root cause analysis
Module 9: Security and Compliance for Data Pipelines - Principle of least privilege in Kubernetes RBAC
- Role and RoleBinding for data pipeline teams
- ClusterRole for cross-namespace operations
- PodSecurityPolicies and their replacement with Pod Security Admission
- Enforcing baseline, restricted, and privileged policies
- Network segmentation for sensitive data stages
- Encryption of data in transit with mTLS
- Regulatory compliance: GDPR, HIPAA, CCPA
- Audit logging for compliance reporting
- Immutable infrastructure for tamper-proof pipelines
- Secrets rotation strategies for long-running jobs
- Non-root containers and seccomp profiles
- AppArmor and SELinux integration
- Image provenance with Sigstore and cosign
- Signed deployments to prevent tampering
- Data anonymization in staging environments
- Access controls for debugging and monitoring tools
- Secure pipeline handoff between teams
- Automated compliance checks with Rego policies
- Regular vulnerability scanning with Trivy and Grype
Module 10: Real-World Projects and Integration Patterns - End-to-end project: Build a clickstream data pipeline
- Ingesting JSON logs from web servers into Kafka
- Deploying a Kafka cluster on Kubernetes with Strimzi
- Consuming messages with a Python-based transformer
- Scaling transformers based on lag metrics
- Writing processed data to PostgreSQL with connection pooling
- Scheduling daily aggregation with CronJobs
- Visualizing results with a lightweight dashboard service
- Setting up automated alerts for ingestion failures
- Project: Migrating an on-prem ETL job to Kubernetes
- Containerizing legacy shell scripts and Perl tools
- Replicating file-based workflows with PVCs
- Handling dependencies with init containers
- Simulating network latency in staging
- Integrating with Active Directory for access control
- Project: Real-time anomaly detection pipeline
- Streaming data with Apache Flink on Kubernetes
- Auto-recovery from checkpoint failures
- Exposing metrics for operations team dashboard
- Automated scale-down during idle periods
Module 11: Advanced Custom Resources and Operators - Extending Kubernetes with Custom Resource Definitions
- Building a DataPipeline custom resource
- Controller logic for lifecycle management
- Reconciling desired and actual state
- Using Kubebuilder for operator scaffolding
- Controller Runtime architecture
- Handling events and enqueueing work
- Finalizers for graceful deletion
- Owner references and garbage collection
- Operator best practices for production
- Monitoring operators with built-in metrics
- Handling partial failures in reconciliation loops
- Upgrading operators with backward compatibility
- Testing operators with envtest
- Using operators to manage Airflow, Spark, Flink clusters
- Deploying a managed Airflow instance via Operator
- Synchronizing DAG updates from Git
- Scaling Airflow workers based on DAG queue
- Automated backup of DAG metadata
- Self-healing pipeline components using operators
Module 12: Certification, Portfolio, and Next Steps - Preparing for the final assessment
- Best practices for documenting your pipeline design
- Creating a professional project portfolio
- Adding your Certificate of Completion to LinkedIn
- Highlighting Kubernetes and data pipeline skills on resumes
- Contributing to open-source data projects
- Joining Kubernetes and data engineering communities
- Staying updated with KEPs and SIGs
- Advanced certifications: CKA, CKAD, CKS
- Building a personal lab with Kind or Minikube
- Automating pipeline testing with GitHub Actions
- Sharing reusable Helm charts on Artifact Hub
- Writing technical blogs on your implementation journey
- Presenting at meetups and conferences
- Setting career goals: Data Architect, MLOps Lead, SRE
- Transitioning to platform engineering roles
- Lifetime access and continuous content updates
- Progress tracking and milestone achievements
- Gamified learning paths for sustained motivation
- Final Certificate of Completion issued by The Art of Service
- The three pillars: metrics, logs, and traces
- Setting up Prometheus for Kubernetes monitoring
- Scraping metrics from pipeline components
- Creating custom metrics for data throughput
- Exporting metrics from Python and Java jobs
- Configuring Grafana dashboards for pipeline health
- Real-time visualization of transformation latency
- Alerting with Alertmanager for pipeline failures
- Notification routing: email, Slack, PagerDuty
- Log aggregation with Fluentd and Fluent Bit
- Sending logs to Elasticsearch or Loki
- Structured logging with JSON in data containers
- Tracing distributed pipelines with Jaeger and OpenTelemetry
- Context propagation across microservices
- Identifying bottlenecks using trace analysis
- Service-level objectives (SLOs) for data pipelines
- Error budgeting and burn rate monitoring
- Using kubectl top for real-time resource usage
- Custom dashboards for business KPIs
- Correlating logs, metrics, and traces for root cause analysis
Module 9: Security and Compliance for Data Pipelines - Principle of least privilege in Kubernetes RBAC
- Role and RoleBinding for data pipeline teams
- ClusterRole for cross-namespace operations
- PodSecurityPolicies and their replacement with Pod Security Admission
- Enforcing baseline, restricted, and privileged policies
- Network segmentation for sensitive data stages
- Encryption of data in transit with mTLS
- Regulatory compliance: GDPR, HIPAA, CCPA
- Audit logging for compliance reporting
- Immutable infrastructure for tamper-proof pipelines
- Secrets rotation strategies for long-running jobs
- Non-root containers and seccomp profiles
- AppArmor and SELinux integration
- Image provenance with Sigstore and cosign
- Signed deployments to prevent tampering
- Data anonymization in staging environments
- Access controls for debugging and monitoring tools
- Secure pipeline handoff between teams
- Automated compliance checks with Rego policies
- Regular vulnerability scanning with Trivy and Grype
Module 10: Real-World Projects and Integration Patterns - End-to-end project: Build a clickstream data pipeline
- Ingesting JSON logs from web servers into Kafka
- Deploying a Kafka cluster on Kubernetes with Strimzi
- Consuming messages with a Python-based transformer
- Scaling transformers based on lag metrics
- Writing processed data to PostgreSQL with connection pooling
- Scheduling daily aggregation with CronJobs
- Visualizing results with a lightweight dashboard service
- Setting up automated alerts for ingestion failures
- Project: Migrating an on-prem ETL job to Kubernetes
- Containerizing legacy shell scripts and Perl tools
- Replicating file-based workflows with PVCs
- Handling dependencies with init containers
- Simulating network latency in staging
- Integrating with Active Directory for access control
- Project: Real-time anomaly detection pipeline
- Streaming data with Apache Flink on Kubernetes
- Auto-recovery from checkpoint failures
- Exposing metrics for operations team dashboard
- Automated scale-down during idle periods
Module 11: Advanced Custom Resources and Operators - Extending Kubernetes with Custom Resource Definitions
- Building a DataPipeline custom resource
- Controller logic for lifecycle management
- Reconciling desired and actual state
- Using Kubebuilder for operator scaffolding
- Controller Runtime architecture
- Handling events and enqueueing work
- Finalizers for graceful deletion
- Owner references and garbage collection
- Operator best practices for production
- Monitoring operators with built-in metrics
- Handling partial failures in reconciliation loops
- Upgrading operators with backward compatibility
- Testing operators with envtest
- Using operators to manage Airflow, Spark, Flink clusters
- Deploying a managed Airflow instance via Operator
- Synchronizing DAG updates from Git
- Scaling Airflow workers based on DAG queue
- Automated backup of DAG metadata
- Self-healing pipeline components using operators
Module 12: Certification, Portfolio, and Next Steps - Preparing for the final assessment
- Best practices for documenting your pipeline design
- Creating a professional project portfolio
- Adding your Certificate of Completion to LinkedIn
- Highlighting Kubernetes and data pipeline skills on resumes
- Contributing to open-source data projects
- Joining Kubernetes and data engineering communities
- Staying updated with KEPs and SIGs
- Advanced certifications: CKA, CKAD, CKS
- Building a personal lab with Kind or Minikube
- Automating pipeline testing with GitHub Actions
- Sharing reusable Helm charts on Artifact Hub
- Writing technical blogs on your implementation journey
- Presenting at meetups and conferences
- Setting career goals: Data Architect, MLOps Lead, SRE
- Transitioning to platform engineering roles
- Lifetime access and continuous content updates
- Progress tracking and milestone achievements
- Gamified learning paths for sustained motivation
- Final Certificate of Completion issued by The Art of Service
- End-to-end project: Build a clickstream data pipeline
- Ingesting JSON logs from web servers into Kafka
- Deploying a Kafka cluster on Kubernetes with Strimzi
- Consuming messages with a Python-based transformer
- Scaling transformers based on lag metrics
- Writing processed data to PostgreSQL with connection pooling
- Scheduling daily aggregation with CronJobs
- Visualizing results with a lightweight dashboard service
- Setting up automated alerts for ingestion failures
- Project: Migrating an on-prem ETL job to Kubernetes
- Containerizing legacy shell scripts and Perl tools
- Replicating file-based workflows with PVCs
- Handling dependencies with init containers
- Simulating network latency in staging
- Integrating with Active Directory for access control
- Project: Real-time anomaly detection pipeline
- Streaming data with Apache Flink on Kubernetes
- Auto-recovery from checkpoint failures
- Exposing metrics for operations team dashboard
- Automated scale-down during idle periods
Module 11: Advanced Custom Resources and Operators - Extending Kubernetes with Custom Resource Definitions
- Building a DataPipeline custom resource
- Controller logic for lifecycle management
- Reconciling desired and actual state
- Using Kubebuilder for operator scaffolding
- Controller Runtime architecture
- Handling events and enqueueing work
- Finalizers for graceful deletion
- Owner references and garbage collection
- Operator best practices for production
- Monitoring operators with built-in metrics
- Handling partial failures in reconciliation loops
- Upgrading operators with backward compatibility
- Testing operators with envtest
- Using operators to manage Airflow, Spark, Flink clusters
- Deploying a managed Airflow instance via Operator
- Synchronizing DAG updates from Git
- Scaling Airflow workers based on DAG queue
- Automated backup of DAG metadata
- Self-healing pipeline components using operators
Module 12: Certification, Portfolio, and Next Steps - Preparing for the final assessment
- Best practices for documenting your pipeline design
- Creating a professional project portfolio
- Adding your Certificate of Completion to LinkedIn
- Highlighting Kubernetes and data pipeline skills on resumes
- Contributing to open-source data projects
- Joining Kubernetes and data engineering communities
- Staying updated with KEPs and SIGs
- Advanced certifications: CKA, CKAD, CKS
- Building a personal lab with Kind or Minikube
- Automating pipeline testing with GitHub Actions
- Sharing reusable Helm charts on Artifact Hub
- Writing technical blogs on your implementation journey
- Presenting at meetups and conferences
- Setting career goals: Data Architect, MLOps Lead, SRE
- Transitioning to platform engineering roles
- Lifetime access and continuous content updates
- Progress tracking and milestone achievements
- Gamified learning paths for sustained motivation
- Final Certificate of Completion issued by The Art of Service
- Preparing for the final assessment
- Best practices for documenting your pipeline design
- Creating a professional project portfolio
- Adding your Certificate of Completion to LinkedIn
- Highlighting Kubernetes and data pipeline skills on resumes
- Contributing to open-source data projects
- Joining Kubernetes and data engineering communities
- Staying updated with KEPs and SIGs
- Advanced certifications: CKA, CKAD, CKS
- Building a personal lab with Kind or Minikube
- Automating pipeline testing with GitHub Actions
- Sharing reusable Helm charts on Artifact Hub
- Writing technical blogs on your implementation journey
- Presenting at meetups and conferences
- Setting career goals: Data Architect, MLOps Lead, SRE
- Transitioning to platform engineering roles
- Lifetime access and continuous content updates
- Progress tracking and milestone achievements
- Gamified learning paths for sustained motivation
- Final Certificate of Completion issued by The Art of Service