Design Patterns for Scalable Data Engineering
You’re not just another data engineer. You’re the one they rely on when pipelines break, when latency spikes, and when leadership demands faster insights. But lately, the pressure is rising. Systems are growing, stakeholders expect more, and the legacy code that once worked now creaks under load. You’re expected to scale fast, design flawlessly, and deliver tomorrow what should have been ready yesterday. It’s not your skills that are the problem. It’s the framework. Without proven design patterns, even the best engineers build brittle systems. You’ve seen projects delayed, reworked, or scrapped because of architectural debt. You’ve lost nights to scaling fires you didn’t anticipate. And worst of all? You’re not getting credit-because your designs aren’t being recognised as enterprise-grade. Design Patterns for Scalable Data Engineering is your blueprint for breaking free. This is not theory. It’s the exact system used by senior architects at top-tier tech firms to design resilient, future-proof data infrastructures from day one. You’ll go from firefighting reactive pipelines to architecting scalable systems that earn trust, funding, and visibility. The outcome? Within 30 days, you’ll have designed and documented a board-ready data architecture proposal. A real-world system. One that demonstrates clean separation, resiliency patterns, and cost-efficient scaling-all built using battle-tested design principles taught in this course. You’ll walk in with uncertainty and walk out with a portfolio-grade project that screams “technical leader”. Like Maria T., Senior Data Engineer at a Fortune 500 fintech, who used this framework to redesign their event ingestion layer. Her team cut cloud costs by 42%, reduced pipeline failures by 78%, and presented the work to C-suite-landing her a promotion to Principal Engineer within six weeks. This isn’t luck. It’s method. And it’s repeatable. Here’s how this course is structured to help you get there.Course Format & Delivery Details Self-Paced, Always Accessible, Engineered for Real Careers
This course is designed for professionals who lead complex data environments but don’t have time for rigid schedules or filler content. From the moment you enrol, you gain structured, on-demand access to every module. No fixed start dates. No deadlines. You decide when and where you learn-whether that’s during a quiet weekend or between sprints at work. Most learners complete the core curriculum in 20 to 30 hours, with tangible results visible in under two weeks. You can implement one pattern this week and present it next Monday. The ROI starts early, not after the final lesson. All materials are mobile-friendly and accessible 24/7 from any device. Whether you’re reviewing architecture diagrams on your phone during a commute or deep-diving into implementation checklists at your desk, the interface adapts seamlessly to your workflow. You receive lifetime access to the full course content, including all future updates. As new data platforms emerge and patterns evolve, the material is refined and expanded-at no additional cost. This is not a one-time training. It’s a living reference you’ll use for years. Expert Guidance & Real-World Validation
You’re not learning in isolation. Each module includes direct guidance from senior data architects with 10+ years of experience designing systems for petabyte-scale environments. Their insights are embedded in practical decision trees, architecture templates, and implementation checklists. Need clarification? You’ll have access to structured support channels, where expert reviewers provide feedback on your project designs and answer technical questions with precision. Upon completion, you’ll earn a Certificate of Completion issued by The Art of Service-a globally recognised credential trusted by engineering leaders in over 85 countries. This is not a participation badge. It’s verification that you’ve mastered scalable design patterns to enterprise standards. Hiring managers know the name. Recruiters cite it. Your peers will notice. No Risk. Full Clarity. 100% Value Protection.
We know the biggest question is: “Will this work for me?” Especially if you’re working with legacy systems, hybrid clouds, or non-standard tooling. Let us be clear: This works even if you’re not at a tech giant, even if your stack isn’t cutting-edge, and even if you’ve never led a full architecture rollout before. The design patterns taught here are stack-agnostic and principle-driven. They’ve been applied successfully by engineers using Snowflake, BigQuery, Kafka, Flink, Delta Lake, Redshift, and custom-built ingestion layers. You’ll see examples from data engineers in healthcare, logistics, SaaS, and finance-all adapting the same core patterns to their context. John R., Data Architect in Berlin, used these templates to modernise a 7-year-old ETL system running on-premise. With no cloud migration budget, he applied hybrid caching and backpressure patterns to stabilise the system-reducing SLA breaches from 17% to under 2% in eight weeks. Pricing is straightforward with no hidden fees. What you see is what you pay-zero surprises. We accept all major payment methods, including Visa, Mastercard, and PayPal. After enrolling, you’ll receive a confirmation email. Your access details and learning portal credentials will be sent separately once your course materials are fully provisioned. If at any point you feel this course hasn’t delivered clear, actionable value, contact us for a full refund. We stand behind this material so completely that we offer a satisfied-or-refunded guarantee. Because your career momentum matters more than any sale.
Module 1: Foundations of Scalable Data Systems - Defining scalability in modern data engineering
- Vertical vs. horizontal scaling: when to use each
- Understanding throughput, latency, and burst capacity
- The role of idempotency in scalable pipelines
- Data volume growth curves and forecasting techniques
- Identifying bottlenecks before they occur
- Stateless vs. stateful processing trade-offs
- Consistency models: strong, eventual, causal
- Distributed systems challenges: network partitioning, clock skew
- Backpressure fundamentals and propagation mechanisms
- Idempotent processing in high-volume ingestion
- At-least-once vs. exactly-once delivery semantics
- The CAP theorem and its practical implications
- Partitioning strategies: range, hash, list, dynamic
- Sharding and its impact on query performance
- Replication: synchronous vs. asynchronous models
- Failure domains and isolation boundaries
- Designing for multi-region deployment
- Cost of redundancy: availability vs. expense
- Common anti-patterns in early-stage scaling
Module 2: Core Design Patterns for Ingestion - Event-driven ingestion vs. batch polling
- Using message brokers for decoupled ingestion
- Schema-on-read vs. schema-on-write approaches
- Avro, Protobuf, and JSON: format selection criteria
- Schema registry implementation patterns
- Handling schema evolution safely
- Dead-letter queues and error routing strategies
- Retry mechanisms with exponential backoff
- Circuit breaker pattern in data pipelines
- Throttling and rate limiting on source systems
- Checkpointing and offset management
- Idempotent consumers and deduplication keys
- Handling late-arriving data
- Watermarking techniques in streaming systems
- Replayability of event streams
- Log compaction and retention policies
- Securing data in transit during ingestion
- Authentication and authorisation for data sources
- Observability: monitoring ingestion lag
- Automated alerting for ingestion failures
Module 3: Data Processing Architecture Patterns - Micro-batch vs. continuous streaming
- Lambda architecture: components and trade-offs
- Kappa architecture: simplification and benefits
- Unified processing with modern engines (Flink, Spark)
- Data enrichment: inline vs. post-process
- Joining streaming and static datasets
- Windowing strategies: tumbling, sliding, session
- State management in distributed processing
- Checkpoint intervals and recovery time objectives
- Skew handling in distributed aggregation
- Dynamic scaling of processing units
- Resource isolation for multi-tenant pipelines
- Graceful shutdown and restart protocols
- Rolling updates with zero downtime
- Blue-green deployments for data jobs
- Canary testing of pipeline logic
- Rollback strategies for failed deployments
- Feature flags in data transformation logic
- Version control for ETL/ELT scripts
- Infrastructure-as-code for pipeline orchestration
Module 4: Storage Layer Design Principles - Hot, warm, cold data tiering strategies
- Choosing file formats: Parquet, ORC, Iceberg
- Columnar storage benefits and optimisations
- Partitioning for query performance
- Clustering and sorting keys in large tables
- Compaction strategies for small files
- File size optimisation for cloud storage
- Data lake vs. data warehouse trade-offs
- Z-ordering for multi-dimensional queries
- Indexing strategies in distributed storage
- Metadata management with central catalogues
- ACID transactions in data lakes
- Time travel and point-in-time queries
- Schema enforcement and governance policies
- Data lifecycle automation with retention rules
- Cold storage migration triggers
- Encryption at rest: key management models
- Access patterns and performance profiling
- Cost-aware storage selection
- Benchmarking storage performance
Module 5: Orchestration & Workflow Management - Directed Acyclic Graphs (DAGs) as first-class citizens
- Dependency management across pipelines
- Dynamic task generation patterns
- Parametrised workflows for reusability
- Error handling in DAG execution
- Rerun strategies for failed tasks
- Upstream vs. downstream triggering
- External task sensors and integration points
- Timeout and SLA monitoring for workflows
- Alerting on DAG failure or delay
- Scheduling strategies: cron, event-based, hybrid
- Distributed scheduling with load balancing
- High availability for orchestration backends
- Scaling orchestrators under load
- UI-based monitoring of pipeline health
- Metadata database optimisation
- Orchestrator logging and audit trails
- Role-based access control for DAGs
- Testing workflows in isolation
- Mocking external systems during development
Module 6: Streaming System Patterns - Kafka Streams vs. Flink vs. Spark Streaming
- Event time vs. processing time semantics
- Kafka consumer group scaling
- Rebalancing strategies and minimising downtime
- Pulsar and Kinesis as alternatives
- Exactly-once processing guarantees
- Transactional producers and consumers
- Fan-out patterns for real-time subscribers
- Broadcast join patterns in streaming
- State stores and RocksDB optimisations
- Scaling stateful stream processing
- Queryable state for real-time lookups
- Windowed joins and sessionisation
- Topology design for low-latency pipelines
- Backpressure handling in streaming graphs
- Load shedding during peak loads
- Metrics collection from stream processors
- Latency monitoring and p99 tracking
- Testing streaming logic with test harnesses
- Replay testing for correctness validation
Module 7: Data Quality & Observability - Defining data quality dimensions: accuracy, completeness, timeliness
- Schema conformance checks at ingestion
- Statistical profiling for anomaly detection
- Threshold-based alerting on data drift
- Reference data validation patterns
- Null rate monitoring and field-level checks
- Custom data quality rules with DSLs
- Automated remediation workflows
- Data lineage tracking across transformations
- Column-level lineage vs. table-level
- Impact analysis for schema changes
- Visualising data flow dependencies
- Observability: logs, metrics, traces
- Structured logging for pipeline debugging
- Correlation IDs across distributed systems
- Monitoring resource utilisation: CPU, memory, I/O
- Auto-scaling triggers based on metrics
- Cost monitoring per pipeline or job
- Alert fatigue reduction with intelligent routing
- Dashboards for operational visibility
Module 8: Scalability Patterns for Modern Warehousing - Separation of compute and storage
- Automatic scaling of query engines
- Workload management with queues and pools
- Cost controls for runaway queries
- Resource monitoring and utilisation alerts
- Query optimisation: predicate pushdown, pruning
- Materialised views and incrementality
- Incremental data loading with change data capture
- Change Data Capture: log-based vs. trigger-based
- Tracking deletions in incremental loads
- Slowly Changing Dimensions (SCD) Types 1–4
- SCD Type 6: hybrid approach implementation
- Upsert patterns with MERGE statements
- Indexing strategies in cloud data warehouses
- Partitioning large fact tables
- Clustering for query performance
- Query history analysis for tuning
- Cost attribution by team or project
- Role-based access and data masking
- Row-level security policies
Module 9: Data Mesh & Decentralised Architecture - Domain-driven data ownership principles
- Defining data products as first-class citizens
- Self-serve data platforms and infrastructure
- Contract-first development with data APIs
- Schema as code and versioned contracts
- Federated governance models
- Central standards with local autonomy
- Data product discovery with catalogues
- Tagging, documentation, and ownership metadata
- Distributed testing and CI/CD for data products
- Automated compliance checks in pipelines
- Monitoring SLAs across teams
- Chargeback and showback models
- Cost transparency for data consumers
- API gateways for data access
- GraphQL for flexible data querying
- REST vs. gRPC for data services
- Authentication for data product APIs
- Audit logging for access tracking
- Service level objectives for data freshness
Module 10: Resiliency & Disaster Recovery - Designing for failure: assume everything breaks
- Retry patterns with jitter and backoff
- Circuit breaker implementation with fallbacks
- Failover strategies for primary-secondary systems
- Active-active vs. active-passive deployments
- Cross-region replication of critical data
- Automated switchover testing schedules
- Backup strategies: full, incremental, differential
- Point-in-time recovery planning
- Recovery Time Objective (RTO) definition
- Recovery Point Objective (RPO) alignment
- Testing disaster recovery runbooks
- Backup validation with automated restore tests
- Data consistency checks post-recovery
- Immutable backups to prevent tampering
- Retention policies for compliance
- Monitoring backup completion and integrity
- Automated alerts for failed backups
- Secure key management for encrypted backups
- Incident response playbooks for data outages
Module 11: Cost Optimisation at Scale - Unit economics of data processing
- Cost per GB ingested, stored, queried
- Identifying cost outliers in pipelines
- Right-sizing compute clusters
- Auto-pausing and auto-resuming clusters
- Spot instances and preemptible VMs for batch jobs
- Data compression strategies and savings impact
- Tiered storage cost models
- Archiving older data to low-cost storage
- Query optimisation to reduce scanned data
- Materialised aggregations for expensive queries
- Query caching and result reuse
- Cost attribution by team, project, or pipeline
- Budget alerts and spending caps
- Cost allocation tags and naming conventions
- Monitoring tools for cloud spend
- Negotiating reserved capacity discounts
- Using query profiles to detect inefficiencies
- Automated cost reporting and dashboards
- Cost-aware development practices
Module 12: Implementation Roadmap & Real-World Projects - Assessing current system maturity
- Gap analysis against scalable patterns
- Prioritisation framework: impact vs. effort
- Building a phased rollout plan
- Risk assessment for pattern adoption
- Stakeholder communication strategy
- Change management for engineering teams
- Creating a board-ready architecture proposal
- Documenting design decisions with ADRs
- Architecture Decision Records (ADRs) best practices
- Presenting technical trade-offs to executives
- Visualising architecture with diagrams
- Using C4 model for system visualisation
- Creating component and container diagrams
- Defining success metrics for implementation
- Setting KPIs for scalability and reliability
- Running a pilot project with measurable outcomes
- Gathering feedback from users and teams
- Scaling the pattern enterprise-wide
- Continuous improvement with retrospectives
Module 13: Certification, Career Advancement & Next Steps - Reviewing core design patterns for mastery
- Self-assessment checklist for pattern application
- Preparing your Certificate of Completion project submission
- Documentation standards for professional review
- How to present your completed architecture proposal
- Adding the credential to LinkedIn and CVs
- Using the certification in promotion discussions
- Negotiating higher compensation with proven skills
- Becoming the go-to architect in your organisation
- Mentoring junior engineers using design patterns
- Contributing to internal design councils
- Speaking at tech talks with confidence
- Building a personal brand as a data systems expert
- Contributing to open-source data projects
- Staying updated with evolving patterns
- Accessing future course updates and community forums
- Real-time notifications for new pattern releases
- Exclusive access to advanced pattern libraries
- Lifetime updates to the curriculum
- Navigating the next career level with clarity and proof
- Defining scalability in modern data engineering
- Vertical vs. horizontal scaling: when to use each
- Understanding throughput, latency, and burst capacity
- The role of idempotency in scalable pipelines
- Data volume growth curves and forecasting techniques
- Identifying bottlenecks before they occur
- Stateless vs. stateful processing trade-offs
- Consistency models: strong, eventual, causal
- Distributed systems challenges: network partitioning, clock skew
- Backpressure fundamentals and propagation mechanisms
- Idempotent processing in high-volume ingestion
- At-least-once vs. exactly-once delivery semantics
- The CAP theorem and its practical implications
- Partitioning strategies: range, hash, list, dynamic
- Sharding and its impact on query performance
- Replication: synchronous vs. asynchronous models
- Failure domains and isolation boundaries
- Designing for multi-region deployment
- Cost of redundancy: availability vs. expense
- Common anti-patterns in early-stage scaling
Module 2: Core Design Patterns for Ingestion - Event-driven ingestion vs. batch polling
- Using message brokers for decoupled ingestion
- Schema-on-read vs. schema-on-write approaches
- Avro, Protobuf, and JSON: format selection criteria
- Schema registry implementation patterns
- Handling schema evolution safely
- Dead-letter queues and error routing strategies
- Retry mechanisms with exponential backoff
- Circuit breaker pattern in data pipelines
- Throttling and rate limiting on source systems
- Checkpointing and offset management
- Idempotent consumers and deduplication keys
- Handling late-arriving data
- Watermarking techniques in streaming systems
- Replayability of event streams
- Log compaction and retention policies
- Securing data in transit during ingestion
- Authentication and authorisation for data sources
- Observability: monitoring ingestion lag
- Automated alerting for ingestion failures
Module 3: Data Processing Architecture Patterns - Micro-batch vs. continuous streaming
- Lambda architecture: components and trade-offs
- Kappa architecture: simplification and benefits
- Unified processing with modern engines (Flink, Spark)
- Data enrichment: inline vs. post-process
- Joining streaming and static datasets
- Windowing strategies: tumbling, sliding, session
- State management in distributed processing
- Checkpoint intervals and recovery time objectives
- Skew handling in distributed aggregation
- Dynamic scaling of processing units
- Resource isolation for multi-tenant pipelines
- Graceful shutdown and restart protocols
- Rolling updates with zero downtime
- Blue-green deployments for data jobs
- Canary testing of pipeline logic
- Rollback strategies for failed deployments
- Feature flags in data transformation logic
- Version control for ETL/ELT scripts
- Infrastructure-as-code for pipeline orchestration
Module 4: Storage Layer Design Principles - Hot, warm, cold data tiering strategies
- Choosing file formats: Parquet, ORC, Iceberg
- Columnar storage benefits and optimisations
- Partitioning for query performance
- Clustering and sorting keys in large tables
- Compaction strategies for small files
- File size optimisation for cloud storage
- Data lake vs. data warehouse trade-offs
- Z-ordering for multi-dimensional queries
- Indexing strategies in distributed storage
- Metadata management with central catalogues
- ACID transactions in data lakes
- Time travel and point-in-time queries
- Schema enforcement and governance policies
- Data lifecycle automation with retention rules
- Cold storage migration triggers
- Encryption at rest: key management models
- Access patterns and performance profiling
- Cost-aware storage selection
- Benchmarking storage performance
Module 5: Orchestration & Workflow Management - Directed Acyclic Graphs (DAGs) as first-class citizens
- Dependency management across pipelines
- Dynamic task generation patterns
- Parametrised workflows for reusability
- Error handling in DAG execution
- Rerun strategies for failed tasks
- Upstream vs. downstream triggering
- External task sensors and integration points
- Timeout and SLA monitoring for workflows
- Alerting on DAG failure or delay
- Scheduling strategies: cron, event-based, hybrid
- Distributed scheduling with load balancing
- High availability for orchestration backends
- Scaling orchestrators under load
- UI-based monitoring of pipeline health
- Metadata database optimisation
- Orchestrator logging and audit trails
- Role-based access control for DAGs
- Testing workflows in isolation
- Mocking external systems during development
Module 6: Streaming System Patterns - Kafka Streams vs. Flink vs. Spark Streaming
- Event time vs. processing time semantics
- Kafka consumer group scaling
- Rebalancing strategies and minimising downtime
- Pulsar and Kinesis as alternatives
- Exactly-once processing guarantees
- Transactional producers and consumers
- Fan-out patterns for real-time subscribers
- Broadcast join patterns in streaming
- State stores and RocksDB optimisations
- Scaling stateful stream processing
- Queryable state for real-time lookups
- Windowed joins and sessionisation
- Topology design for low-latency pipelines
- Backpressure handling in streaming graphs
- Load shedding during peak loads
- Metrics collection from stream processors
- Latency monitoring and p99 tracking
- Testing streaming logic with test harnesses
- Replay testing for correctness validation
Module 7: Data Quality & Observability - Defining data quality dimensions: accuracy, completeness, timeliness
- Schema conformance checks at ingestion
- Statistical profiling for anomaly detection
- Threshold-based alerting on data drift
- Reference data validation patterns
- Null rate monitoring and field-level checks
- Custom data quality rules with DSLs
- Automated remediation workflows
- Data lineage tracking across transformations
- Column-level lineage vs. table-level
- Impact analysis for schema changes
- Visualising data flow dependencies
- Observability: logs, metrics, traces
- Structured logging for pipeline debugging
- Correlation IDs across distributed systems
- Monitoring resource utilisation: CPU, memory, I/O
- Auto-scaling triggers based on metrics
- Cost monitoring per pipeline or job
- Alert fatigue reduction with intelligent routing
- Dashboards for operational visibility
Module 8: Scalability Patterns for Modern Warehousing - Separation of compute and storage
- Automatic scaling of query engines
- Workload management with queues and pools
- Cost controls for runaway queries
- Resource monitoring and utilisation alerts
- Query optimisation: predicate pushdown, pruning
- Materialised views and incrementality
- Incremental data loading with change data capture
- Change Data Capture: log-based vs. trigger-based
- Tracking deletions in incremental loads
- Slowly Changing Dimensions (SCD) Types 1–4
- SCD Type 6: hybrid approach implementation
- Upsert patterns with MERGE statements
- Indexing strategies in cloud data warehouses
- Partitioning large fact tables
- Clustering for query performance
- Query history analysis for tuning
- Cost attribution by team or project
- Role-based access and data masking
- Row-level security policies
Module 9: Data Mesh & Decentralised Architecture - Domain-driven data ownership principles
- Defining data products as first-class citizens
- Self-serve data platforms and infrastructure
- Contract-first development with data APIs
- Schema as code and versioned contracts
- Federated governance models
- Central standards with local autonomy
- Data product discovery with catalogues
- Tagging, documentation, and ownership metadata
- Distributed testing and CI/CD for data products
- Automated compliance checks in pipelines
- Monitoring SLAs across teams
- Chargeback and showback models
- Cost transparency for data consumers
- API gateways for data access
- GraphQL for flexible data querying
- REST vs. gRPC for data services
- Authentication for data product APIs
- Audit logging for access tracking
- Service level objectives for data freshness
Module 10: Resiliency & Disaster Recovery - Designing for failure: assume everything breaks
- Retry patterns with jitter and backoff
- Circuit breaker implementation with fallbacks
- Failover strategies for primary-secondary systems
- Active-active vs. active-passive deployments
- Cross-region replication of critical data
- Automated switchover testing schedules
- Backup strategies: full, incremental, differential
- Point-in-time recovery planning
- Recovery Time Objective (RTO) definition
- Recovery Point Objective (RPO) alignment
- Testing disaster recovery runbooks
- Backup validation with automated restore tests
- Data consistency checks post-recovery
- Immutable backups to prevent tampering
- Retention policies for compliance
- Monitoring backup completion and integrity
- Automated alerts for failed backups
- Secure key management for encrypted backups
- Incident response playbooks for data outages
Module 11: Cost Optimisation at Scale - Unit economics of data processing
- Cost per GB ingested, stored, queried
- Identifying cost outliers in pipelines
- Right-sizing compute clusters
- Auto-pausing and auto-resuming clusters
- Spot instances and preemptible VMs for batch jobs
- Data compression strategies and savings impact
- Tiered storage cost models
- Archiving older data to low-cost storage
- Query optimisation to reduce scanned data
- Materialised aggregations for expensive queries
- Query caching and result reuse
- Cost attribution by team, project, or pipeline
- Budget alerts and spending caps
- Cost allocation tags and naming conventions
- Monitoring tools for cloud spend
- Negotiating reserved capacity discounts
- Using query profiles to detect inefficiencies
- Automated cost reporting and dashboards
- Cost-aware development practices
Module 12: Implementation Roadmap & Real-World Projects - Assessing current system maturity
- Gap analysis against scalable patterns
- Prioritisation framework: impact vs. effort
- Building a phased rollout plan
- Risk assessment for pattern adoption
- Stakeholder communication strategy
- Change management for engineering teams
- Creating a board-ready architecture proposal
- Documenting design decisions with ADRs
- Architecture Decision Records (ADRs) best practices
- Presenting technical trade-offs to executives
- Visualising architecture with diagrams
- Using C4 model for system visualisation
- Creating component and container diagrams
- Defining success metrics for implementation
- Setting KPIs for scalability and reliability
- Running a pilot project with measurable outcomes
- Gathering feedback from users and teams
- Scaling the pattern enterprise-wide
- Continuous improvement with retrospectives
Module 13: Certification, Career Advancement & Next Steps - Reviewing core design patterns for mastery
- Self-assessment checklist for pattern application
- Preparing your Certificate of Completion project submission
- Documentation standards for professional review
- How to present your completed architecture proposal
- Adding the credential to LinkedIn and CVs
- Using the certification in promotion discussions
- Negotiating higher compensation with proven skills
- Becoming the go-to architect in your organisation
- Mentoring junior engineers using design patterns
- Contributing to internal design councils
- Speaking at tech talks with confidence
- Building a personal brand as a data systems expert
- Contributing to open-source data projects
- Staying updated with evolving patterns
- Accessing future course updates and community forums
- Real-time notifications for new pattern releases
- Exclusive access to advanced pattern libraries
- Lifetime updates to the curriculum
- Navigating the next career level with clarity and proof
- Micro-batch vs. continuous streaming
- Lambda architecture: components and trade-offs
- Kappa architecture: simplification and benefits
- Unified processing with modern engines (Flink, Spark)
- Data enrichment: inline vs. post-process
- Joining streaming and static datasets
- Windowing strategies: tumbling, sliding, session
- State management in distributed processing
- Checkpoint intervals and recovery time objectives
- Skew handling in distributed aggregation
- Dynamic scaling of processing units
- Resource isolation for multi-tenant pipelines
- Graceful shutdown and restart protocols
- Rolling updates with zero downtime
- Blue-green deployments for data jobs
- Canary testing of pipeline logic
- Rollback strategies for failed deployments
- Feature flags in data transformation logic
- Version control for ETL/ELT scripts
- Infrastructure-as-code for pipeline orchestration
Module 4: Storage Layer Design Principles - Hot, warm, cold data tiering strategies
- Choosing file formats: Parquet, ORC, Iceberg
- Columnar storage benefits and optimisations
- Partitioning for query performance
- Clustering and sorting keys in large tables
- Compaction strategies for small files
- File size optimisation for cloud storage
- Data lake vs. data warehouse trade-offs
- Z-ordering for multi-dimensional queries
- Indexing strategies in distributed storage
- Metadata management with central catalogues
- ACID transactions in data lakes
- Time travel and point-in-time queries
- Schema enforcement and governance policies
- Data lifecycle automation with retention rules
- Cold storage migration triggers
- Encryption at rest: key management models
- Access patterns and performance profiling
- Cost-aware storage selection
- Benchmarking storage performance
Module 5: Orchestration & Workflow Management - Directed Acyclic Graphs (DAGs) as first-class citizens
- Dependency management across pipelines
- Dynamic task generation patterns
- Parametrised workflows for reusability
- Error handling in DAG execution
- Rerun strategies for failed tasks
- Upstream vs. downstream triggering
- External task sensors and integration points
- Timeout and SLA monitoring for workflows
- Alerting on DAG failure or delay
- Scheduling strategies: cron, event-based, hybrid
- Distributed scheduling with load balancing
- High availability for orchestration backends
- Scaling orchestrators under load
- UI-based monitoring of pipeline health
- Metadata database optimisation
- Orchestrator logging and audit trails
- Role-based access control for DAGs
- Testing workflows in isolation
- Mocking external systems during development
Module 6: Streaming System Patterns - Kafka Streams vs. Flink vs. Spark Streaming
- Event time vs. processing time semantics
- Kafka consumer group scaling
- Rebalancing strategies and minimising downtime
- Pulsar and Kinesis as alternatives
- Exactly-once processing guarantees
- Transactional producers and consumers
- Fan-out patterns for real-time subscribers
- Broadcast join patterns in streaming
- State stores and RocksDB optimisations
- Scaling stateful stream processing
- Queryable state for real-time lookups
- Windowed joins and sessionisation
- Topology design for low-latency pipelines
- Backpressure handling in streaming graphs
- Load shedding during peak loads
- Metrics collection from stream processors
- Latency monitoring and p99 tracking
- Testing streaming logic with test harnesses
- Replay testing for correctness validation
Module 7: Data Quality & Observability - Defining data quality dimensions: accuracy, completeness, timeliness
- Schema conformance checks at ingestion
- Statistical profiling for anomaly detection
- Threshold-based alerting on data drift
- Reference data validation patterns
- Null rate monitoring and field-level checks
- Custom data quality rules with DSLs
- Automated remediation workflows
- Data lineage tracking across transformations
- Column-level lineage vs. table-level
- Impact analysis for schema changes
- Visualising data flow dependencies
- Observability: logs, metrics, traces
- Structured logging for pipeline debugging
- Correlation IDs across distributed systems
- Monitoring resource utilisation: CPU, memory, I/O
- Auto-scaling triggers based on metrics
- Cost monitoring per pipeline or job
- Alert fatigue reduction with intelligent routing
- Dashboards for operational visibility
Module 8: Scalability Patterns for Modern Warehousing - Separation of compute and storage
- Automatic scaling of query engines
- Workload management with queues and pools
- Cost controls for runaway queries
- Resource monitoring and utilisation alerts
- Query optimisation: predicate pushdown, pruning
- Materialised views and incrementality
- Incremental data loading with change data capture
- Change Data Capture: log-based vs. trigger-based
- Tracking deletions in incremental loads
- Slowly Changing Dimensions (SCD) Types 1–4
- SCD Type 6: hybrid approach implementation
- Upsert patterns with MERGE statements
- Indexing strategies in cloud data warehouses
- Partitioning large fact tables
- Clustering for query performance
- Query history analysis for tuning
- Cost attribution by team or project
- Role-based access and data masking
- Row-level security policies
Module 9: Data Mesh & Decentralised Architecture - Domain-driven data ownership principles
- Defining data products as first-class citizens
- Self-serve data platforms and infrastructure
- Contract-first development with data APIs
- Schema as code and versioned contracts
- Federated governance models
- Central standards with local autonomy
- Data product discovery with catalogues
- Tagging, documentation, and ownership metadata
- Distributed testing and CI/CD for data products
- Automated compliance checks in pipelines
- Monitoring SLAs across teams
- Chargeback and showback models
- Cost transparency for data consumers
- API gateways for data access
- GraphQL for flexible data querying
- REST vs. gRPC for data services
- Authentication for data product APIs
- Audit logging for access tracking
- Service level objectives for data freshness
Module 10: Resiliency & Disaster Recovery - Designing for failure: assume everything breaks
- Retry patterns with jitter and backoff
- Circuit breaker implementation with fallbacks
- Failover strategies for primary-secondary systems
- Active-active vs. active-passive deployments
- Cross-region replication of critical data
- Automated switchover testing schedules
- Backup strategies: full, incremental, differential
- Point-in-time recovery planning
- Recovery Time Objective (RTO) definition
- Recovery Point Objective (RPO) alignment
- Testing disaster recovery runbooks
- Backup validation with automated restore tests
- Data consistency checks post-recovery
- Immutable backups to prevent tampering
- Retention policies for compliance
- Monitoring backup completion and integrity
- Automated alerts for failed backups
- Secure key management for encrypted backups
- Incident response playbooks for data outages
Module 11: Cost Optimisation at Scale - Unit economics of data processing
- Cost per GB ingested, stored, queried
- Identifying cost outliers in pipelines
- Right-sizing compute clusters
- Auto-pausing and auto-resuming clusters
- Spot instances and preemptible VMs for batch jobs
- Data compression strategies and savings impact
- Tiered storage cost models
- Archiving older data to low-cost storage
- Query optimisation to reduce scanned data
- Materialised aggregations for expensive queries
- Query caching and result reuse
- Cost attribution by team, project, or pipeline
- Budget alerts and spending caps
- Cost allocation tags and naming conventions
- Monitoring tools for cloud spend
- Negotiating reserved capacity discounts
- Using query profiles to detect inefficiencies
- Automated cost reporting and dashboards
- Cost-aware development practices
Module 12: Implementation Roadmap & Real-World Projects - Assessing current system maturity
- Gap analysis against scalable patterns
- Prioritisation framework: impact vs. effort
- Building a phased rollout plan
- Risk assessment for pattern adoption
- Stakeholder communication strategy
- Change management for engineering teams
- Creating a board-ready architecture proposal
- Documenting design decisions with ADRs
- Architecture Decision Records (ADRs) best practices
- Presenting technical trade-offs to executives
- Visualising architecture with diagrams
- Using C4 model for system visualisation
- Creating component and container diagrams
- Defining success metrics for implementation
- Setting KPIs for scalability and reliability
- Running a pilot project with measurable outcomes
- Gathering feedback from users and teams
- Scaling the pattern enterprise-wide
- Continuous improvement with retrospectives
Module 13: Certification, Career Advancement & Next Steps - Reviewing core design patterns for mastery
- Self-assessment checklist for pattern application
- Preparing your Certificate of Completion project submission
- Documentation standards for professional review
- How to present your completed architecture proposal
- Adding the credential to LinkedIn and CVs
- Using the certification in promotion discussions
- Negotiating higher compensation with proven skills
- Becoming the go-to architect in your organisation
- Mentoring junior engineers using design patterns
- Contributing to internal design councils
- Speaking at tech talks with confidence
- Building a personal brand as a data systems expert
- Contributing to open-source data projects
- Staying updated with evolving patterns
- Accessing future course updates and community forums
- Real-time notifications for new pattern releases
- Exclusive access to advanced pattern libraries
- Lifetime updates to the curriculum
- Navigating the next career level with clarity and proof
- Directed Acyclic Graphs (DAGs) as first-class citizens
- Dependency management across pipelines
- Dynamic task generation patterns
- Parametrised workflows for reusability
- Error handling in DAG execution
- Rerun strategies for failed tasks
- Upstream vs. downstream triggering
- External task sensors and integration points
- Timeout and SLA monitoring for workflows
- Alerting on DAG failure or delay
- Scheduling strategies: cron, event-based, hybrid
- Distributed scheduling with load balancing
- High availability for orchestration backends
- Scaling orchestrators under load
- UI-based monitoring of pipeline health
- Metadata database optimisation
- Orchestrator logging and audit trails
- Role-based access control for DAGs
- Testing workflows in isolation
- Mocking external systems during development
Module 6: Streaming System Patterns - Kafka Streams vs. Flink vs. Spark Streaming
- Event time vs. processing time semantics
- Kafka consumer group scaling
- Rebalancing strategies and minimising downtime
- Pulsar and Kinesis as alternatives
- Exactly-once processing guarantees
- Transactional producers and consumers
- Fan-out patterns for real-time subscribers
- Broadcast join patterns in streaming
- State stores and RocksDB optimisations
- Scaling stateful stream processing
- Queryable state for real-time lookups
- Windowed joins and sessionisation
- Topology design for low-latency pipelines
- Backpressure handling in streaming graphs
- Load shedding during peak loads
- Metrics collection from stream processors
- Latency monitoring and p99 tracking
- Testing streaming logic with test harnesses
- Replay testing for correctness validation
Module 7: Data Quality & Observability - Defining data quality dimensions: accuracy, completeness, timeliness
- Schema conformance checks at ingestion
- Statistical profiling for anomaly detection
- Threshold-based alerting on data drift
- Reference data validation patterns
- Null rate monitoring and field-level checks
- Custom data quality rules with DSLs
- Automated remediation workflows
- Data lineage tracking across transformations
- Column-level lineage vs. table-level
- Impact analysis for schema changes
- Visualising data flow dependencies
- Observability: logs, metrics, traces
- Structured logging for pipeline debugging
- Correlation IDs across distributed systems
- Monitoring resource utilisation: CPU, memory, I/O
- Auto-scaling triggers based on metrics
- Cost monitoring per pipeline or job
- Alert fatigue reduction with intelligent routing
- Dashboards for operational visibility
Module 8: Scalability Patterns for Modern Warehousing - Separation of compute and storage
- Automatic scaling of query engines
- Workload management with queues and pools
- Cost controls for runaway queries
- Resource monitoring and utilisation alerts
- Query optimisation: predicate pushdown, pruning
- Materialised views and incrementality
- Incremental data loading with change data capture
- Change Data Capture: log-based vs. trigger-based
- Tracking deletions in incremental loads
- Slowly Changing Dimensions (SCD) Types 1–4
- SCD Type 6: hybrid approach implementation
- Upsert patterns with MERGE statements
- Indexing strategies in cloud data warehouses
- Partitioning large fact tables
- Clustering for query performance
- Query history analysis for tuning
- Cost attribution by team or project
- Role-based access and data masking
- Row-level security policies
Module 9: Data Mesh & Decentralised Architecture - Domain-driven data ownership principles
- Defining data products as first-class citizens
- Self-serve data platforms and infrastructure
- Contract-first development with data APIs
- Schema as code and versioned contracts
- Federated governance models
- Central standards with local autonomy
- Data product discovery with catalogues
- Tagging, documentation, and ownership metadata
- Distributed testing and CI/CD for data products
- Automated compliance checks in pipelines
- Monitoring SLAs across teams
- Chargeback and showback models
- Cost transparency for data consumers
- API gateways for data access
- GraphQL for flexible data querying
- REST vs. gRPC for data services
- Authentication for data product APIs
- Audit logging for access tracking
- Service level objectives for data freshness
Module 10: Resiliency & Disaster Recovery - Designing for failure: assume everything breaks
- Retry patterns with jitter and backoff
- Circuit breaker implementation with fallbacks
- Failover strategies for primary-secondary systems
- Active-active vs. active-passive deployments
- Cross-region replication of critical data
- Automated switchover testing schedules
- Backup strategies: full, incremental, differential
- Point-in-time recovery planning
- Recovery Time Objective (RTO) definition
- Recovery Point Objective (RPO) alignment
- Testing disaster recovery runbooks
- Backup validation with automated restore tests
- Data consistency checks post-recovery
- Immutable backups to prevent tampering
- Retention policies for compliance
- Monitoring backup completion and integrity
- Automated alerts for failed backups
- Secure key management for encrypted backups
- Incident response playbooks for data outages
Module 11: Cost Optimisation at Scale - Unit economics of data processing
- Cost per GB ingested, stored, queried
- Identifying cost outliers in pipelines
- Right-sizing compute clusters
- Auto-pausing and auto-resuming clusters
- Spot instances and preemptible VMs for batch jobs
- Data compression strategies and savings impact
- Tiered storage cost models
- Archiving older data to low-cost storage
- Query optimisation to reduce scanned data
- Materialised aggregations for expensive queries
- Query caching and result reuse
- Cost attribution by team, project, or pipeline
- Budget alerts and spending caps
- Cost allocation tags and naming conventions
- Monitoring tools for cloud spend
- Negotiating reserved capacity discounts
- Using query profiles to detect inefficiencies
- Automated cost reporting and dashboards
- Cost-aware development practices
Module 12: Implementation Roadmap & Real-World Projects - Assessing current system maturity
- Gap analysis against scalable patterns
- Prioritisation framework: impact vs. effort
- Building a phased rollout plan
- Risk assessment for pattern adoption
- Stakeholder communication strategy
- Change management for engineering teams
- Creating a board-ready architecture proposal
- Documenting design decisions with ADRs
- Architecture Decision Records (ADRs) best practices
- Presenting technical trade-offs to executives
- Visualising architecture with diagrams
- Using C4 model for system visualisation
- Creating component and container diagrams
- Defining success metrics for implementation
- Setting KPIs for scalability and reliability
- Running a pilot project with measurable outcomes
- Gathering feedback from users and teams
- Scaling the pattern enterprise-wide
- Continuous improvement with retrospectives
Module 13: Certification, Career Advancement & Next Steps - Reviewing core design patterns for mastery
- Self-assessment checklist for pattern application
- Preparing your Certificate of Completion project submission
- Documentation standards for professional review
- How to present your completed architecture proposal
- Adding the credential to LinkedIn and CVs
- Using the certification in promotion discussions
- Negotiating higher compensation with proven skills
- Becoming the go-to architect in your organisation
- Mentoring junior engineers using design patterns
- Contributing to internal design councils
- Speaking at tech talks with confidence
- Building a personal brand as a data systems expert
- Contributing to open-source data projects
- Staying updated with evolving patterns
- Accessing future course updates and community forums
- Real-time notifications for new pattern releases
- Exclusive access to advanced pattern libraries
- Lifetime updates to the curriculum
- Navigating the next career level with clarity and proof
- Defining data quality dimensions: accuracy, completeness, timeliness
- Schema conformance checks at ingestion
- Statistical profiling for anomaly detection
- Threshold-based alerting on data drift
- Reference data validation patterns
- Null rate monitoring and field-level checks
- Custom data quality rules with DSLs
- Automated remediation workflows
- Data lineage tracking across transformations
- Column-level lineage vs. table-level
- Impact analysis for schema changes
- Visualising data flow dependencies
- Observability: logs, metrics, traces
- Structured logging for pipeline debugging
- Correlation IDs across distributed systems
- Monitoring resource utilisation: CPU, memory, I/O
- Auto-scaling triggers based on metrics
- Cost monitoring per pipeline or job
- Alert fatigue reduction with intelligent routing
- Dashboards for operational visibility
Module 8: Scalability Patterns for Modern Warehousing - Separation of compute and storage
- Automatic scaling of query engines
- Workload management with queues and pools
- Cost controls for runaway queries
- Resource monitoring and utilisation alerts
- Query optimisation: predicate pushdown, pruning
- Materialised views and incrementality
- Incremental data loading with change data capture
- Change Data Capture: log-based vs. trigger-based
- Tracking deletions in incremental loads
- Slowly Changing Dimensions (SCD) Types 1–4
- SCD Type 6: hybrid approach implementation
- Upsert patterns with MERGE statements
- Indexing strategies in cloud data warehouses
- Partitioning large fact tables
- Clustering for query performance
- Query history analysis for tuning
- Cost attribution by team or project
- Role-based access and data masking
- Row-level security policies
Module 9: Data Mesh & Decentralised Architecture - Domain-driven data ownership principles
- Defining data products as first-class citizens
- Self-serve data platforms and infrastructure
- Contract-first development with data APIs
- Schema as code and versioned contracts
- Federated governance models
- Central standards with local autonomy
- Data product discovery with catalogues
- Tagging, documentation, and ownership metadata
- Distributed testing and CI/CD for data products
- Automated compliance checks in pipelines
- Monitoring SLAs across teams
- Chargeback and showback models
- Cost transparency for data consumers
- API gateways for data access
- GraphQL for flexible data querying
- REST vs. gRPC for data services
- Authentication for data product APIs
- Audit logging for access tracking
- Service level objectives for data freshness
Module 10: Resiliency & Disaster Recovery - Designing for failure: assume everything breaks
- Retry patterns with jitter and backoff
- Circuit breaker implementation with fallbacks
- Failover strategies for primary-secondary systems
- Active-active vs. active-passive deployments
- Cross-region replication of critical data
- Automated switchover testing schedules
- Backup strategies: full, incremental, differential
- Point-in-time recovery planning
- Recovery Time Objective (RTO) definition
- Recovery Point Objective (RPO) alignment
- Testing disaster recovery runbooks
- Backup validation with automated restore tests
- Data consistency checks post-recovery
- Immutable backups to prevent tampering
- Retention policies for compliance
- Monitoring backup completion and integrity
- Automated alerts for failed backups
- Secure key management for encrypted backups
- Incident response playbooks for data outages
Module 11: Cost Optimisation at Scale - Unit economics of data processing
- Cost per GB ingested, stored, queried
- Identifying cost outliers in pipelines
- Right-sizing compute clusters
- Auto-pausing and auto-resuming clusters
- Spot instances and preemptible VMs for batch jobs
- Data compression strategies and savings impact
- Tiered storage cost models
- Archiving older data to low-cost storage
- Query optimisation to reduce scanned data
- Materialised aggregations for expensive queries
- Query caching and result reuse
- Cost attribution by team, project, or pipeline
- Budget alerts and spending caps
- Cost allocation tags and naming conventions
- Monitoring tools for cloud spend
- Negotiating reserved capacity discounts
- Using query profiles to detect inefficiencies
- Automated cost reporting and dashboards
- Cost-aware development practices
Module 12: Implementation Roadmap & Real-World Projects - Assessing current system maturity
- Gap analysis against scalable patterns
- Prioritisation framework: impact vs. effort
- Building a phased rollout plan
- Risk assessment for pattern adoption
- Stakeholder communication strategy
- Change management for engineering teams
- Creating a board-ready architecture proposal
- Documenting design decisions with ADRs
- Architecture Decision Records (ADRs) best practices
- Presenting technical trade-offs to executives
- Visualising architecture with diagrams
- Using C4 model for system visualisation
- Creating component and container diagrams
- Defining success metrics for implementation
- Setting KPIs for scalability and reliability
- Running a pilot project with measurable outcomes
- Gathering feedback from users and teams
- Scaling the pattern enterprise-wide
- Continuous improvement with retrospectives
Module 13: Certification, Career Advancement & Next Steps - Reviewing core design patterns for mastery
- Self-assessment checklist for pattern application
- Preparing your Certificate of Completion project submission
- Documentation standards for professional review
- How to present your completed architecture proposal
- Adding the credential to LinkedIn and CVs
- Using the certification in promotion discussions
- Negotiating higher compensation with proven skills
- Becoming the go-to architect in your organisation
- Mentoring junior engineers using design patterns
- Contributing to internal design councils
- Speaking at tech talks with confidence
- Building a personal brand as a data systems expert
- Contributing to open-source data projects
- Staying updated with evolving patterns
- Accessing future course updates and community forums
- Real-time notifications for new pattern releases
- Exclusive access to advanced pattern libraries
- Lifetime updates to the curriculum
- Navigating the next career level with clarity and proof
- Domain-driven data ownership principles
- Defining data products as first-class citizens
- Self-serve data platforms and infrastructure
- Contract-first development with data APIs
- Schema as code and versioned contracts
- Federated governance models
- Central standards with local autonomy
- Data product discovery with catalogues
- Tagging, documentation, and ownership metadata
- Distributed testing and CI/CD for data products
- Automated compliance checks in pipelines
- Monitoring SLAs across teams
- Chargeback and showback models
- Cost transparency for data consumers
- API gateways for data access
- GraphQL for flexible data querying
- REST vs. gRPC for data services
- Authentication for data product APIs
- Audit logging for access tracking
- Service level objectives for data freshness
Module 10: Resiliency & Disaster Recovery - Designing for failure: assume everything breaks
- Retry patterns with jitter and backoff
- Circuit breaker implementation with fallbacks
- Failover strategies for primary-secondary systems
- Active-active vs. active-passive deployments
- Cross-region replication of critical data
- Automated switchover testing schedules
- Backup strategies: full, incremental, differential
- Point-in-time recovery planning
- Recovery Time Objective (RTO) definition
- Recovery Point Objective (RPO) alignment
- Testing disaster recovery runbooks
- Backup validation with automated restore tests
- Data consistency checks post-recovery
- Immutable backups to prevent tampering
- Retention policies for compliance
- Monitoring backup completion and integrity
- Automated alerts for failed backups
- Secure key management for encrypted backups
- Incident response playbooks for data outages
Module 11: Cost Optimisation at Scale - Unit economics of data processing
- Cost per GB ingested, stored, queried
- Identifying cost outliers in pipelines
- Right-sizing compute clusters
- Auto-pausing and auto-resuming clusters
- Spot instances and preemptible VMs for batch jobs
- Data compression strategies and savings impact
- Tiered storage cost models
- Archiving older data to low-cost storage
- Query optimisation to reduce scanned data
- Materialised aggregations for expensive queries
- Query caching and result reuse
- Cost attribution by team, project, or pipeline
- Budget alerts and spending caps
- Cost allocation tags and naming conventions
- Monitoring tools for cloud spend
- Negotiating reserved capacity discounts
- Using query profiles to detect inefficiencies
- Automated cost reporting and dashboards
- Cost-aware development practices
Module 12: Implementation Roadmap & Real-World Projects - Assessing current system maturity
- Gap analysis against scalable patterns
- Prioritisation framework: impact vs. effort
- Building a phased rollout plan
- Risk assessment for pattern adoption
- Stakeholder communication strategy
- Change management for engineering teams
- Creating a board-ready architecture proposal
- Documenting design decisions with ADRs
- Architecture Decision Records (ADRs) best practices
- Presenting technical trade-offs to executives
- Visualising architecture with diagrams
- Using C4 model for system visualisation
- Creating component and container diagrams
- Defining success metrics for implementation
- Setting KPIs for scalability and reliability
- Running a pilot project with measurable outcomes
- Gathering feedback from users and teams
- Scaling the pattern enterprise-wide
- Continuous improvement with retrospectives
Module 13: Certification, Career Advancement & Next Steps - Reviewing core design patterns for mastery
- Self-assessment checklist for pattern application
- Preparing your Certificate of Completion project submission
- Documentation standards for professional review
- How to present your completed architecture proposal
- Adding the credential to LinkedIn and CVs
- Using the certification in promotion discussions
- Negotiating higher compensation with proven skills
- Becoming the go-to architect in your organisation
- Mentoring junior engineers using design patterns
- Contributing to internal design councils
- Speaking at tech talks with confidence
- Building a personal brand as a data systems expert
- Contributing to open-source data projects
- Staying updated with evolving patterns
- Accessing future course updates and community forums
- Real-time notifications for new pattern releases
- Exclusive access to advanced pattern libraries
- Lifetime updates to the curriculum
- Navigating the next career level with clarity and proof
- Unit economics of data processing
- Cost per GB ingested, stored, queried
- Identifying cost outliers in pipelines
- Right-sizing compute clusters
- Auto-pausing and auto-resuming clusters
- Spot instances and preemptible VMs for batch jobs
- Data compression strategies and savings impact
- Tiered storage cost models
- Archiving older data to low-cost storage
- Query optimisation to reduce scanned data
- Materialised aggregations for expensive queries
- Query caching and result reuse
- Cost attribution by team, project, or pipeline
- Budget alerts and spending caps
- Cost allocation tags and naming conventions
- Monitoring tools for cloud spend
- Negotiating reserved capacity discounts
- Using query profiles to detect inefficiencies
- Automated cost reporting and dashboards
- Cost-aware development practices
Module 12: Implementation Roadmap & Real-World Projects - Assessing current system maturity
- Gap analysis against scalable patterns
- Prioritisation framework: impact vs. effort
- Building a phased rollout plan
- Risk assessment for pattern adoption
- Stakeholder communication strategy
- Change management for engineering teams
- Creating a board-ready architecture proposal
- Documenting design decisions with ADRs
- Architecture Decision Records (ADRs) best practices
- Presenting technical trade-offs to executives
- Visualising architecture with diagrams
- Using C4 model for system visualisation
- Creating component and container diagrams
- Defining success metrics for implementation
- Setting KPIs for scalability and reliability
- Running a pilot project with measurable outcomes
- Gathering feedback from users and teams
- Scaling the pattern enterprise-wide
- Continuous improvement with retrospectives
Module 13: Certification, Career Advancement & Next Steps - Reviewing core design patterns for mastery
- Self-assessment checklist for pattern application
- Preparing your Certificate of Completion project submission
- Documentation standards for professional review
- How to present your completed architecture proposal
- Adding the credential to LinkedIn and CVs
- Using the certification in promotion discussions
- Negotiating higher compensation with proven skills
- Becoming the go-to architect in your organisation
- Mentoring junior engineers using design patterns
- Contributing to internal design councils
- Speaking at tech talks with confidence
- Building a personal brand as a data systems expert
- Contributing to open-source data projects
- Staying updated with evolving patterns
- Accessing future course updates and community forums
- Real-time notifications for new pattern releases
- Exclusive access to advanced pattern libraries
- Lifetime updates to the curriculum
- Navigating the next career level with clarity and proof
- Reviewing core design patterns for mastery
- Self-assessment checklist for pattern application
- Preparing your Certificate of Completion project submission
- Documentation standards for professional review
- How to present your completed architecture proposal
- Adding the credential to LinkedIn and CVs
- Using the certification in promotion discussions
- Negotiating higher compensation with proven skills
- Becoming the go-to architect in your organisation
- Mentoring junior engineers using design patterns
- Contributing to internal design councils
- Speaking at tech talks with confidence
- Building a personal brand as a data systems expert
- Contributing to open-source data projects
- Staying updated with evolving patterns
- Accessing future course updates and community forums
- Real-time notifications for new pattern releases
- Exclusive access to advanced pattern libraries
- Lifetime updates to the curriculum
- Navigating the next career level with clarity and proof