Mastering Real-Time Data Pipelines with Modern Cloud Architecture
You're under pressure. Your organisation needs faster insights, but your data pipelines are fragile, slow to adapt, and costly to maintain. You're expected to deliver real-time analytics, event-driven automation, and scalable cloud solutions-yet the tools, architectures, and best practices evolve too fast to keep up. Every delay costs you credibility. Every failed deployment questions your technical judgment. And every promotion cycle passes by while others get the high-visibility, AI-ready infrastructure projects you know you’re capable of leading. Now imagine walking into that boardroom with a battle-tested, production-grade real-time data pipeline fully documented, optimised for cost and performance, and built on proven modern cloud patterns. Not theoretical. Not academic. But practical, repeatable, and designed for enterprise impact. Mastering Real-Time Data Pipelines with Modern Cloud Architecture is not another tutorial series. It’s a complete implementation blueprint that takes you from concept to compliant, scalable pipeline in under 30 days-with full architecture diagrams, deployment checklists, and a board-ready deployment proposal you can customise and present immediately. You’ll join engineers like Nadeem K., Senior Cloud Architect at a Fortune 500 financial services firm, who used this exact framework to reduce his company’s fraud detection latency from 45 minutes to 1.8 seconds-earning him a spot on the executive innovation taskforce and a 22% salary increase within six months. No fluff. No filler. Just the precise knowledge, structured workflows, and industry-recognised certification you need to move from overlooked to indispensable. Here’s how this course is structured to help you get there.Course Format & Delivery Details Fully Self-Paced, On-Demand, & Designed for Real Careers
This course is self-paced with immediate online access upon confirmation of enrollment. You progress at your own speed, on your schedule, without fixed deadlines or mandatory live sessions. Most learners complete the core implementation project in 28 to 35 hours and deploy their first pipeline prototype within 10 days. Once enrolled, you gain 24/7 global access to all course materials from any device-including full mobile compatibility. Whether you're refining architecture on your tablet during travel or troubleshooting ingestion flows from your phone, the learning environment adapts to your workflow. Lifetime Access with Ongoing Updates at No Extra Cost
Your investment includes lifetime access to all current and future updates. Cloud platforms evolve, and so do best practices. We continuously refresh deployment templates, security protocols, and integration patterns to reflect AWS, GCP, and Azure’s latest services-ensuring your skills remain modern and your certification stays relevant for years. - Detailed architectural blueprints updated quarterly
- New real-world case studies added biannually
- Automated pipeline testing frameworks revised with tooling changes
- Zero additional fees-ever
Direct Instructor Guidance & Implementation Support
You’re not alone. Throughout the course, you receive structured guidance from certified cloud architects with over 15 years of production experience in financial, healthcare, and logistics sectors. This includes access to expert-written decision trees, code walkthroughs, and troubleshooting playbooks designed to eliminate roadblocks in deployment, scaling, and monitoring. Ask precise implementation questions through the secure learning portal and receive timely, technical responses focused on your use case-whether you’re building stream processing for IoT telemetry or event sourcing for customer journey analytics. Industry-Recognised Certification & Career Validation
Upon successful completion, you earn a Certificate of Completion issued by The Art of Service-a globally recognised credential trusted by engineering teams at Google, SAP, and JPMorgan Chase. This certificate validates your ability to design, deploy, and govern real-time data pipelines using modern cloud-native patterns and is verifiable through secure digital badge integration. Simple, Transparent Pricing - No Hidden Fees
The course fee is straightforward and all-inclusive. There are no tiered pricing plans, no subscription traps, and no paywalls to advanced content. What you see is what you get-complete access to every module, template, and lab exercise. We accept all major payment methods, including Visa, Mastercard, and PayPal, with encrypted processing compliant with PCI DSS Level 1 standards. Zero-Risk Enrollment: Satisfied or Refunded Guarantee
We guarantee your satisfaction. If you engage with the course materials in good faith and do not achieve clarity on core pipeline design principles within the first module, submit your feedback and receive a full refund-no questions asked. This is our promise to eliminate all perceived risk in your learning investment. You’ll Receive Confirmation & Access Separately
After enrollment, you’ll receive an automated confirmation email. Once your credentials are finalised, your unique access details will be sent separately to ensure secure onboarding. This process allows us to maintain high data integrity and system reliability across our global learner network. This Works For You - Even If…
You’ve struggled with stream processing before. Or your past cloud projects stalled in testing. Or you’re transitioning from batch ETL and feel behind on event-driven thinking. This course is built for real practitioners-not idealised learners. Real engineers from diverse roles have succeeded: - Data Engineers have used it to transition into cloud-native roles with 30% higher compensation offers.
- Solution Architects have leveraged the frameworks to win internal funding for real-time analytics upgrades.
- DevOps Engineers have applied the CI/CD templates to automate pipeline deployments with 90% fewer rollbacks.
- Analysts with basic SQL have used the low-code ingestion labs to demonstrate business impact and earn cross-functional leadership seats.
This works even if you’ve never built a streaming pipeline before. The step-by-step scaffolding ensures you build confidence along with capability-module by module, decision by decision. You're gaining more than knowledge. You’re gaining a repeatable methodology, trusted by professionals, backed by global certification, and proven in production environments.
Module 1: Foundations of Real-Time Data Systems - Defining real-time vs near real-time vs batch processing
- Understanding event-driven architecture principles
- Key components of a modern data pipeline
- The role of data velocity, volume, and variety in pipeline design
- Common anti-patterns in legacy ETL systems
- Business drivers for real-time analytics adoption
- Latency tolerance thresholds by industry
- Overview of cloud-native data processing ecosystem
- Comparing monolithic vs distributed pipeline architectures
- Lifecycle stages of a data event from source to insight
Module 2: Cloud Platform Selection & Environment Setup - Evaluating AWS vs Azure vs GCP for streaming workloads
- Configuring project-level IAM and service accounts securely
- Setting up region and zone strategies for low-latency processing
- Resource naming conventions and tagging policies
- Organising cloud projects for cost transparency and compliance
- Estimating initial infrastructure costs using pricing calculators
- Enabling necessary APIs and services across providers
- Configuring VPCs and private service connect for data isolation
- Establishing monitoring and logging at the project level
- Setting up billing alerts and quota thresholds
Module 3: Data Ingestion Patterns & Tools - Pull vs push ingestion models
- Using AWS Kinesis Data Streams for high-throughput ingestion
- Configuring Azure Event Hubs with partitioning strategies
- Implementing Google Cloud Pub/Sub with dead-letter topics
- Designing idempotent ingestion endpoints
- Evaluating Apache Kafka vs managed offerings
- Building custom ingestion adapters with REST and gRPC
- Securing data in transit using TLS and mTLS
- Validating message schemas at the ingestion boundary
- Handling burst traffic with auto-scaling ingestion clusters
- Tuning buffer sizes and retention periods for cost-efficiency
- Monitoring ingestion health with custom metrics dashboards
- Automating failover between ingestion endpoints
- Implementing backpressure handling logic
- Testing ingestion resilience under network failure
Module 4: Stream Processing Fundamentals - Understanding windowing: tumbling, sliding, and session windows
- Processing time vs event time semantics
- Stateful vs stateless transformations
- Using AWS Kinesis Data Analytics with SQL
- Building Apache Flink jobs on Google Cloud Dataflow
- Deploying structured streaming applications on Azure Databricks
- Chaining multiple processing stages in a pipeline
- Applying filtering, aggregation, and enrichment operations
- Joining streaming and batch datasets efficiently
- Implementing watermarking for late-arriving data
- Managing state consistency and checkpointing
- Optimising parallelism and task distribution
- Reducing processing latency with edge computing strategies
- Debugging stream job failures using execution graphs
- Monitoring processing lag and memory pressure
Module 5: Schema Design & Data Modelling for Streams - Choosing between Avro, Protobuf, JSON, and Parquet for events
- Defining canonical event schemas with versioning
- Using schema registries on AWS, Azure, and GCP
- Handling schema evolution with backward compatibility
- Designing event hierarchies for domain-driven contexts
- Normalising vs denormalising event payloads
- Embedding metadata for lineage and governance
- Compressing event payloads without losing readability
- Validating schemas using JSON Schema and OpenAPI
- Enforcing schema conformance at processing entry points
- Generating code stubs from schema definitions
- Documenting event contracts for cross-team reuse
- Annotating schemas for compliance and PII tagging
- Testing schema migration scenarios with rollback paths
- Automating schema compliance in CI/CD pipelines
Module 6: Real-Time Storage & Data Lake Integration - Routing processed streams to data lakes efficiently
- Partitioning strategies for time-series data in S3 and ADLS
- Using Apache Iceberg for ACID transactions on object storage
- Integrating with Delta Lake for schema enforcement and versioning
- Designing ingestion zones: raw, curated, and trusted
- Optimising file sizes for query performance
- Compressing data using Snappy, Zstandard, and gzip
- Managing metadata with AWS Glue Data Catalog
- Implementing data expiry and retention policies
- Synchronising data across regions for disaster recovery
- Securing data at rest with customer-managed encryption keys
- Indexing frequently accessed event attributes
- Using materialised views for accelerated access
- Replicating subsets to analytics databases in real time
- Monitoring storage cost growth by pipeline and team
Module 7: Orchestration & Pipeline Automation - Choosing between Airflow, Prefect, and native cloud orchestrators
- Defining DAGs for hybrid batch-stream workflows
- Scheduling pipeline health checks and validation jobs
- Triggering downstream processes based on event patterns
- Using AWS Step Functions for serverless orchestration
- Implementing Azure Logic Apps for low-code workflows
- Configuring Google Cloud Workflows for complex branching
- Handling retries, timeouts, and circuit breaking
- Passing context and metadata across orchestration steps
- Automating pipeline rollbacks using versioned configurations
- Integrating with CI/CD systems like GitHub Actions and GitLab CI
- Enabling infrastructure as code using Terraform modules
- Validating pipeline deployments with preflight checks
- Automating documentation updates on pipeline changes
- Generating deployment audit trails for compliance
Module 8: Monitoring, Alerting & Observability - Defining SLIs and SLOs for real-time pipelines
- Instrumenting pipelines with custom metrics and logs
- Building unified dashboards using Grafana and cloud tools
- Setting up alerts for data lag, throughput drops, and errors
- Using distributed tracing for end-to-end latency analysis
- Correlating logs across ingestion, processing, and storage
- Implementing health probes for pipeline components
- Creating synthetic transactions to test pipeline vitality
- Automating alert suppression during maintenance windows
- Generating daily observability reports for stakeholders
- Using anomaly detection for proactive failure prediction
- Managing alert fatigue with escalation policies
- Exporting telemetry data for long-term trend analysis
- Configuring log retention and export policies
- Integrating with incident management tools like PagerDuty
Module 9: Fault Tolerance & Disaster Recovery - Designing pipelines for high availability
- Implementing multi-region deployment strategies
- Replicating state stores across availability zones
- Handling broker failures in message queues
- Recovering from consumer group rebalancing issues
- Checkpointing strategies for minimal data replay
- Setting up dead-letter queues for error isolation
- Automating reprocessing of failed events
- Designing idempotent sinks to prevent duplication
- Validating recovery procedures with chaos engineering
- Documenting runbooks for common failure scenarios
- Testing failover with simulated network partitions
- Using feature flags to isolate failing pipeline segments
- Establishing pipeline version rollback procedures
- Measuring RTO and RPO for critical data flows
Module 10: Security, Compliance & Governance - Implementing least privilege access for pipeline components
- Encrypting data in transit and at rest with managed keys
- Using VPC service controls to prevent data exfiltration
- Enabling audit logging for all pipeline activities
- Classifying data for PII, PHI, and PCI sensitivity
- Masking sensitive fields in logs and dashboards
- Integrating with enterprise identity providers (SAML, OIDC)
- Enforcing data usage policies with attribute-based access
- Generating compliance reports for SOC 2 and ISO 27001
- Implementing retention and deletion workflows for GDPR
- Conducting regular security configuration reviews
- Using confidential computing for sensitive processing
- Applying data lineage tracking across transformations
- Validating pipeline configurations against security benchmarks
- Automating compliance checks in deployment pipelines
Module 11: Cost Optimisation & Performance Tuning - Analysing cost drivers in real-time pipelines
- Tuning instance types for CPU and memory efficiency
- Using spot instances and preemptible VMs safely
- Right-sizing stream partitions and shards
- Reducing data transfer costs with protocol optimisation
- Implementing data sampling for testing environments
- Archiving cold data to lower-cost storage tiers
- Monitoring and controlling egress charges
- Using auto-scaling policies for demand fluctuations
- Leveraging serverless options to eliminate idle costs
- Analysing cost per million events processed
- Creating cost allocation tags by team and use case
- Optimising query patterns on downstream systems
- Eliminating redundant processing stages
- Generating monthly cost review reports
Module 12: Advanced Patterns & Use Case Implementations - Building CQRS patterns with event sourcing
- Implementing change data capture from databases
- Streaming IoT sensor data with MQTT integration
- Processing log data at scale with ingestion agents
- Real-time personalisation using customer event streams
- Fraud detection with anomaly scoring in motion
- Building real-time dashboards with Apache Superset
- Integrating with machine learning models via endpoints
- Streaming data to low-latency databases like Druid and ClickHouse
- Implementing geo-distributed pipelines for global apps
- Enabling real-time inventory updates for e-commerce
- Processing financial tick data with microsecond precision
- Streaming video metadata for content moderation
- Delivering real-time notifications using push gateways
- Orchestrating multi-tenant pipelines with isolation
Module 13: Pipeline Testing & Quality Assurance - Designing test strategies for streaming systems
- Creating synthetic event generators for load testing
- Testing schema validation and transformation logic
- Validating idempotency and exactly-once processing
- Simulating network failures and retries
- Measuring end-to-end latency under stress
- Using canary deployments for pipeline updates
- Implementing automated pipeline health checks
- Testing disaster recovery runbooks
- Validating security controls with penetration tests
- Testing compliance rule enforcement
- Generating test coverage reports for pipeline logic
- Integrating tests into CI/CD workflows
- Measuring quality metrics: uptime, accuracy, freshness
- Establishing QA sign-off gates for production
Module 14: Implementation Playbook & Production Rollout - Creating a pipeline implementation checklist
- Documenting architectural decision records (ADRs)
- Preparing a deployment change advisory package
- Conducting pre-launch load and resilience tests
- Engaging stakeholders with communication plans
- Scheduling low-risk rollout windows
- Implementing phased traffic routing (dark launches)
- Monitoring key indicators during go-live
- Handling production incidents with runbook execution
- Collecting feedback from downstream consumers
- Enabling pipeline observability for support teams
- Documenting lessons learned and iteration plans
- Setting up ongoing maintenance and review cycles
- Creating handover packages for operations
- Finalising operational SLAs and escalation paths
Module 15: Certification, Career Advancement & Next Steps - Preparing for the final certification assessment
- Reviewing key concepts and decision frameworks
- Submitting your real-time pipeline implementation project
- Receiving feedback from certified evaluators
- Earning your Certificate of Completion issued by The Art of Service
- Accessing digital badge credentials for LinkedIn and resumes
- Using the certification in salary negotiation and promotions
- Highlighting project experience in job applications
- Joining the alumni network of certified practitioners
- Accessing exclusive job boards and partner opportunities
- Continuing education pathways in ML engineering and MLOps
- Staying updated with monthly expert briefings
- Leveraging templates for future architecture proposals
- Building a personal portfolio of pipeline designs
- Presenting your work in technical leadership forums
- Defining real-time vs near real-time vs batch processing
- Understanding event-driven architecture principles
- Key components of a modern data pipeline
- The role of data velocity, volume, and variety in pipeline design
- Common anti-patterns in legacy ETL systems
- Business drivers for real-time analytics adoption
- Latency tolerance thresholds by industry
- Overview of cloud-native data processing ecosystem
- Comparing monolithic vs distributed pipeline architectures
- Lifecycle stages of a data event from source to insight
Module 2: Cloud Platform Selection & Environment Setup - Evaluating AWS vs Azure vs GCP for streaming workloads
- Configuring project-level IAM and service accounts securely
- Setting up region and zone strategies for low-latency processing
- Resource naming conventions and tagging policies
- Organising cloud projects for cost transparency and compliance
- Estimating initial infrastructure costs using pricing calculators
- Enabling necessary APIs and services across providers
- Configuring VPCs and private service connect for data isolation
- Establishing monitoring and logging at the project level
- Setting up billing alerts and quota thresholds
Module 3: Data Ingestion Patterns & Tools - Pull vs push ingestion models
- Using AWS Kinesis Data Streams for high-throughput ingestion
- Configuring Azure Event Hubs with partitioning strategies
- Implementing Google Cloud Pub/Sub with dead-letter topics
- Designing idempotent ingestion endpoints
- Evaluating Apache Kafka vs managed offerings
- Building custom ingestion adapters with REST and gRPC
- Securing data in transit using TLS and mTLS
- Validating message schemas at the ingestion boundary
- Handling burst traffic with auto-scaling ingestion clusters
- Tuning buffer sizes and retention periods for cost-efficiency
- Monitoring ingestion health with custom metrics dashboards
- Automating failover between ingestion endpoints
- Implementing backpressure handling logic
- Testing ingestion resilience under network failure
Module 4: Stream Processing Fundamentals - Understanding windowing: tumbling, sliding, and session windows
- Processing time vs event time semantics
- Stateful vs stateless transformations
- Using AWS Kinesis Data Analytics with SQL
- Building Apache Flink jobs on Google Cloud Dataflow
- Deploying structured streaming applications on Azure Databricks
- Chaining multiple processing stages in a pipeline
- Applying filtering, aggregation, and enrichment operations
- Joining streaming and batch datasets efficiently
- Implementing watermarking for late-arriving data
- Managing state consistency and checkpointing
- Optimising parallelism and task distribution
- Reducing processing latency with edge computing strategies
- Debugging stream job failures using execution graphs
- Monitoring processing lag and memory pressure
Module 5: Schema Design & Data Modelling for Streams - Choosing between Avro, Protobuf, JSON, and Parquet for events
- Defining canonical event schemas with versioning
- Using schema registries on AWS, Azure, and GCP
- Handling schema evolution with backward compatibility
- Designing event hierarchies for domain-driven contexts
- Normalising vs denormalising event payloads
- Embedding metadata for lineage and governance
- Compressing event payloads without losing readability
- Validating schemas using JSON Schema and OpenAPI
- Enforcing schema conformance at processing entry points
- Generating code stubs from schema definitions
- Documenting event contracts for cross-team reuse
- Annotating schemas for compliance and PII tagging
- Testing schema migration scenarios with rollback paths
- Automating schema compliance in CI/CD pipelines
Module 6: Real-Time Storage & Data Lake Integration - Routing processed streams to data lakes efficiently
- Partitioning strategies for time-series data in S3 and ADLS
- Using Apache Iceberg for ACID transactions on object storage
- Integrating with Delta Lake for schema enforcement and versioning
- Designing ingestion zones: raw, curated, and trusted
- Optimising file sizes for query performance
- Compressing data using Snappy, Zstandard, and gzip
- Managing metadata with AWS Glue Data Catalog
- Implementing data expiry and retention policies
- Synchronising data across regions for disaster recovery
- Securing data at rest with customer-managed encryption keys
- Indexing frequently accessed event attributes
- Using materialised views for accelerated access
- Replicating subsets to analytics databases in real time
- Monitoring storage cost growth by pipeline and team
Module 7: Orchestration & Pipeline Automation - Choosing between Airflow, Prefect, and native cloud orchestrators
- Defining DAGs for hybrid batch-stream workflows
- Scheduling pipeline health checks and validation jobs
- Triggering downstream processes based on event patterns
- Using AWS Step Functions for serverless orchestration
- Implementing Azure Logic Apps for low-code workflows
- Configuring Google Cloud Workflows for complex branching
- Handling retries, timeouts, and circuit breaking
- Passing context and metadata across orchestration steps
- Automating pipeline rollbacks using versioned configurations
- Integrating with CI/CD systems like GitHub Actions and GitLab CI
- Enabling infrastructure as code using Terraform modules
- Validating pipeline deployments with preflight checks
- Automating documentation updates on pipeline changes
- Generating deployment audit trails for compliance
Module 8: Monitoring, Alerting & Observability - Defining SLIs and SLOs for real-time pipelines
- Instrumenting pipelines with custom metrics and logs
- Building unified dashboards using Grafana and cloud tools
- Setting up alerts for data lag, throughput drops, and errors
- Using distributed tracing for end-to-end latency analysis
- Correlating logs across ingestion, processing, and storage
- Implementing health probes for pipeline components
- Creating synthetic transactions to test pipeline vitality
- Automating alert suppression during maintenance windows
- Generating daily observability reports for stakeholders
- Using anomaly detection for proactive failure prediction
- Managing alert fatigue with escalation policies
- Exporting telemetry data for long-term trend analysis
- Configuring log retention and export policies
- Integrating with incident management tools like PagerDuty
Module 9: Fault Tolerance & Disaster Recovery - Designing pipelines for high availability
- Implementing multi-region deployment strategies
- Replicating state stores across availability zones
- Handling broker failures in message queues
- Recovering from consumer group rebalancing issues
- Checkpointing strategies for minimal data replay
- Setting up dead-letter queues for error isolation
- Automating reprocessing of failed events
- Designing idempotent sinks to prevent duplication
- Validating recovery procedures with chaos engineering
- Documenting runbooks for common failure scenarios
- Testing failover with simulated network partitions
- Using feature flags to isolate failing pipeline segments
- Establishing pipeline version rollback procedures
- Measuring RTO and RPO for critical data flows
Module 10: Security, Compliance & Governance - Implementing least privilege access for pipeline components
- Encrypting data in transit and at rest with managed keys
- Using VPC service controls to prevent data exfiltration
- Enabling audit logging for all pipeline activities
- Classifying data for PII, PHI, and PCI sensitivity
- Masking sensitive fields in logs and dashboards
- Integrating with enterprise identity providers (SAML, OIDC)
- Enforcing data usage policies with attribute-based access
- Generating compliance reports for SOC 2 and ISO 27001
- Implementing retention and deletion workflows for GDPR
- Conducting regular security configuration reviews
- Using confidential computing for sensitive processing
- Applying data lineage tracking across transformations
- Validating pipeline configurations against security benchmarks
- Automating compliance checks in deployment pipelines
Module 11: Cost Optimisation & Performance Tuning - Analysing cost drivers in real-time pipelines
- Tuning instance types for CPU and memory efficiency
- Using spot instances and preemptible VMs safely
- Right-sizing stream partitions and shards
- Reducing data transfer costs with protocol optimisation
- Implementing data sampling for testing environments
- Archiving cold data to lower-cost storage tiers
- Monitoring and controlling egress charges
- Using auto-scaling policies for demand fluctuations
- Leveraging serverless options to eliminate idle costs
- Analysing cost per million events processed
- Creating cost allocation tags by team and use case
- Optimising query patterns on downstream systems
- Eliminating redundant processing stages
- Generating monthly cost review reports
Module 12: Advanced Patterns & Use Case Implementations - Building CQRS patterns with event sourcing
- Implementing change data capture from databases
- Streaming IoT sensor data with MQTT integration
- Processing log data at scale with ingestion agents
- Real-time personalisation using customer event streams
- Fraud detection with anomaly scoring in motion
- Building real-time dashboards with Apache Superset
- Integrating with machine learning models via endpoints
- Streaming data to low-latency databases like Druid and ClickHouse
- Implementing geo-distributed pipelines for global apps
- Enabling real-time inventory updates for e-commerce
- Processing financial tick data with microsecond precision
- Streaming video metadata for content moderation
- Delivering real-time notifications using push gateways
- Orchestrating multi-tenant pipelines with isolation
Module 13: Pipeline Testing & Quality Assurance - Designing test strategies for streaming systems
- Creating synthetic event generators for load testing
- Testing schema validation and transformation logic
- Validating idempotency and exactly-once processing
- Simulating network failures and retries
- Measuring end-to-end latency under stress
- Using canary deployments for pipeline updates
- Implementing automated pipeline health checks
- Testing disaster recovery runbooks
- Validating security controls with penetration tests
- Testing compliance rule enforcement
- Generating test coverage reports for pipeline logic
- Integrating tests into CI/CD workflows
- Measuring quality metrics: uptime, accuracy, freshness
- Establishing QA sign-off gates for production
Module 14: Implementation Playbook & Production Rollout - Creating a pipeline implementation checklist
- Documenting architectural decision records (ADRs)
- Preparing a deployment change advisory package
- Conducting pre-launch load and resilience tests
- Engaging stakeholders with communication plans
- Scheduling low-risk rollout windows
- Implementing phased traffic routing (dark launches)
- Monitoring key indicators during go-live
- Handling production incidents with runbook execution
- Collecting feedback from downstream consumers
- Enabling pipeline observability for support teams
- Documenting lessons learned and iteration plans
- Setting up ongoing maintenance and review cycles
- Creating handover packages for operations
- Finalising operational SLAs and escalation paths
Module 15: Certification, Career Advancement & Next Steps - Preparing for the final certification assessment
- Reviewing key concepts and decision frameworks
- Submitting your real-time pipeline implementation project
- Receiving feedback from certified evaluators
- Earning your Certificate of Completion issued by The Art of Service
- Accessing digital badge credentials for LinkedIn and resumes
- Using the certification in salary negotiation and promotions
- Highlighting project experience in job applications
- Joining the alumni network of certified practitioners
- Accessing exclusive job boards and partner opportunities
- Continuing education pathways in ML engineering and MLOps
- Staying updated with monthly expert briefings
- Leveraging templates for future architecture proposals
- Building a personal portfolio of pipeline designs
- Presenting your work in technical leadership forums
- Pull vs push ingestion models
- Using AWS Kinesis Data Streams for high-throughput ingestion
- Configuring Azure Event Hubs with partitioning strategies
- Implementing Google Cloud Pub/Sub with dead-letter topics
- Designing idempotent ingestion endpoints
- Evaluating Apache Kafka vs managed offerings
- Building custom ingestion adapters with REST and gRPC
- Securing data in transit using TLS and mTLS
- Validating message schemas at the ingestion boundary
- Handling burst traffic with auto-scaling ingestion clusters
- Tuning buffer sizes and retention periods for cost-efficiency
- Monitoring ingestion health with custom metrics dashboards
- Automating failover between ingestion endpoints
- Implementing backpressure handling logic
- Testing ingestion resilience under network failure
Module 4: Stream Processing Fundamentals - Understanding windowing: tumbling, sliding, and session windows
- Processing time vs event time semantics
- Stateful vs stateless transformations
- Using AWS Kinesis Data Analytics with SQL
- Building Apache Flink jobs on Google Cloud Dataflow
- Deploying structured streaming applications on Azure Databricks
- Chaining multiple processing stages in a pipeline
- Applying filtering, aggregation, and enrichment operations
- Joining streaming and batch datasets efficiently
- Implementing watermarking for late-arriving data
- Managing state consistency and checkpointing
- Optimising parallelism and task distribution
- Reducing processing latency with edge computing strategies
- Debugging stream job failures using execution graphs
- Monitoring processing lag and memory pressure
Module 5: Schema Design & Data Modelling for Streams - Choosing between Avro, Protobuf, JSON, and Parquet for events
- Defining canonical event schemas with versioning
- Using schema registries on AWS, Azure, and GCP
- Handling schema evolution with backward compatibility
- Designing event hierarchies for domain-driven contexts
- Normalising vs denormalising event payloads
- Embedding metadata for lineage and governance
- Compressing event payloads without losing readability
- Validating schemas using JSON Schema and OpenAPI
- Enforcing schema conformance at processing entry points
- Generating code stubs from schema definitions
- Documenting event contracts for cross-team reuse
- Annotating schemas for compliance and PII tagging
- Testing schema migration scenarios with rollback paths
- Automating schema compliance in CI/CD pipelines
Module 6: Real-Time Storage & Data Lake Integration - Routing processed streams to data lakes efficiently
- Partitioning strategies for time-series data in S3 and ADLS
- Using Apache Iceberg for ACID transactions on object storage
- Integrating with Delta Lake for schema enforcement and versioning
- Designing ingestion zones: raw, curated, and trusted
- Optimising file sizes for query performance
- Compressing data using Snappy, Zstandard, and gzip
- Managing metadata with AWS Glue Data Catalog
- Implementing data expiry and retention policies
- Synchronising data across regions for disaster recovery
- Securing data at rest with customer-managed encryption keys
- Indexing frequently accessed event attributes
- Using materialised views for accelerated access
- Replicating subsets to analytics databases in real time
- Monitoring storage cost growth by pipeline and team
Module 7: Orchestration & Pipeline Automation - Choosing between Airflow, Prefect, and native cloud orchestrators
- Defining DAGs for hybrid batch-stream workflows
- Scheduling pipeline health checks and validation jobs
- Triggering downstream processes based on event patterns
- Using AWS Step Functions for serverless orchestration
- Implementing Azure Logic Apps for low-code workflows
- Configuring Google Cloud Workflows for complex branching
- Handling retries, timeouts, and circuit breaking
- Passing context and metadata across orchestration steps
- Automating pipeline rollbacks using versioned configurations
- Integrating with CI/CD systems like GitHub Actions and GitLab CI
- Enabling infrastructure as code using Terraform modules
- Validating pipeline deployments with preflight checks
- Automating documentation updates on pipeline changes
- Generating deployment audit trails for compliance
Module 8: Monitoring, Alerting & Observability - Defining SLIs and SLOs for real-time pipelines
- Instrumenting pipelines with custom metrics and logs
- Building unified dashboards using Grafana and cloud tools
- Setting up alerts for data lag, throughput drops, and errors
- Using distributed tracing for end-to-end latency analysis
- Correlating logs across ingestion, processing, and storage
- Implementing health probes for pipeline components
- Creating synthetic transactions to test pipeline vitality
- Automating alert suppression during maintenance windows
- Generating daily observability reports for stakeholders
- Using anomaly detection for proactive failure prediction
- Managing alert fatigue with escalation policies
- Exporting telemetry data for long-term trend analysis
- Configuring log retention and export policies
- Integrating with incident management tools like PagerDuty
Module 9: Fault Tolerance & Disaster Recovery - Designing pipelines for high availability
- Implementing multi-region deployment strategies
- Replicating state stores across availability zones
- Handling broker failures in message queues
- Recovering from consumer group rebalancing issues
- Checkpointing strategies for minimal data replay
- Setting up dead-letter queues for error isolation
- Automating reprocessing of failed events
- Designing idempotent sinks to prevent duplication
- Validating recovery procedures with chaos engineering
- Documenting runbooks for common failure scenarios
- Testing failover with simulated network partitions
- Using feature flags to isolate failing pipeline segments
- Establishing pipeline version rollback procedures
- Measuring RTO and RPO for critical data flows
Module 10: Security, Compliance & Governance - Implementing least privilege access for pipeline components
- Encrypting data in transit and at rest with managed keys
- Using VPC service controls to prevent data exfiltration
- Enabling audit logging for all pipeline activities
- Classifying data for PII, PHI, and PCI sensitivity
- Masking sensitive fields in logs and dashboards
- Integrating with enterprise identity providers (SAML, OIDC)
- Enforcing data usage policies with attribute-based access
- Generating compliance reports for SOC 2 and ISO 27001
- Implementing retention and deletion workflows for GDPR
- Conducting regular security configuration reviews
- Using confidential computing for sensitive processing
- Applying data lineage tracking across transformations
- Validating pipeline configurations against security benchmarks
- Automating compliance checks in deployment pipelines
Module 11: Cost Optimisation & Performance Tuning - Analysing cost drivers in real-time pipelines
- Tuning instance types for CPU and memory efficiency
- Using spot instances and preemptible VMs safely
- Right-sizing stream partitions and shards
- Reducing data transfer costs with protocol optimisation
- Implementing data sampling for testing environments
- Archiving cold data to lower-cost storage tiers
- Monitoring and controlling egress charges
- Using auto-scaling policies for demand fluctuations
- Leveraging serverless options to eliminate idle costs
- Analysing cost per million events processed
- Creating cost allocation tags by team and use case
- Optimising query patterns on downstream systems
- Eliminating redundant processing stages
- Generating monthly cost review reports
Module 12: Advanced Patterns & Use Case Implementations - Building CQRS patterns with event sourcing
- Implementing change data capture from databases
- Streaming IoT sensor data with MQTT integration
- Processing log data at scale with ingestion agents
- Real-time personalisation using customer event streams
- Fraud detection with anomaly scoring in motion
- Building real-time dashboards with Apache Superset
- Integrating with machine learning models via endpoints
- Streaming data to low-latency databases like Druid and ClickHouse
- Implementing geo-distributed pipelines for global apps
- Enabling real-time inventory updates for e-commerce
- Processing financial tick data with microsecond precision
- Streaming video metadata for content moderation
- Delivering real-time notifications using push gateways
- Orchestrating multi-tenant pipelines with isolation
Module 13: Pipeline Testing & Quality Assurance - Designing test strategies for streaming systems
- Creating synthetic event generators for load testing
- Testing schema validation and transformation logic
- Validating idempotency and exactly-once processing
- Simulating network failures and retries
- Measuring end-to-end latency under stress
- Using canary deployments for pipeline updates
- Implementing automated pipeline health checks
- Testing disaster recovery runbooks
- Validating security controls with penetration tests
- Testing compliance rule enforcement
- Generating test coverage reports for pipeline logic
- Integrating tests into CI/CD workflows
- Measuring quality metrics: uptime, accuracy, freshness
- Establishing QA sign-off gates for production
Module 14: Implementation Playbook & Production Rollout - Creating a pipeline implementation checklist
- Documenting architectural decision records (ADRs)
- Preparing a deployment change advisory package
- Conducting pre-launch load and resilience tests
- Engaging stakeholders with communication plans
- Scheduling low-risk rollout windows
- Implementing phased traffic routing (dark launches)
- Monitoring key indicators during go-live
- Handling production incidents with runbook execution
- Collecting feedback from downstream consumers
- Enabling pipeline observability for support teams
- Documenting lessons learned and iteration plans
- Setting up ongoing maintenance and review cycles
- Creating handover packages for operations
- Finalising operational SLAs and escalation paths
Module 15: Certification, Career Advancement & Next Steps - Preparing for the final certification assessment
- Reviewing key concepts and decision frameworks
- Submitting your real-time pipeline implementation project
- Receiving feedback from certified evaluators
- Earning your Certificate of Completion issued by The Art of Service
- Accessing digital badge credentials for LinkedIn and resumes
- Using the certification in salary negotiation and promotions
- Highlighting project experience in job applications
- Joining the alumni network of certified practitioners
- Accessing exclusive job boards and partner opportunities
- Continuing education pathways in ML engineering and MLOps
- Staying updated with monthly expert briefings
- Leveraging templates for future architecture proposals
- Building a personal portfolio of pipeline designs
- Presenting your work in technical leadership forums
- Choosing between Avro, Protobuf, JSON, and Parquet for events
- Defining canonical event schemas with versioning
- Using schema registries on AWS, Azure, and GCP
- Handling schema evolution with backward compatibility
- Designing event hierarchies for domain-driven contexts
- Normalising vs denormalising event payloads
- Embedding metadata for lineage and governance
- Compressing event payloads without losing readability
- Validating schemas using JSON Schema and OpenAPI
- Enforcing schema conformance at processing entry points
- Generating code stubs from schema definitions
- Documenting event contracts for cross-team reuse
- Annotating schemas for compliance and PII tagging
- Testing schema migration scenarios with rollback paths
- Automating schema compliance in CI/CD pipelines
Module 6: Real-Time Storage & Data Lake Integration - Routing processed streams to data lakes efficiently
- Partitioning strategies for time-series data in S3 and ADLS
- Using Apache Iceberg for ACID transactions on object storage
- Integrating with Delta Lake for schema enforcement and versioning
- Designing ingestion zones: raw, curated, and trusted
- Optimising file sizes for query performance
- Compressing data using Snappy, Zstandard, and gzip
- Managing metadata with AWS Glue Data Catalog
- Implementing data expiry and retention policies
- Synchronising data across regions for disaster recovery
- Securing data at rest with customer-managed encryption keys
- Indexing frequently accessed event attributes
- Using materialised views for accelerated access
- Replicating subsets to analytics databases in real time
- Monitoring storage cost growth by pipeline and team
Module 7: Orchestration & Pipeline Automation - Choosing between Airflow, Prefect, and native cloud orchestrators
- Defining DAGs for hybrid batch-stream workflows
- Scheduling pipeline health checks and validation jobs
- Triggering downstream processes based on event patterns
- Using AWS Step Functions for serverless orchestration
- Implementing Azure Logic Apps for low-code workflows
- Configuring Google Cloud Workflows for complex branching
- Handling retries, timeouts, and circuit breaking
- Passing context and metadata across orchestration steps
- Automating pipeline rollbacks using versioned configurations
- Integrating with CI/CD systems like GitHub Actions and GitLab CI
- Enabling infrastructure as code using Terraform modules
- Validating pipeline deployments with preflight checks
- Automating documentation updates on pipeline changes
- Generating deployment audit trails for compliance
Module 8: Monitoring, Alerting & Observability - Defining SLIs and SLOs for real-time pipelines
- Instrumenting pipelines with custom metrics and logs
- Building unified dashboards using Grafana and cloud tools
- Setting up alerts for data lag, throughput drops, and errors
- Using distributed tracing for end-to-end latency analysis
- Correlating logs across ingestion, processing, and storage
- Implementing health probes for pipeline components
- Creating synthetic transactions to test pipeline vitality
- Automating alert suppression during maintenance windows
- Generating daily observability reports for stakeholders
- Using anomaly detection for proactive failure prediction
- Managing alert fatigue with escalation policies
- Exporting telemetry data for long-term trend analysis
- Configuring log retention and export policies
- Integrating with incident management tools like PagerDuty
Module 9: Fault Tolerance & Disaster Recovery - Designing pipelines for high availability
- Implementing multi-region deployment strategies
- Replicating state stores across availability zones
- Handling broker failures in message queues
- Recovering from consumer group rebalancing issues
- Checkpointing strategies for minimal data replay
- Setting up dead-letter queues for error isolation
- Automating reprocessing of failed events
- Designing idempotent sinks to prevent duplication
- Validating recovery procedures with chaos engineering
- Documenting runbooks for common failure scenarios
- Testing failover with simulated network partitions
- Using feature flags to isolate failing pipeline segments
- Establishing pipeline version rollback procedures
- Measuring RTO and RPO for critical data flows
Module 10: Security, Compliance & Governance - Implementing least privilege access for pipeline components
- Encrypting data in transit and at rest with managed keys
- Using VPC service controls to prevent data exfiltration
- Enabling audit logging for all pipeline activities
- Classifying data for PII, PHI, and PCI sensitivity
- Masking sensitive fields in logs and dashboards
- Integrating with enterprise identity providers (SAML, OIDC)
- Enforcing data usage policies with attribute-based access
- Generating compliance reports for SOC 2 and ISO 27001
- Implementing retention and deletion workflows for GDPR
- Conducting regular security configuration reviews
- Using confidential computing for sensitive processing
- Applying data lineage tracking across transformations
- Validating pipeline configurations against security benchmarks
- Automating compliance checks in deployment pipelines
Module 11: Cost Optimisation & Performance Tuning - Analysing cost drivers in real-time pipelines
- Tuning instance types for CPU and memory efficiency
- Using spot instances and preemptible VMs safely
- Right-sizing stream partitions and shards
- Reducing data transfer costs with protocol optimisation
- Implementing data sampling for testing environments
- Archiving cold data to lower-cost storage tiers
- Monitoring and controlling egress charges
- Using auto-scaling policies for demand fluctuations
- Leveraging serverless options to eliminate idle costs
- Analysing cost per million events processed
- Creating cost allocation tags by team and use case
- Optimising query patterns on downstream systems
- Eliminating redundant processing stages
- Generating monthly cost review reports
Module 12: Advanced Patterns & Use Case Implementations - Building CQRS patterns with event sourcing
- Implementing change data capture from databases
- Streaming IoT sensor data with MQTT integration
- Processing log data at scale with ingestion agents
- Real-time personalisation using customer event streams
- Fraud detection with anomaly scoring in motion
- Building real-time dashboards with Apache Superset
- Integrating with machine learning models via endpoints
- Streaming data to low-latency databases like Druid and ClickHouse
- Implementing geo-distributed pipelines for global apps
- Enabling real-time inventory updates for e-commerce
- Processing financial tick data with microsecond precision
- Streaming video metadata for content moderation
- Delivering real-time notifications using push gateways
- Orchestrating multi-tenant pipelines with isolation
Module 13: Pipeline Testing & Quality Assurance - Designing test strategies for streaming systems
- Creating synthetic event generators for load testing
- Testing schema validation and transformation logic
- Validating idempotency and exactly-once processing
- Simulating network failures and retries
- Measuring end-to-end latency under stress
- Using canary deployments for pipeline updates
- Implementing automated pipeline health checks
- Testing disaster recovery runbooks
- Validating security controls with penetration tests
- Testing compliance rule enforcement
- Generating test coverage reports for pipeline logic
- Integrating tests into CI/CD workflows
- Measuring quality metrics: uptime, accuracy, freshness
- Establishing QA sign-off gates for production
Module 14: Implementation Playbook & Production Rollout - Creating a pipeline implementation checklist
- Documenting architectural decision records (ADRs)
- Preparing a deployment change advisory package
- Conducting pre-launch load and resilience tests
- Engaging stakeholders with communication plans
- Scheduling low-risk rollout windows
- Implementing phased traffic routing (dark launches)
- Monitoring key indicators during go-live
- Handling production incidents with runbook execution
- Collecting feedback from downstream consumers
- Enabling pipeline observability for support teams
- Documenting lessons learned and iteration plans
- Setting up ongoing maintenance and review cycles
- Creating handover packages for operations
- Finalising operational SLAs and escalation paths
Module 15: Certification, Career Advancement & Next Steps - Preparing for the final certification assessment
- Reviewing key concepts and decision frameworks
- Submitting your real-time pipeline implementation project
- Receiving feedback from certified evaluators
- Earning your Certificate of Completion issued by The Art of Service
- Accessing digital badge credentials for LinkedIn and resumes
- Using the certification in salary negotiation and promotions
- Highlighting project experience in job applications
- Joining the alumni network of certified practitioners
- Accessing exclusive job boards and partner opportunities
- Continuing education pathways in ML engineering and MLOps
- Staying updated with monthly expert briefings
- Leveraging templates for future architecture proposals
- Building a personal portfolio of pipeline designs
- Presenting your work in technical leadership forums
- Choosing between Airflow, Prefect, and native cloud orchestrators
- Defining DAGs for hybrid batch-stream workflows
- Scheduling pipeline health checks and validation jobs
- Triggering downstream processes based on event patterns
- Using AWS Step Functions for serverless orchestration
- Implementing Azure Logic Apps for low-code workflows
- Configuring Google Cloud Workflows for complex branching
- Handling retries, timeouts, and circuit breaking
- Passing context and metadata across orchestration steps
- Automating pipeline rollbacks using versioned configurations
- Integrating with CI/CD systems like GitHub Actions and GitLab CI
- Enabling infrastructure as code using Terraform modules
- Validating pipeline deployments with preflight checks
- Automating documentation updates on pipeline changes
- Generating deployment audit trails for compliance
Module 8: Monitoring, Alerting & Observability - Defining SLIs and SLOs for real-time pipelines
- Instrumenting pipelines with custom metrics and logs
- Building unified dashboards using Grafana and cloud tools
- Setting up alerts for data lag, throughput drops, and errors
- Using distributed tracing for end-to-end latency analysis
- Correlating logs across ingestion, processing, and storage
- Implementing health probes for pipeline components
- Creating synthetic transactions to test pipeline vitality
- Automating alert suppression during maintenance windows
- Generating daily observability reports for stakeholders
- Using anomaly detection for proactive failure prediction
- Managing alert fatigue with escalation policies
- Exporting telemetry data for long-term trend analysis
- Configuring log retention and export policies
- Integrating with incident management tools like PagerDuty
Module 9: Fault Tolerance & Disaster Recovery - Designing pipelines for high availability
- Implementing multi-region deployment strategies
- Replicating state stores across availability zones
- Handling broker failures in message queues
- Recovering from consumer group rebalancing issues
- Checkpointing strategies for minimal data replay
- Setting up dead-letter queues for error isolation
- Automating reprocessing of failed events
- Designing idempotent sinks to prevent duplication
- Validating recovery procedures with chaos engineering
- Documenting runbooks for common failure scenarios
- Testing failover with simulated network partitions
- Using feature flags to isolate failing pipeline segments
- Establishing pipeline version rollback procedures
- Measuring RTO and RPO for critical data flows
Module 10: Security, Compliance & Governance - Implementing least privilege access for pipeline components
- Encrypting data in transit and at rest with managed keys
- Using VPC service controls to prevent data exfiltration
- Enabling audit logging for all pipeline activities
- Classifying data for PII, PHI, and PCI sensitivity
- Masking sensitive fields in logs and dashboards
- Integrating with enterprise identity providers (SAML, OIDC)
- Enforcing data usage policies with attribute-based access
- Generating compliance reports for SOC 2 and ISO 27001
- Implementing retention and deletion workflows for GDPR
- Conducting regular security configuration reviews
- Using confidential computing for sensitive processing
- Applying data lineage tracking across transformations
- Validating pipeline configurations against security benchmarks
- Automating compliance checks in deployment pipelines
Module 11: Cost Optimisation & Performance Tuning - Analysing cost drivers in real-time pipelines
- Tuning instance types for CPU and memory efficiency
- Using spot instances and preemptible VMs safely
- Right-sizing stream partitions and shards
- Reducing data transfer costs with protocol optimisation
- Implementing data sampling for testing environments
- Archiving cold data to lower-cost storage tiers
- Monitoring and controlling egress charges
- Using auto-scaling policies for demand fluctuations
- Leveraging serverless options to eliminate idle costs
- Analysing cost per million events processed
- Creating cost allocation tags by team and use case
- Optimising query patterns on downstream systems
- Eliminating redundant processing stages
- Generating monthly cost review reports
Module 12: Advanced Patterns & Use Case Implementations - Building CQRS patterns with event sourcing
- Implementing change data capture from databases
- Streaming IoT sensor data with MQTT integration
- Processing log data at scale with ingestion agents
- Real-time personalisation using customer event streams
- Fraud detection with anomaly scoring in motion
- Building real-time dashboards with Apache Superset
- Integrating with machine learning models via endpoints
- Streaming data to low-latency databases like Druid and ClickHouse
- Implementing geo-distributed pipelines for global apps
- Enabling real-time inventory updates for e-commerce
- Processing financial tick data with microsecond precision
- Streaming video metadata for content moderation
- Delivering real-time notifications using push gateways
- Orchestrating multi-tenant pipelines with isolation
Module 13: Pipeline Testing & Quality Assurance - Designing test strategies for streaming systems
- Creating synthetic event generators for load testing
- Testing schema validation and transformation logic
- Validating idempotency and exactly-once processing
- Simulating network failures and retries
- Measuring end-to-end latency under stress
- Using canary deployments for pipeline updates
- Implementing automated pipeline health checks
- Testing disaster recovery runbooks
- Validating security controls with penetration tests
- Testing compliance rule enforcement
- Generating test coverage reports for pipeline logic
- Integrating tests into CI/CD workflows
- Measuring quality metrics: uptime, accuracy, freshness
- Establishing QA sign-off gates for production
Module 14: Implementation Playbook & Production Rollout - Creating a pipeline implementation checklist
- Documenting architectural decision records (ADRs)
- Preparing a deployment change advisory package
- Conducting pre-launch load and resilience tests
- Engaging stakeholders with communication plans
- Scheduling low-risk rollout windows
- Implementing phased traffic routing (dark launches)
- Monitoring key indicators during go-live
- Handling production incidents with runbook execution
- Collecting feedback from downstream consumers
- Enabling pipeline observability for support teams
- Documenting lessons learned and iteration plans
- Setting up ongoing maintenance and review cycles
- Creating handover packages for operations
- Finalising operational SLAs and escalation paths
Module 15: Certification, Career Advancement & Next Steps - Preparing for the final certification assessment
- Reviewing key concepts and decision frameworks
- Submitting your real-time pipeline implementation project
- Receiving feedback from certified evaluators
- Earning your Certificate of Completion issued by The Art of Service
- Accessing digital badge credentials for LinkedIn and resumes
- Using the certification in salary negotiation and promotions
- Highlighting project experience in job applications
- Joining the alumni network of certified practitioners
- Accessing exclusive job boards and partner opportunities
- Continuing education pathways in ML engineering and MLOps
- Staying updated with monthly expert briefings
- Leveraging templates for future architecture proposals
- Building a personal portfolio of pipeline designs
- Presenting your work in technical leadership forums
- Designing pipelines for high availability
- Implementing multi-region deployment strategies
- Replicating state stores across availability zones
- Handling broker failures in message queues
- Recovering from consumer group rebalancing issues
- Checkpointing strategies for minimal data replay
- Setting up dead-letter queues for error isolation
- Automating reprocessing of failed events
- Designing idempotent sinks to prevent duplication
- Validating recovery procedures with chaos engineering
- Documenting runbooks for common failure scenarios
- Testing failover with simulated network partitions
- Using feature flags to isolate failing pipeline segments
- Establishing pipeline version rollback procedures
- Measuring RTO and RPO for critical data flows
Module 10: Security, Compliance & Governance - Implementing least privilege access for pipeline components
- Encrypting data in transit and at rest with managed keys
- Using VPC service controls to prevent data exfiltration
- Enabling audit logging for all pipeline activities
- Classifying data for PII, PHI, and PCI sensitivity
- Masking sensitive fields in logs and dashboards
- Integrating with enterprise identity providers (SAML, OIDC)
- Enforcing data usage policies with attribute-based access
- Generating compliance reports for SOC 2 and ISO 27001
- Implementing retention and deletion workflows for GDPR
- Conducting regular security configuration reviews
- Using confidential computing for sensitive processing
- Applying data lineage tracking across transformations
- Validating pipeline configurations against security benchmarks
- Automating compliance checks in deployment pipelines
Module 11: Cost Optimisation & Performance Tuning - Analysing cost drivers in real-time pipelines
- Tuning instance types for CPU and memory efficiency
- Using spot instances and preemptible VMs safely
- Right-sizing stream partitions and shards
- Reducing data transfer costs with protocol optimisation
- Implementing data sampling for testing environments
- Archiving cold data to lower-cost storage tiers
- Monitoring and controlling egress charges
- Using auto-scaling policies for demand fluctuations
- Leveraging serverless options to eliminate idle costs
- Analysing cost per million events processed
- Creating cost allocation tags by team and use case
- Optimising query patterns on downstream systems
- Eliminating redundant processing stages
- Generating monthly cost review reports
Module 12: Advanced Patterns & Use Case Implementations - Building CQRS patterns with event sourcing
- Implementing change data capture from databases
- Streaming IoT sensor data with MQTT integration
- Processing log data at scale with ingestion agents
- Real-time personalisation using customer event streams
- Fraud detection with anomaly scoring in motion
- Building real-time dashboards with Apache Superset
- Integrating with machine learning models via endpoints
- Streaming data to low-latency databases like Druid and ClickHouse
- Implementing geo-distributed pipelines for global apps
- Enabling real-time inventory updates for e-commerce
- Processing financial tick data with microsecond precision
- Streaming video metadata for content moderation
- Delivering real-time notifications using push gateways
- Orchestrating multi-tenant pipelines with isolation
Module 13: Pipeline Testing & Quality Assurance - Designing test strategies for streaming systems
- Creating synthetic event generators for load testing
- Testing schema validation and transformation logic
- Validating idempotency and exactly-once processing
- Simulating network failures and retries
- Measuring end-to-end latency under stress
- Using canary deployments for pipeline updates
- Implementing automated pipeline health checks
- Testing disaster recovery runbooks
- Validating security controls with penetration tests
- Testing compliance rule enforcement
- Generating test coverage reports for pipeline logic
- Integrating tests into CI/CD workflows
- Measuring quality metrics: uptime, accuracy, freshness
- Establishing QA sign-off gates for production
Module 14: Implementation Playbook & Production Rollout - Creating a pipeline implementation checklist
- Documenting architectural decision records (ADRs)
- Preparing a deployment change advisory package
- Conducting pre-launch load and resilience tests
- Engaging stakeholders with communication plans
- Scheduling low-risk rollout windows
- Implementing phased traffic routing (dark launches)
- Monitoring key indicators during go-live
- Handling production incidents with runbook execution
- Collecting feedback from downstream consumers
- Enabling pipeline observability for support teams
- Documenting lessons learned and iteration plans
- Setting up ongoing maintenance and review cycles
- Creating handover packages for operations
- Finalising operational SLAs and escalation paths
Module 15: Certification, Career Advancement & Next Steps - Preparing for the final certification assessment
- Reviewing key concepts and decision frameworks
- Submitting your real-time pipeline implementation project
- Receiving feedback from certified evaluators
- Earning your Certificate of Completion issued by The Art of Service
- Accessing digital badge credentials for LinkedIn and resumes
- Using the certification in salary negotiation and promotions
- Highlighting project experience in job applications
- Joining the alumni network of certified practitioners
- Accessing exclusive job boards and partner opportunities
- Continuing education pathways in ML engineering and MLOps
- Staying updated with monthly expert briefings
- Leveraging templates for future architecture proposals
- Building a personal portfolio of pipeline designs
- Presenting your work in technical leadership forums
- Analysing cost drivers in real-time pipelines
- Tuning instance types for CPU and memory efficiency
- Using spot instances and preemptible VMs safely
- Right-sizing stream partitions and shards
- Reducing data transfer costs with protocol optimisation
- Implementing data sampling for testing environments
- Archiving cold data to lower-cost storage tiers
- Monitoring and controlling egress charges
- Using auto-scaling policies for demand fluctuations
- Leveraging serverless options to eliminate idle costs
- Analysing cost per million events processed
- Creating cost allocation tags by team and use case
- Optimising query patterns on downstream systems
- Eliminating redundant processing stages
- Generating monthly cost review reports
Module 12: Advanced Patterns & Use Case Implementations - Building CQRS patterns with event sourcing
- Implementing change data capture from databases
- Streaming IoT sensor data with MQTT integration
- Processing log data at scale with ingestion agents
- Real-time personalisation using customer event streams
- Fraud detection with anomaly scoring in motion
- Building real-time dashboards with Apache Superset
- Integrating with machine learning models via endpoints
- Streaming data to low-latency databases like Druid and ClickHouse
- Implementing geo-distributed pipelines for global apps
- Enabling real-time inventory updates for e-commerce
- Processing financial tick data with microsecond precision
- Streaming video metadata for content moderation
- Delivering real-time notifications using push gateways
- Orchestrating multi-tenant pipelines with isolation
Module 13: Pipeline Testing & Quality Assurance - Designing test strategies for streaming systems
- Creating synthetic event generators for load testing
- Testing schema validation and transformation logic
- Validating idempotency and exactly-once processing
- Simulating network failures and retries
- Measuring end-to-end latency under stress
- Using canary deployments for pipeline updates
- Implementing automated pipeline health checks
- Testing disaster recovery runbooks
- Validating security controls with penetration tests
- Testing compliance rule enforcement
- Generating test coverage reports for pipeline logic
- Integrating tests into CI/CD workflows
- Measuring quality metrics: uptime, accuracy, freshness
- Establishing QA sign-off gates for production
Module 14: Implementation Playbook & Production Rollout - Creating a pipeline implementation checklist
- Documenting architectural decision records (ADRs)
- Preparing a deployment change advisory package
- Conducting pre-launch load and resilience tests
- Engaging stakeholders with communication plans
- Scheduling low-risk rollout windows
- Implementing phased traffic routing (dark launches)
- Monitoring key indicators during go-live
- Handling production incidents with runbook execution
- Collecting feedback from downstream consumers
- Enabling pipeline observability for support teams
- Documenting lessons learned and iteration plans
- Setting up ongoing maintenance and review cycles
- Creating handover packages for operations
- Finalising operational SLAs and escalation paths
Module 15: Certification, Career Advancement & Next Steps - Preparing for the final certification assessment
- Reviewing key concepts and decision frameworks
- Submitting your real-time pipeline implementation project
- Receiving feedback from certified evaluators
- Earning your Certificate of Completion issued by The Art of Service
- Accessing digital badge credentials for LinkedIn and resumes
- Using the certification in salary negotiation and promotions
- Highlighting project experience in job applications
- Joining the alumni network of certified practitioners
- Accessing exclusive job boards and partner opportunities
- Continuing education pathways in ML engineering and MLOps
- Staying updated with monthly expert briefings
- Leveraging templates for future architecture proposals
- Building a personal portfolio of pipeline designs
- Presenting your work in technical leadership forums
- Designing test strategies for streaming systems
- Creating synthetic event generators for load testing
- Testing schema validation and transformation logic
- Validating idempotency and exactly-once processing
- Simulating network failures and retries
- Measuring end-to-end latency under stress
- Using canary deployments for pipeline updates
- Implementing automated pipeline health checks
- Testing disaster recovery runbooks
- Validating security controls with penetration tests
- Testing compliance rule enforcement
- Generating test coverage reports for pipeline logic
- Integrating tests into CI/CD workflows
- Measuring quality metrics: uptime, accuracy, freshness
- Establishing QA sign-off gates for production
Module 14: Implementation Playbook & Production Rollout - Creating a pipeline implementation checklist
- Documenting architectural decision records (ADRs)
- Preparing a deployment change advisory package
- Conducting pre-launch load and resilience tests
- Engaging stakeholders with communication plans
- Scheduling low-risk rollout windows
- Implementing phased traffic routing (dark launches)
- Monitoring key indicators during go-live
- Handling production incidents with runbook execution
- Collecting feedback from downstream consumers
- Enabling pipeline observability for support teams
- Documenting lessons learned and iteration plans
- Setting up ongoing maintenance and review cycles
- Creating handover packages for operations
- Finalising operational SLAs and escalation paths
Module 15: Certification, Career Advancement & Next Steps - Preparing for the final certification assessment
- Reviewing key concepts and decision frameworks
- Submitting your real-time pipeline implementation project
- Receiving feedback from certified evaluators
- Earning your Certificate of Completion issued by The Art of Service
- Accessing digital badge credentials for LinkedIn and resumes
- Using the certification in salary negotiation and promotions
- Highlighting project experience in job applications
- Joining the alumni network of certified practitioners
- Accessing exclusive job boards and partner opportunities
- Continuing education pathways in ML engineering and MLOps
- Staying updated with monthly expert briefings
- Leveraging templates for future architecture proposals
- Building a personal portfolio of pipeline designs
- Presenting your work in technical leadership forums
- Preparing for the final certification assessment
- Reviewing key concepts and decision frameworks
- Submitting your real-time pipeline implementation project
- Receiving feedback from certified evaluators
- Earning your Certificate of Completion issued by The Art of Service
- Accessing digital badge credentials for LinkedIn and resumes
- Using the certification in salary negotiation and promotions
- Highlighting project experience in job applications
- Joining the alumni network of certified practitioners
- Accessing exclusive job boards and partner opportunities
- Continuing education pathways in ML engineering and MLOps
- Staying updated with monthly expert briefings
- Leveraging templates for future architecture proposals
- Building a personal portfolio of pipeline designs
- Presenting your work in technical leadership forums