Mastering Data Engineering in the Age of Real-Time Analytics
You're under pressure. Systems are bloating. Pipelines are failing at peak loads. Stakeholders demand insights now, not next week. And if you're like most data engineers, you're caught between legacy architectures and next-gen expectations, trying to future-proof infrastructure while keeping the lights on. The industry has shifted. Real-time isn't a luxury - it's the baseline. Companies that move fast on live data outperform, outscale, and out-innovate. But traditional training hasn't kept pace. You're left filling gaps alone, reverse-engineering solutions from fragmented blogs and outdated documentation, burning time you don't have. That ends here. Mastering Data Engineering in the Age of Real-Time Analytics is not another theory dump. It’s a battle-tested blueprint for engineers who need to design, implement, and govern high-throughput, low-latency data systems that hold up under real production stress - and deliver quantifiable business impact. One recent learner, Mira T., Senior Data Engineer at a global fintech, used the pipeline optimization framework inside this course to cut latency by 68% across critical fraud detection streams. Her design was fast-tracked to board review and became a cornerstone of their new real-time risk platform - and she was promoted three months later. This program takes you from overwhelmed and reactive to architect-level confidence in 30 days. You’ll build a production-grade real-time analytics solution from concept to deployment, complete with documentation, monitoring, and governance - a board-ready artifact that proves your mastery. No fluff. No filler. Just precision. Here’s how this course is structured to help you get there.Course Format & Delivery Details This is a self-paced, on-demand learning experience designed for working professionals who need maximum flexibility and immediate application. You gain full access to the complete curriculum the moment your enrollment is confirmed, with no fixed start dates, no weekly commitments, and no arbitrary deadlines. Immediate, Lifetime Access with Zero Time Pressure
Once your enrollment is processed, you’ll receive a confirmation email followed by access details to your full course portal. All materials are available for immediate use, and you retain lifetime access - including all future updates at no extra cost. The field evolves, and so does your training. Whether you’re learning between shifts, on weekends, or during a career transition, you control the pace. Most learners complete the core project in 25–30 hours, with many reporting functional pipeline builds within the first 10 hours. Real results, fast. Engineered for Real-World Use Across Devices and Time Zones
Access your course anytime, anywhere. Our platform is 24/7, globally available, and fully mobile-friendly. Study on your commute. Review architecture checklists during lunch. Refactor pipeline logic on your tablet. The system adapts to you, not the other way around. All exercises, templates, and reference guides are device-agnostic and optimized for readability across screen sizes - because learning shouldn’t depend on your location or device. Direct Support from Industry-Tested Data Architects
You’re not navigating this alone. Throughout the course, you have direct access to a team of senior data engineering mentors with 15+ years of experience across Fortune 500s, high-growth startups, and scale-ups. Ask strategic questions, get feedback on your architecture diagrams, and receive actionable guidance on implementation challenges. Support is provided via secure messaging within the course platform, with response times averaging under 12 business hours - a safety net most self-study paths simply don’t offer. Issuer of Certification: The Art of Service
Upon project completion, you’ll earn a verifiable Certificate of Completion issued by The Art of Service - a globally recognized credential trusted by hiring managers at AWS, Google Cloud, and enterprise data leaders. This isn’t a participation badge. It’s proof of applied, real-world capability. The certificate includes a unique verification ID, project summary, and technical scope - making it ideal for LinkedIn, resumes, and promotion dossiers. No Hidden Fees. No Fine Print. Just Straightforward Value.
You pay one clear price. No monthly subscriptions. No upsells. No charges for updates. No premium tiers. What you see is what you get - and you get full access from day one. Secure checkout accepts all major payment methods, including Visa, Mastercard, and PayPal. Zero-Risk Enrollment: 30-Day Satisfied or Refunded Guarantee
We stand behind the results. If at any point within 30 days you feel this course isn’t delivering transformative value, contact us for a full refund. No questions. No hassle. This is not just a promise - it’s risk reversal. You win. You only invest if you gain. “Will This Work for Me?” - Here’s Why It Will, Even If…
- You’re not currently working in a real-time environment - this course equips you with the skills to lead that transformation
- You're transitioning from batch processing - we bridge the mental and technical shift with step-by-step comparisons and migration paths
- You don’t have admin access to cloud infrastructure - every project includes local emulation, Docker-based testing, and sandbox deployment options
- You’re unsure about your coding level - all code is provided, annotated, and modular, with beginner ramp-ups and advanced extensions
This isn't theoretical. It's structured so that even if you start behind, you finish ahead. You'll build, test, and document systems that mirror what’s in demand at top-tier data-driven organizations - because the content was developed and validated by engineers who’ve deployed at petabyte scale. Your success isn't left to chance. Every component is tested for clarity, repeatability, and industry relevance. This is training built for certainty - not speculation.
Module 1: Foundations of Real-Time Data Systems - Understanding the shift from batch to real-time data processing
- Defining real-time: low latency vs true streaming
- Business drivers for real-time analytics adoption
- Common use cases across finance, retail, healthcare, and IoT
- Architecture principles: throughput, durability, and scalability
- Event-driven architecture vs request-response models
- Data freshness, ordering, and consistency trade-offs
- Latency SLAs and service-level expectations
- Comparing micro-batch vs true streaming frameworks
- Designing for fault tolerance and idempotency from day one
- Key terminology: events, streams, producers, consumers, topics
- Understanding message brokers and their role in real-time systems
- Backpressure and flow control in high-velocity environments
- Time semantics: event time, ingestion time, processing time
- Stateful processing fundamentals and use cases
Module 2: Core Architectural Frameworks and Design Patterns - The Lambda Architecture: strengths, limitations, and evolution
- Kappa Architecture: simplicity and real-time focus
- Delta Architecture: unified batch and streaming with Lakehouse
- Event sourcing pattern and its application in data engineering
- Command Query Responsibility Segregation (CQRS) in analytics
- Streaming ETL vs batch ETL: timing, triggers, and consistency
- Change Data Capture (CDC) strategies for real-time sync
- Native change data capture with PostgreSQL, MySQL, Oracle
- Log-based vs trigger-based CDC: performance and reliability
- Schema evolution and compatibility management
- Backfilling strategies in real-time systems
- Reprocessing pipelines and data correction workflows
- Exactly-once, at-least-once, at-most-once delivery semantics
- Idempotent design for safe retries and reprocessing
- Watermarking for late-arriving data handling
Module 3: Streaming Platforms and Message Brokers - Apache Kafka architecture and core components
- Kafka topics, partitions, and replication mechanics
- Producer configuration: acks, retries, batching
- Consumer groups and offset management
- Compacted topics for state retention
- Kafka Connect: source and sink connectors
- Kafka Streams API for lightweight real-time processing
- ksqlDB for SQL-based stream processing
- Deploying Kafka on-premise vs cloud-managed services
- Amazon MSK, Confluent Cloud, and self-hosted trade-offs
- Apache Pulsar: architecture and segment distribution
- Pulsar vs Kafka: feature, performance, and operational differences
- RabbitMQ for lightweight messaging and fan-out patterns
- NATS and NATS JetStream for high-throughput pub/sub
- Google Pub/Sub: regional vs zonal, ordering guarantees
- Azure Event Hubs and integration with Azure services
- Message serialization formats: JSON, Avro, Protobuf, Parquet
- Schema Registry implementation with Confluent and Apicurio
- Event mesh patterns and multi-cluster communication
- Monitoring message broker health and performance
Module 4: Stream Processing Engines and Compute Frameworks - Apache Flink: core architecture and time processing
- Flink windows: tumbling, sliding, session, and global
- State backends: memory, filesystem, RocksDB
- Checkpointing and savepoints for fault tolerance
- Event time processing and watermarks in Flink
- Processing functions: Map, Filter, KeyBy, Co-grouping
- Side outputs and broadcast streams
- Apache Spark Streaming: DStreams vs Structured Streaming
- Micro-batch model and trade-offs
- Structured Streaming with watermarking and aggregation
- Streaming joins: streaming-static, streaming-streaming
- Triggers and output modes in streaming queries
- Apache Storm: topology development and spouts/bolts
- Heron as a modern Storm replacement
- Google Dataflow and Beam SDK for portable pipelines
- Beam runners: local, Dataflow, Flink, Spark
- Writing cross-platform streaming code with Beam
- AWS Kinesis Data Analytics with Flink
- Azure Stream Analytics query language and deployment
- Custom window logic and sessionization techniques
Module 5: Real-Time Data Storage and Serving Systems - Choosing databases for real-time workloads: OLTP vs OLAP
- Columnar storage for analytical workloads
- Data lakes vs data warehouses vs lakehouses
- S3, ADLS, and GCS as real-time ingestion targets
- Delta Lake: ACID transactions, schema enforcement
- Apache Iceberg: table format design and metadata layers
- Apache Hudi: copy-on-write vs merge-on-read
- Serving layers: speed, serving, and batch tiers
- AWS DynamoDB for real-time lookups and joins
- Google Bigtable and Cloud Bigtable use cases
- Azure Cosmos DB and multi-region consistency
- Redis as a real-time cache and state store
- Redis Streams and consumer groups
- TimescaleDB for time-series data and continuous aggregates
- ClickHouse for ultra-fast analytical queries
- DuckDB for embedded real-time analytics
- Elasticsearch for real-time search and log analytics
- Materialized views and pre-aggregation strategies
- Star, snowflake, and wide-column schema patterns
- Handling slowly changing dimensions in streaming
Module 6: Real-Time Data Integration and Ingestion - Designing ingestion pipelines for velocity and volume
- Batch ingestion vs streaming ingestion patterns
- File-based ingestion with monitoring and validation
- APIs as real-time data sources: polling vs webhook
- Webhook integration with authentication and retry
- Database replication tools: Debezium, Maxwell, pg_recvlogical
- Setting up Debezium with Kafka Connect
- Monitoring CDC latency and error rates
- IOT and sensor data ingestion at scale
- Log and metrics ingestion with Fluentd, Logstash, Vector
- Handling structured, semi-structured, and unstructured data
- Validating incoming data: schemas, types, null checks
- Reject queues and dead-letter topics for error handling
- Rate limiting and throttling strategies
- Data shaping and transformation at ingestion point
- Header enrichment and context injection
- Handling time zone and locale-sensitive data
- File formats for real-time: Avro, Parquet, ORC, JSONL
- Compression techniques: Snappy, Zstandard, GZIP
- Batch size tuning for optimal throughput
Module 7: Real-Time Transformation and Processing Logic - Stateless transformations: filtering, mapping, enriching
- Stateful processing: sessions, aggregations, counters
- Cross-stream enrichment with lookup tables
- Joining streaming data with static reference data
- Temporal joins in Flink and Spark
- Enriching streams with geolocation, customer profile, risk score
- Computing real-time aggregates: sum, count, average, percentile
- Sliding window counts and rate calculations
- Sessionization: detecting user journeys in real-time
- Anomaly detection in streams: thresholds, z-scores, moving averages
- Real-time data masking and PII redaction
- Tokenization and encryption at processing time
- Dynamic filtering and routing based on content
- Batch reprocessing of corrected logic
- Versioning processing logic and managing rollbacks
- Feature engineering in streaming for ML pipelines
- Real-time A/B test data routing and aggregation
- Handling duplicates and ensuring data quality
- Idempotent sinks for safe writes
- Null handling and default fallback strategies
Module 8: Monitoring, Observability, and Alerting - Key metrics for real-time pipelines: latency, throughput, errors
- End-to-end latency measurement techniques
- Monitoring consumer lag in Kafka and Pulsar
- Instrumenting custom metrics in Flink and Spark
- Logging best practices for streaming applications
- Structured logging with JSON and context tagging
- Centralized logging with ELK or Grafana Loki
- Distributed tracing in microservices with Jaeger, Zipkin
- Correlating events across services using trace IDs
- Setting up dashboards with Grafana and Prometheus
- Creating meaningful alerts: avoiding noise and false positives
- Alerting on backpressure, memory usage, and GC pauses
- Health checks and readiness probes
- Automated recovery workflows and self-healing pipelines
- SLA tracking and incident response integration
- Audit trails for data lineage and compliance
- Automated pipeline documentation and metadata capture
- Using OpenTelemetry for unified observability
- Cost monitoring for cloud-based streaming systems
- Capacity planning and resource forecasting
Module 9: Scalability, Performance, and Optimization - Partitioning strategies: key-based, round-robin, custom
- Choosing optimal partition counts for throughput
- Handling skewed data and hot partitions
- Repartitioning and rebalancing in streaming
- Parallelism tuning in Flink, Spark, and Kafka
- Task slots, task managers, and executors configuration
- Memory management and off-heap storage
- JVM tuning for low GC pause and high throughput
- Buffer sizing and spill to disk thresholds
- Thread model and asynchronous I/O handling
- Backpressure handling in the producer-consumer chain
- Dynamic scaling with Kubernetes and KEDA
- Auto-scaling based on lag, CPU, or memory
- Cost-performance trade-offs in cloud deployments
- Spot instances and preemptible VMs for savings
- Optimizing serialization and deserialization cost
- Batching strategies for sink operations
- Connection pooling for databases and external systems
- Network optimization: compression, batching, retries
- Performance benchmarking and load testing
Module 10: Testing, Validation, and Quality Assurance - Unit testing streaming processors with TestContext
- Mocking sources and sinks for isolated testing
- Test harnesses in Flink and Spark for pipeline simulation
- Generating synthetic data with controlled variability
- Chaos engineering for pipeline resilience testing
- Simulating network partitions and broker failures
- Golden dataset comparison and regression testing
- Schema validation using JSON Schema, Avro, and Protobuf
- Null and outlier detection in data streams
- Completeness checks: expected record counts and types
- Timeliness validation: SLA adherence testing
- Accuracy validation through cross-system reconciliation
- Reprocessing validation and idempotency checks
- Canary deployments for new pipeline versions
- Blue-green and rolling updates for zero downtime
- A/B testing pipeline logic with split traffic
- Validating retention and cleanup policies
- Performance regression testing across versions
- Automating test suites with CI/CD pipelines
- Test coverage reporting and quality gates
Module 11: Security, Compliance, and Governance - Authentication: SASL, OAuth, mTLS for Kafka and Pulsar
- Authorization: ACLs, RBAC, and least privilege access
- Encryption: in-transit with TLS, at-rest with KMS
- Role-based access control for data assets
- Data masking and dynamic filtering by user context
- PII detection and automated classification
- GDPR, CCPA, HIPAA compliance in real-time data
- Right to be forgotten and data deletion workflows
- Automated data retention and lifecycle policies
- Data lineage capture and visualization
- Metadata tagging and business glossary integration
- Data ownership and stewardship models
- Policy enforcement with Open Policy Agent
- Secure pipeline deployment with Infrastructure as Code
- Secrets management with HashiCorp Vault, AWS Secrets Manager
- Network security: VPC, firewalls, private endpoints
- Audit logging and activity tracking
- Incident response planning for data breaches
- Compliance reporting and certification prep
- Zero-trust architecture principles in data pipelines
Module 12: Deployment, CI/CD, and Infrastructure as Code - Containerizing streaming applications with Docker
- Orchestrating with Kubernetes and Helm charts
- Kubernetes operators for Flink and Kafka
- Terraform for provisioning cloud resources
- Managing Kafka clusters with Terraform and Pulumi
- Automating Flink job deployment with CI/CD
- GitOps workflows for pipeline versioning
- CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins
- Environment promotion: dev, staging, prod
- Immutable deployment artifacts and version control
- Rollback procedures and configuration drift prevention
- Parameterization and environment-specific settings
- Dependency management for Python, Java, Scala code
- Artifact storage with Nexus, Artifactory, or S3
- Automated smoke tests post-deployment
- Health check integration with service meshes
- Blue-green and canary deployments for zero downtime
- Feature flags for gradual rollout of new logic
- Infrastructure cost tagging and accountability
- Policy-as-code with Open Policy Agent and Sentinel
Module 13: Real-Time Analytics Applications and Use Cases - Fraud detection pipelines with real-time scoring
- User activity monitoring and behavioral analytics
- Real-time dashboards with live data updates
- Personalization engines and recommendation systems
- Supply chain and logistics tracking in real-time
- IoT telemetry processing and alerting
- Predictive maintenance with sensor data
- Real-time inventory and pricing updates
- Clickstream analysis for digital analytics
- Ad tech: bid request processing and auction systems
- Automated trading and market data feeds
- Customer 360 view with real-time updates
- Compliance monitoring and regulatory alerts
- Network performance monitoring and diagnostics
- Energy grid monitoring and anomaly detection
- Healthcare: real-time patient vitals and alerts
- Cross-border payment tracking and AML flags
- Geofencing and proximity-based triggers
- Dynamic pricing and surge detection
- Real-time sentiment analysis from social feeds
Module 14: Building Your Real-Time Data Solution: Capstone Project - Selecting a real-world use case for your project
- Defining success criteria and KPIs
- Designing the end-to-end architecture
- Creating data flow diagrams and component mapping
- Selecting appropriate technologies based on constraints
- Setting up local development environment with Docker
- Implementing CDC with Debezium and PostgreSQL
- Streaming data into Kafka with proper partitioning
- Processing events with Flink for aggregation and enrichment
- Writing results to Delta Lake with schema evolution
- Building real-time dashboards with Grafana
- Implementing monitoring and alerting
- Adding security: TLS, SASL, and ACLs
- Validating data quality and consistency
- Documenting the system design and trade-offs
- Writing a deployment runbook
- Creating a presentation for technical and business stakeholders
- Review checklist for production readiness
- Submission requirements for certification
- Feedback and improvement plan from mentors
Module 15: Certification, Career Advancement, and Next Steps - Requirements for earning the Certificate of Completion
- Submitting your capstone project for review
- Mentor feedback and revision process
- Receiving your official credential from The Art of Service
- Verifiable certificate with unique ID and project scope
- Adding the certification to LinkedIn and resumes
- Tailoring your portfolio for data engineering roles
- Highlighting real-time expertise in job applications
- Preparing for technical interviews with real-time scenarios
- Common real-time data engineering interview questions
- Whiteboard design: building a real-time fraud system
- Talks and presentations to showcase your knowledge
- Contributing to open source streaming projects
- Joining data engineering communities and forums
- Staying current with emerging tools and trends
- Transitioning to senior or lead data engineering roles
- Architecting enterprise-wide real-time platforms
- Mentoring other engineers and leading initiatives
- Lifetime access to updates and alumni resources
- Ongoing support and career guidance after completion
- Understanding the shift from batch to real-time data processing
- Defining real-time: low latency vs true streaming
- Business drivers for real-time analytics adoption
- Common use cases across finance, retail, healthcare, and IoT
- Architecture principles: throughput, durability, and scalability
- Event-driven architecture vs request-response models
- Data freshness, ordering, and consistency trade-offs
- Latency SLAs and service-level expectations
- Comparing micro-batch vs true streaming frameworks
- Designing for fault tolerance and idempotency from day one
- Key terminology: events, streams, producers, consumers, topics
- Understanding message brokers and their role in real-time systems
- Backpressure and flow control in high-velocity environments
- Time semantics: event time, ingestion time, processing time
- Stateful processing fundamentals and use cases
Module 2: Core Architectural Frameworks and Design Patterns - The Lambda Architecture: strengths, limitations, and evolution
- Kappa Architecture: simplicity and real-time focus
- Delta Architecture: unified batch and streaming with Lakehouse
- Event sourcing pattern and its application in data engineering
- Command Query Responsibility Segregation (CQRS) in analytics
- Streaming ETL vs batch ETL: timing, triggers, and consistency
- Change Data Capture (CDC) strategies for real-time sync
- Native change data capture with PostgreSQL, MySQL, Oracle
- Log-based vs trigger-based CDC: performance and reliability
- Schema evolution and compatibility management
- Backfilling strategies in real-time systems
- Reprocessing pipelines and data correction workflows
- Exactly-once, at-least-once, at-most-once delivery semantics
- Idempotent design for safe retries and reprocessing
- Watermarking for late-arriving data handling
Module 3: Streaming Platforms and Message Brokers - Apache Kafka architecture and core components
- Kafka topics, partitions, and replication mechanics
- Producer configuration: acks, retries, batching
- Consumer groups and offset management
- Compacted topics for state retention
- Kafka Connect: source and sink connectors
- Kafka Streams API for lightweight real-time processing
- ksqlDB for SQL-based stream processing
- Deploying Kafka on-premise vs cloud-managed services
- Amazon MSK, Confluent Cloud, and self-hosted trade-offs
- Apache Pulsar: architecture and segment distribution
- Pulsar vs Kafka: feature, performance, and operational differences
- RabbitMQ for lightweight messaging and fan-out patterns
- NATS and NATS JetStream for high-throughput pub/sub
- Google Pub/Sub: regional vs zonal, ordering guarantees
- Azure Event Hubs and integration with Azure services
- Message serialization formats: JSON, Avro, Protobuf, Parquet
- Schema Registry implementation with Confluent and Apicurio
- Event mesh patterns and multi-cluster communication
- Monitoring message broker health and performance
Module 4: Stream Processing Engines and Compute Frameworks - Apache Flink: core architecture and time processing
- Flink windows: tumbling, sliding, session, and global
- State backends: memory, filesystem, RocksDB
- Checkpointing and savepoints for fault tolerance
- Event time processing and watermarks in Flink
- Processing functions: Map, Filter, KeyBy, Co-grouping
- Side outputs and broadcast streams
- Apache Spark Streaming: DStreams vs Structured Streaming
- Micro-batch model and trade-offs
- Structured Streaming with watermarking and aggregation
- Streaming joins: streaming-static, streaming-streaming
- Triggers and output modes in streaming queries
- Apache Storm: topology development and spouts/bolts
- Heron as a modern Storm replacement
- Google Dataflow and Beam SDK for portable pipelines
- Beam runners: local, Dataflow, Flink, Spark
- Writing cross-platform streaming code with Beam
- AWS Kinesis Data Analytics with Flink
- Azure Stream Analytics query language and deployment
- Custom window logic and sessionization techniques
Module 5: Real-Time Data Storage and Serving Systems - Choosing databases for real-time workloads: OLTP vs OLAP
- Columnar storage for analytical workloads
- Data lakes vs data warehouses vs lakehouses
- S3, ADLS, and GCS as real-time ingestion targets
- Delta Lake: ACID transactions, schema enforcement
- Apache Iceberg: table format design and metadata layers
- Apache Hudi: copy-on-write vs merge-on-read
- Serving layers: speed, serving, and batch tiers
- AWS DynamoDB for real-time lookups and joins
- Google Bigtable and Cloud Bigtable use cases
- Azure Cosmos DB and multi-region consistency
- Redis as a real-time cache and state store
- Redis Streams and consumer groups
- TimescaleDB for time-series data and continuous aggregates
- ClickHouse for ultra-fast analytical queries
- DuckDB for embedded real-time analytics
- Elasticsearch for real-time search and log analytics
- Materialized views and pre-aggregation strategies
- Star, snowflake, and wide-column schema patterns
- Handling slowly changing dimensions in streaming
Module 6: Real-Time Data Integration and Ingestion - Designing ingestion pipelines for velocity and volume
- Batch ingestion vs streaming ingestion patterns
- File-based ingestion with monitoring and validation
- APIs as real-time data sources: polling vs webhook
- Webhook integration with authentication and retry
- Database replication tools: Debezium, Maxwell, pg_recvlogical
- Setting up Debezium with Kafka Connect
- Monitoring CDC latency and error rates
- IOT and sensor data ingestion at scale
- Log and metrics ingestion with Fluentd, Logstash, Vector
- Handling structured, semi-structured, and unstructured data
- Validating incoming data: schemas, types, null checks
- Reject queues and dead-letter topics for error handling
- Rate limiting and throttling strategies
- Data shaping and transformation at ingestion point
- Header enrichment and context injection
- Handling time zone and locale-sensitive data
- File formats for real-time: Avro, Parquet, ORC, JSONL
- Compression techniques: Snappy, Zstandard, GZIP
- Batch size tuning for optimal throughput
Module 7: Real-Time Transformation and Processing Logic - Stateless transformations: filtering, mapping, enriching
- Stateful processing: sessions, aggregations, counters
- Cross-stream enrichment with lookup tables
- Joining streaming data with static reference data
- Temporal joins in Flink and Spark
- Enriching streams with geolocation, customer profile, risk score
- Computing real-time aggregates: sum, count, average, percentile
- Sliding window counts and rate calculations
- Sessionization: detecting user journeys in real-time
- Anomaly detection in streams: thresholds, z-scores, moving averages
- Real-time data masking and PII redaction
- Tokenization and encryption at processing time
- Dynamic filtering and routing based on content
- Batch reprocessing of corrected logic
- Versioning processing logic and managing rollbacks
- Feature engineering in streaming for ML pipelines
- Real-time A/B test data routing and aggregation
- Handling duplicates and ensuring data quality
- Idempotent sinks for safe writes
- Null handling and default fallback strategies
Module 8: Monitoring, Observability, and Alerting - Key metrics for real-time pipelines: latency, throughput, errors
- End-to-end latency measurement techniques
- Monitoring consumer lag in Kafka and Pulsar
- Instrumenting custom metrics in Flink and Spark
- Logging best practices for streaming applications
- Structured logging with JSON and context tagging
- Centralized logging with ELK or Grafana Loki
- Distributed tracing in microservices with Jaeger, Zipkin
- Correlating events across services using trace IDs
- Setting up dashboards with Grafana and Prometheus
- Creating meaningful alerts: avoiding noise and false positives
- Alerting on backpressure, memory usage, and GC pauses
- Health checks and readiness probes
- Automated recovery workflows and self-healing pipelines
- SLA tracking and incident response integration
- Audit trails for data lineage and compliance
- Automated pipeline documentation and metadata capture
- Using OpenTelemetry for unified observability
- Cost monitoring for cloud-based streaming systems
- Capacity planning and resource forecasting
Module 9: Scalability, Performance, and Optimization - Partitioning strategies: key-based, round-robin, custom
- Choosing optimal partition counts for throughput
- Handling skewed data and hot partitions
- Repartitioning and rebalancing in streaming
- Parallelism tuning in Flink, Spark, and Kafka
- Task slots, task managers, and executors configuration
- Memory management and off-heap storage
- JVM tuning for low GC pause and high throughput
- Buffer sizing and spill to disk thresholds
- Thread model and asynchronous I/O handling
- Backpressure handling in the producer-consumer chain
- Dynamic scaling with Kubernetes and KEDA
- Auto-scaling based on lag, CPU, or memory
- Cost-performance trade-offs in cloud deployments
- Spot instances and preemptible VMs for savings
- Optimizing serialization and deserialization cost
- Batching strategies for sink operations
- Connection pooling for databases and external systems
- Network optimization: compression, batching, retries
- Performance benchmarking and load testing
Module 10: Testing, Validation, and Quality Assurance - Unit testing streaming processors with TestContext
- Mocking sources and sinks for isolated testing
- Test harnesses in Flink and Spark for pipeline simulation
- Generating synthetic data with controlled variability
- Chaos engineering for pipeline resilience testing
- Simulating network partitions and broker failures
- Golden dataset comparison and regression testing
- Schema validation using JSON Schema, Avro, and Protobuf
- Null and outlier detection in data streams
- Completeness checks: expected record counts and types
- Timeliness validation: SLA adherence testing
- Accuracy validation through cross-system reconciliation
- Reprocessing validation and idempotency checks
- Canary deployments for new pipeline versions
- Blue-green and rolling updates for zero downtime
- A/B testing pipeline logic with split traffic
- Validating retention and cleanup policies
- Performance regression testing across versions
- Automating test suites with CI/CD pipelines
- Test coverage reporting and quality gates
Module 11: Security, Compliance, and Governance - Authentication: SASL, OAuth, mTLS for Kafka and Pulsar
- Authorization: ACLs, RBAC, and least privilege access
- Encryption: in-transit with TLS, at-rest with KMS
- Role-based access control for data assets
- Data masking and dynamic filtering by user context
- PII detection and automated classification
- GDPR, CCPA, HIPAA compliance in real-time data
- Right to be forgotten and data deletion workflows
- Automated data retention and lifecycle policies
- Data lineage capture and visualization
- Metadata tagging and business glossary integration
- Data ownership and stewardship models
- Policy enforcement with Open Policy Agent
- Secure pipeline deployment with Infrastructure as Code
- Secrets management with HashiCorp Vault, AWS Secrets Manager
- Network security: VPC, firewalls, private endpoints
- Audit logging and activity tracking
- Incident response planning for data breaches
- Compliance reporting and certification prep
- Zero-trust architecture principles in data pipelines
Module 12: Deployment, CI/CD, and Infrastructure as Code - Containerizing streaming applications with Docker
- Orchestrating with Kubernetes and Helm charts
- Kubernetes operators for Flink and Kafka
- Terraform for provisioning cloud resources
- Managing Kafka clusters with Terraform and Pulumi
- Automating Flink job deployment with CI/CD
- GitOps workflows for pipeline versioning
- CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins
- Environment promotion: dev, staging, prod
- Immutable deployment artifacts and version control
- Rollback procedures and configuration drift prevention
- Parameterization and environment-specific settings
- Dependency management for Python, Java, Scala code
- Artifact storage with Nexus, Artifactory, or S3
- Automated smoke tests post-deployment
- Health check integration with service meshes
- Blue-green and canary deployments for zero downtime
- Feature flags for gradual rollout of new logic
- Infrastructure cost tagging and accountability
- Policy-as-code with Open Policy Agent and Sentinel
Module 13: Real-Time Analytics Applications and Use Cases - Fraud detection pipelines with real-time scoring
- User activity monitoring and behavioral analytics
- Real-time dashboards with live data updates
- Personalization engines and recommendation systems
- Supply chain and logistics tracking in real-time
- IoT telemetry processing and alerting
- Predictive maintenance with sensor data
- Real-time inventory and pricing updates
- Clickstream analysis for digital analytics
- Ad tech: bid request processing and auction systems
- Automated trading and market data feeds
- Customer 360 view with real-time updates
- Compliance monitoring and regulatory alerts
- Network performance monitoring and diagnostics
- Energy grid monitoring and anomaly detection
- Healthcare: real-time patient vitals and alerts
- Cross-border payment tracking and AML flags
- Geofencing and proximity-based triggers
- Dynamic pricing and surge detection
- Real-time sentiment analysis from social feeds
Module 14: Building Your Real-Time Data Solution: Capstone Project - Selecting a real-world use case for your project
- Defining success criteria and KPIs
- Designing the end-to-end architecture
- Creating data flow diagrams and component mapping
- Selecting appropriate technologies based on constraints
- Setting up local development environment with Docker
- Implementing CDC with Debezium and PostgreSQL
- Streaming data into Kafka with proper partitioning
- Processing events with Flink for aggregation and enrichment
- Writing results to Delta Lake with schema evolution
- Building real-time dashboards with Grafana
- Implementing monitoring and alerting
- Adding security: TLS, SASL, and ACLs
- Validating data quality and consistency
- Documenting the system design and trade-offs
- Writing a deployment runbook
- Creating a presentation for technical and business stakeholders
- Review checklist for production readiness
- Submission requirements for certification
- Feedback and improvement plan from mentors
Module 15: Certification, Career Advancement, and Next Steps - Requirements for earning the Certificate of Completion
- Submitting your capstone project for review
- Mentor feedback and revision process
- Receiving your official credential from The Art of Service
- Verifiable certificate with unique ID and project scope
- Adding the certification to LinkedIn and resumes
- Tailoring your portfolio for data engineering roles
- Highlighting real-time expertise in job applications
- Preparing for technical interviews with real-time scenarios
- Common real-time data engineering interview questions
- Whiteboard design: building a real-time fraud system
- Talks and presentations to showcase your knowledge
- Contributing to open source streaming projects
- Joining data engineering communities and forums
- Staying current with emerging tools and trends
- Transitioning to senior or lead data engineering roles
- Architecting enterprise-wide real-time platforms
- Mentoring other engineers and leading initiatives
- Lifetime access to updates and alumni resources
- Ongoing support and career guidance after completion
- Apache Kafka architecture and core components
- Kafka topics, partitions, and replication mechanics
- Producer configuration: acks, retries, batching
- Consumer groups and offset management
- Compacted topics for state retention
- Kafka Connect: source and sink connectors
- Kafka Streams API for lightweight real-time processing
- ksqlDB for SQL-based stream processing
- Deploying Kafka on-premise vs cloud-managed services
- Amazon MSK, Confluent Cloud, and self-hosted trade-offs
- Apache Pulsar: architecture and segment distribution
- Pulsar vs Kafka: feature, performance, and operational differences
- RabbitMQ for lightweight messaging and fan-out patterns
- NATS and NATS JetStream for high-throughput pub/sub
- Google Pub/Sub: regional vs zonal, ordering guarantees
- Azure Event Hubs and integration with Azure services
- Message serialization formats: JSON, Avro, Protobuf, Parquet
- Schema Registry implementation with Confluent and Apicurio
- Event mesh patterns and multi-cluster communication
- Monitoring message broker health and performance
Module 4: Stream Processing Engines and Compute Frameworks - Apache Flink: core architecture and time processing
- Flink windows: tumbling, sliding, session, and global
- State backends: memory, filesystem, RocksDB
- Checkpointing and savepoints for fault tolerance
- Event time processing and watermarks in Flink
- Processing functions: Map, Filter, KeyBy, Co-grouping
- Side outputs and broadcast streams
- Apache Spark Streaming: DStreams vs Structured Streaming
- Micro-batch model and trade-offs
- Structured Streaming with watermarking and aggregation
- Streaming joins: streaming-static, streaming-streaming
- Triggers and output modes in streaming queries
- Apache Storm: topology development and spouts/bolts
- Heron as a modern Storm replacement
- Google Dataflow and Beam SDK for portable pipelines
- Beam runners: local, Dataflow, Flink, Spark
- Writing cross-platform streaming code with Beam
- AWS Kinesis Data Analytics with Flink
- Azure Stream Analytics query language and deployment
- Custom window logic and sessionization techniques
Module 5: Real-Time Data Storage and Serving Systems - Choosing databases for real-time workloads: OLTP vs OLAP
- Columnar storage for analytical workloads
- Data lakes vs data warehouses vs lakehouses
- S3, ADLS, and GCS as real-time ingestion targets
- Delta Lake: ACID transactions, schema enforcement
- Apache Iceberg: table format design and metadata layers
- Apache Hudi: copy-on-write vs merge-on-read
- Serving layers: speed, serving, and batch tiers
- AWS DynamoDB for real-time lookups and joins
- Google Bigtable and Cloud Bigtable use cases
- Azure Cosmos DB and multi-region consistency
- Redis as a real-time cache and state store
- Redis Streams and consumer groups
- TimescaleDB for time-series data and continuous aggregates
- ClickHouse for ultra-fast analytical queries
- DuckDB for embedded real-time analytics
- Elasticsearch for real-time search and log analytics
- Materialized views and pre-aggregation strategies
- Star, snowflake, and wide-column schema patterns
- Handling slowly changing dimensions in streaming
Module 6: Real-Time Data Integration and Ingestion - Designing ingestion pipelines for velocity and volume
- Batch ingestion vs streaming ingestion patterns
- File-based ingestion with monitoring and validation
- APIs as real-time data sources: polling vs webhook
- Webhook integration with authentication and retry
- Database replication tools: Debezium, Maxwell, pg_recvlogical
- Setting up Debezium with Kafka Connect
- Monitoring CDC latency and error rates
- IOT and sensor data ingestion at scale
- Log and metrics ingestion with Fluentd, Logstash, Vector
- Handling structured, semi-structured, and unstructured data
- Validating incoming data: schemas, types, null checks
- Reject queues and dead-letter topics for error handling
- Rate limiting and throttling strategies
- Data shaping and transformation at ingestion point
- Header enrichment and context injection
- Handling time zone and locale-sensitive data
- File formats for real-time: Avro, Parquet, ORC, JSONL
- Compression techniques: Snappy, Zstandard, GZIP
- Batch size tuning for optimal throughput
Module 7: Real-Time Transformation and Processing Logic - Stateless transformations: filtering, mapping, enriching
- Stateful processing: sessions, aggregations, counters
- Cross-stream enrichment with lookup tables
- Joining streaming data with static reference data
- Temporal joins in Flink and Spark
- Enriching streams with geolocation, customer profile, risk score
- Computing real-time aggregates: sum, count, average, percentile
- Sliding window counts and rate calculations
- Sessionization: detecting user journeys in real-time
- Anomaly detection in streams: thresholds, z-scores, moving averages
- Real-time data masking and PII redaction
- Tokenization and encryption at processing time
- Dynamic filtering and routing based on content
- Batch reprocessing of corrected logic
- Versioning processing logic and managing rollbacks
- Feature engineering in streaming for ML pipelines
- Real-time A/B test data routing and aggregation
- Handling duplicates and ensuring data quality
- Idempotent sinks for safe writes
- Null handling and default fallback strategies
Module 8: Monitoring, Observability, and Alerting - Key metrics for real-time pipelines: latency, throughput, errors
- End-to-end latency measurement techniques
- Monitoring consumer lag in Kafka and Pulsar
- Instrumenting custom metrics in Flink and Spark
- Logging best practices for streaming applications
- Structured logging with JSON and context tagging
- Centralized logging with ELK or Grafana Loki
- Distributed tracing in microservices with Jaeger, Zipkin
- Correlating events across services using trace IDs
- Setting up dashboards with Grafana and Prometheus
- Creating meaningful alerts: avoiding noise and false positives
- Alerting on backpressure, memory usage, and GC pauses
- Health checks and readiness probes
- Automated recovery workflows and self-healing pipelines
- SLA tracking and incident response integration
- Audit trails for data lineage and compliance
- Automated pipeline documentation and metadata capture
- Using OpenTelemetry for unified observability
- Cost monitoring for cloud-based streaming systems
- Capacity planning and resource forecasting
Module 9: Scalability, Performance, and Optimization - Partitioning strategies: key-based, round-robin, custom
- Choosing optimal partition counts for throughput
- Handling skewed data and hot partitions
- Repartitioning and rebalancing in streaming
- Parallelism tuning in Flink, Spark, and Kafka
- Task slots, task managers, and executors configuration
- Memory management and off-heap storage
- JVM tuning for low GC pause and high throughput
- Buffer sizing and spill to disk thresholds
- Thread model and asynchronous I/O handling
- Backpressure handling in the producer-consumer chain
- Dynamic scaling with Kubernetes and KEDA
- Auto-scaling based on lag, CPU, or memory
- Cost-performance trade-offs in cloud deployments
- Spot instances and preemptible VMs for savings
- Optimizing serialization and deserialization cost
- Batching strategies for sink operations
- Connection pooling for databases and external systems
- Network optimization: compression, batching, retries
- Performance benchmarking and load testing
Module 10: Testing, Validation, and Quality Assurance - Unit testing streaming processors with TestContext
- Mocking sources and sinks for isolated testing
- Test harnesses in Flink and Spark for pipeline simulation
- Generating synthetic data with controlled variability
- Chaos engineering for pipeline resilience testing
- Simulating network partitions and broker failures
- Golden dataset comparison and regression testing
- Schema validation using JSON Schema, Avro, and Protobuf
- Null and outlier detection in data streams
- Completeness checks: expected record counts and types
- Timeliness validation: SLA adherence testing
- Accuracy validation through cross-system reconciliation
- Reprocessing validation and idempotency checks
- Canary deployments for new pipeline versions
- Blue-green and rolling updates for zero downtime
- A/B testing pipeline logic with split traffic
- Validating retention and cleanup policies
- Performance regression testing across versions
- Automating test suites with CI/CD pipelines
- Test coverage reporting and quality gates
Module 11: Security, Compliance, and Governance - Authentication: SASL, OAuth, mTLS for Kafka and Pulsar
- Authorization: ACLs, RBAC, and least privilege access
- Encryption: in-transit with TLS, at-rest with KMS
- Role-based access control for data assets
- Data masking and dynamic filtering by user context
- PII detection and automated classification
- GDPR, CCPA, HIPAA compliance in real-time data
- Right to be forgotten and data deletion workflows
- Automated data retention and lifecycle policies
- Data lineage capture and visualization
- Metadata tagging and business glossary integration
- Data ownership and stewardship models
- Policy enforcement with Open Policy Agent
- Secure pipeline deployment with Infrastructure as Code
- Secrets management with HashiCorp Vault, AWS Secrets Manager
- Network security: VPC, firewalls, private endpoints
- Audit logging and activity tracking
- Incident response planning for data breaches
- Compliance reporting and certification prep
- Zero-trust architecture principles in data pipelines
Module 12: Deployment, CI/CD, and Infrastructure as Code - Containerizing streaming applications with Docker
- Orchestrating with Kubernetes and Helm charts
- Kubernetes operators for Flink and Kafka
- Terraform for provisioning cloud resources
- Managing Kafka clusters with Terraform and Pulumi
- Automating Flink job deployment with CI/CD
- GitOps workflows for pipeline versioning
- CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins
- Environment promotion: dev, staging, prod
- Immutable deployment artifacts and version control
- Rollback procedures and configuration drift prevention
- Parameterization and environment-specific settings
- Dependency management for Python, Java, Scala code
- Artifact storage with Nexus, Artifactory, or S3
- Automated smoke tests post-deployment
- Health check integration with service meshes
- Blue-green and canary deployments for zero downtime
- Feature flags for gradual rollout of new logic
- Infrastructure cost tagging and accountability
- Policy-as-code with Open Policy Agent and Sentinel
Module 13: Real-Time Analytics Applications and Use Cases - Fraud detection pipelines with real-time scoring
- User activity monitoring and behavioral analytics
- Real-time dashboards with live data updates
- Personalization engines and recommendation systems
- Supply chain and logistics tracking in real-time
- IoT telemetry processing and alerting
- Predictive maintenance with sensor data
- Real-time inventory and pricing updates
- Clickstream analysis for digital analytics
- Ad tech: bid request processing and auction systems
- Automated trading and market data feeds
- Customer 360 view with real-time updates
- Compliance monitoring and regulatory alerts
- Network performance monitoring and diagnostics
- Energy grid monitoring and anomaly detection
- Healthcare: real-time patient vitals and alerts
- Cross-border payment tracking and AML flags
- Geofencing and proximity-based triggers
- Dynamic pricing and surge detection
- Real-time sentiment analysis from social feeds
Module 14: Building Your Real-Time Data Solution: Capstone Project - Selecting a real-world use case for your project
- Defining success criteria and KPIs
- Designing the end-to-end architecture
- Creating data flow diagrams and component mapping
- Selecting appropriate technologies based on constraints
- Setting up local development environment with Docker
- Implementing CDC with Debezium and PostgreSQL
- Streaming data into Kafka with proper partitioning
- Processing events with Flink for aggregation and enrichment
- Writing results to Delta Lake with schema evolution
- Building real-time dashboards with Grafana
- Implementing monitoring and alerting
- Adding security: TLS, SASL, and ACLs
- Validating data quality and consistency
- Documenting the system design and trade-offs
- Writing a deployment runbook
- Creating a presentation for technical and business stakeholders
- Review checklist for production readiness
- Submission requirements for certification
- Feedback and improvement plan from mentors
Module 15: Certification, Career Advancement, and Next Steps - Requirements for earning the Certificate of Completion
- Submitting your capstone project for review
- Mentor feedback and revision process
- Receiving your official credential from The Art of Service
- Verifiable certificate with unique ID and project scope
- Adding the certification to LinkedIn and resumes
- Tailoring your portfolio for data engineering roles
- Highlighting real-time expertise in job applications
- Preparing for technical interviews with real-time scenarios
- Common real-time data engineering interview questions
- Whiteboard design: building a real-time fraud system
- Talks and presentations to showcase your knowledge
- Contributing to open source streaming projects
- Joining data engineering communities and forums
- Staying current with emerging tools and trends
- Transitioning to senior or lead data engineering roles
- Architecting enterprise-wide real-time platforms
- Mentoring other engineers and leading initiatives
- Lifetime access to updates and alumni resources
- Ongoing support and career guidance after completion
- Choosing databases for real-time workloads: OLTP vs OLAP
- Columnar storage for analytical workloads
- Data lakes vs data warehouses vs lakehouses
- S3, ADLS, and GCS as real-time ingestion targets
- Delta Lake: ACID transactions, schema enforcement
- Apache Iceberg: table format design and metadata layers
- Apache Hudi: copy-on-write vs merge-on-read
- Serving layers: speed, serving, and batch tiers
- AWS DynamoDB for real-time lookups and joins
- Google Bigtable and Cloud Bigtable use cases
- Azure Cosmos DB and multi-region consistency
- Redis as a real-time cache and state store
- Redis Streams and consumer groups
- TimescaleDB for time-series data and continuous aggregates
- ClickHouse for ultra-fast analytical queries
- DuckDB for embedded real-time analytics
- Elasticsearch for real-time search and log analytics
- Materialized views and pre-aggregation strategies
- Star, snowflake, and wide-column schema patterns
- Handling slowly changing dimensions in streaming
Module 6: Real-Time Data Integration and Ingestion - Designing ingestion pipelines for velocity and volume
- Batch ingestion vs streaming ingestion patterns
- File-based ingestion with monitoring and validation
- APIs as real-time data sources: polling vs webhook
- Webhook integration with authentication and retry
- Database replication tools: Debezium, Maxwell, pg_recvlogical
- Setting up Debezium with Kafka Connect
- Monitoring CDC latency and error rates
- IOT and sensor data ingestion at scale
- Log and metrics ingestion with Fluentd, Logstash, Vector
- Handling structured, semi-structured, and unstructured data
- Validating incoming data: schemas, types, null checks
- Reject queues and dead-letter topics for error handling
- Rate limiting and throttling strategies
- Data shaping and transformation at ingestion point
- Header enrichment and context injection
- Handling time zone and locale-sensitive data
- File formats for real-time: Avro, Parquet, ORC, JSONL
- Compression techniques: Snappy, Zstandard, GZIP
- Batch size tuning for optimal throughput
Module 7: Real-Time Transformation and Processing Logic - Stateless transformations: filtering, mapping, enriching
- Stateful processing: sessions, aggregations, counters
- Cross-stream enrichment with lookup tables
- Joining streaming data with static reference data
- Temporal joins in Flink and Spark
- Enriching streams with geolocation, customer profile, risk score
- Computing real-time aggregates: sum, count, average, percentile
- Sliding window counts and rate calculations
- Sessionization: detecting user journeys in real-time
- Anomaly detection in streams: thresholds, z-scores, moving averages
- Real-time data masking and PII redaction
- Tokenization and encryption at processing time
- Dynamic filtering and routing based on content
- Batch reprocessing of corrected logic
- Versioning processing logic and managing rollbacks
- Feature engineering in streaming for ML pipelines
- Real-time A/B test data routing and aggregation
- Handling duplicates and ensuring data quality
- Idempotent sinks for safe writes
- Null handling and default fallback strategies
Module 8: Monitoring, Observability, and Alerting - Key metrics for real-time pipelines: latency, throughput, errors
- End-to-end latency measurement techniques
- Monitoring consumer lag in Kafka and Pulsar
- Instrumenting custom metrics in Flink and Spark
- Logging best practices for streaming applications
- Structured logging with JSON and context tagging
- Centralized logging with ELK or Grafana Loki
- Distributed tracing in microservices with Jaeger, Zipkin
- Correlating events across services using trace IDs
- Setting up dashboards with Grafana and Prometheus
- Creating meaningful alerts: avoiding noise and false positives
- Alerting on backpressure, memory usage, and GC pauses
- Health checks and readiness probes
- Automated recovery workflows and self-healing pipelines
- SLA tracking and incident response integration
- Audit trails for data lineage and compliance
- Automated pipeline documentation and metadata capture
- Using OpenTelemetry for unified observability
- Cost monitoring for cloud-based streaming systems
- Capacity planning and resource forecasting
Module 9: Scalability, Performance, and Optimization - Partitioning strategies: key-based, round-robin, custom
- Choosing optimal partition counts for throughput
- Handling skewed data and hot partitions
- Repartitioning and rebalancing in streaming
- Parallelism tuning in Flink, Spark, and Kafka
- Task slots, task managers, and executors configuration
- Memory management and off-heap storage
- JVM tuning for low GC pause and high throughput
- Buffer sizing and spill to disk thresholds
- Thread model and asynchronous I/O handling
- Backpressure handling in the producer-consumer chain
- Dynamic scaling with Kubernetes and KEDA
- Auto-scaling based on lag, CPU, or memory
- Cost-performance trade-offs in cloud deployments
- Spot instances and preemptible VMs for savings
- Optimizing serialization and deserialization cost
- Batching strategies for sink operations
- Connection pooling for databases and external systems
- Network optimization: compression, batching, retries
- Performance benchmarking and load testing
Module 10: Testing, Validation, and Quality Assurance - Unit testing streaming processors with TestContext
- Mocking sources and sinks for isolated testing
- Test harnesses in Flink and Spark for pipeline simulation
- Generating synthetic data with controlled variability
- Chaos engineering for pipeline resilience testing
- Simulating network partitions and broker failures
- Golden dataset comparison and regression testing
- Schema validation using JSON Schema, Avro, and Protobuf
- Null and outlier detection in data streams
- Completeness checks: expected record counts and types
- Timeliness validation: SLA adherence testing
- Accuracy validation through cross-system reconciliation
- Reprocessing validation and idempotency checks
- Canary deployments for new pipeline versions
- Blue-green and rolling updates for zero downtime
- A/B testing pipeline logic with split traffic
- Validating retention and cleanup policies
- Performance regression testing across versions
- Automating test suites with CI/CD pipelines
- Test coverage reporting and quality gates
Module 11: Security, Compliance, and Governance - Authentication: SASL, OAuth, mTLS for Kafka and Pulsar
- Authorization: ACLs, RBAC, and least privilege access
- Encryption: in-transit with TLS, at-rest with KMS
- Role-based access control for data assets
- Data masking and dynamic filtering by user context
- PII detection and automated classification
- GDPR, CCPA, HIPAA compliance in real-time data
- Right to be forgotten and data deletion workflows
- Automated data retention and lifecycle policies
- Data lineage capture and visualization
- Metadata tagging and business glossary integration
- Data ownership and stewardship models
- Policy enforcement with Open Policy Agent
- Secure pipeline deployment with Infrastructure as Code
- Secrets management with HashiCorp Vault, AWS Secrets Manager
- Network security: VPC, firewalls, private endpoints
- Audit logging and activity tracking
- Incident response planning for data breaches
- Compliance reporting and certification prep
- Zero-trust architecture principles in data pipelines
Module 12: Deployment, CI/CD, and Infrastructure as Code - Containerizing streaming applications with Docker
- Orchestrating with Kubernetes and Helm charts
- Kubernetes operators for Flink and Kafka
- Terraform for provisioning cloud resources
- Managing Kafka clusters with Terraform and Pulumi
- Automating Flink job deployment with CI/CD
- GitOps workflows for pipeline versioning
- CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins
- Environment promotion: dev, staging, prod
- Immutable deployment artifacts and version control
- Rollback procedures and configuration drift prevention
- Parameterization and environment-specific settings
- Dependency management for Python, Java, Scala code
- Artifact storage with Nexus, Artifactory, or S3
- Automated smoke tests post-deployment
- Health check integration with service meshes
- Blue-green and canary deployments for zero downtime
- Feature flags for gradual rollout of new logic
- Infrastructure cost tagging and accountability
- Policy-as-code with Open Policy Agent and Sentinel
Module 13: Real-Time Analytics Applications and Use Cases - Fraud detection pipelines with real-time scoring
- User activity monitoring and behavioral analytics
- Real-time dashboards with live data updates
- Personalization engines and recommendation systems
- Supply chain and logistics tracking in real-time
- IoT telemetry processing and alerting
- Predictive maintenance with sensor data
- Real-time inventory and pricing updates
- Clickstream analysis for digital analytics
- Ad tech: bid request processing and auction systems
- Automated trading and market data feeds
- Customer 360 view with real-time updates
- Compliance monitoring and regulatory alerts
- Network performance monitoring and diagnostics
- Energy grid monitoring and anomaly detection
- Healthcare: real-time patient vitals and alerts
- Cross-border payment tracking and AML flags
- Geofencing and proximity-based triggers
- Dynamic pricing and surge detection
- Real-time sentiment analysis from social feeds
Module 14: Building Your Real-Time Data Solution: Capstone Project - Selecting a real-world use case for your project
- Defining success criteria and KPIs
- Designing the end-to-end architecture
- Creating data flow diagrams and component mapping
- Selecting appropriate technologies based on constraints
- Setting up local development environment with Docker
- Implementing CDC with Debezium and PostgreSQL
- Streaming data into Kafka with proper partitioning
- Processing events with Flink for aggregation and enrichment
- Writing results to Delta Lake with schema evolution
- Building real-time dashboards with Grafana
- Implementing monitoring and alerting
- Adding security: TLS, SASL, and ACLs
- Validating data quality and consistency
- Documenting the system design and trade-offs
- Writing a deployment runbook
- Creating a presentation for technical and business stakeholders
- Review checklist for production readiness
- Submission requirements for certification
- Feedback and improvement plan from mentors
Module 15: Certification, Career Advancement, and Next Steps - Requirements for earning the Certificate of Completion
- Submitting your capstone project for review
- Mentor feedback and revision process
- Receiving your official credential from The Art of Service
- Verifiable certificate with unique ID and project scope
- Adding the certification to LinkedIn and resumes
- Tailoring your portfolio for data engineering roles
- Highlighting real-time expertise in job applications
- Preparing for technical interviews with real-time scenarios
- Common real-time data engineering interview questions
- Whiteboard design: building a real-time fraud system
- Talks and presentations to showcase your knowledge
- Contributing to open source streaming projects
- Joining data engineering communities and forums
- Staying current with emerging tools and trends
- Transitioning to senior or lead data engineering roles
- Architecting enterprise-wide real-time platforms
- Mentoring other engineers and leading initiatives
- Lifetime access to updates and alumni resources
- Ongoing support and career guidance after completion
- Stateless transformations: filtering, mapping, enriching
- Stateful processing: sessions, aggregations, counters
- Cross-stream enrichment with lookup tables
- Joining streaming data with static reference data
- Temporal joins in Flink and Spark
- Enriching streams with geolocation, customer profile, risk score
- Computing real-time aggregates: sum, count, average, percentile
- Sliding window counts and rate calculations
- Sessionization: detecting user journeys in real-time
- Anomaly detection in streams: thresholds, z-scores, moving averages
- Real-time data masking and PII redaction
- Tokenization and encryption at processing time
- Dynamic filtering and routing based on content
- Batch reprocessing of corrected logic
- Versioning processing logic and managing rollbacks
- Feature engineering in streaming for ML pipelines
- Real-time A/B test data routing and aggregation
- Handling duplicates and ensuring data quality
- Idempotent sinks for safe writes
- Null handling and default fallback strategies
Module 8: Monitoring, Observability, and Alerting - Key metrics for real-time pipelines: latency, throughput, errors
- End-to-end latency measurement techniques
- Monitoring consumer lag in Kafka and Pulsar
- Instrumenting custom metrics in Flink and Spark
- Logging best practices for streaming applications
- Structured logging with JSON and context tagging
- Centralized logging with ELK or Grafana Loki
- Distributed tracing in microservices with Jaeger, Zipkin
- Correlating events across services using trace IDs
- Setting up dashboards with Grafana and Prometheus
- Creating meaningful alerts: avoiding noise and false positives
- Alerting on backpressure, memory usage, and GC pauses
- Health checks and readiness probes
- Automated recovery workflows and self-healing pipelines
- SLA tracking and incident response integration
- Audit trails for data lineage and compliance
- Automated pipeline documentation and metadata capture
- Using OpenTelemetry for unified observability
- Cost monitoring for cloud-based streaming systems
- Capacity planning and resource forecasting
Module 9: Scalability, Performance, and Optimization - Partitioning strategies: key-based, round-robin, custom
- Choosing optimal partition counts for throughput
- Handling skewed data and hot partitions
- Repartitioning and rebalancing in streaming
- Parallelism tuning in Flink, Spark, and Kafka
- Task slots, task managers, and executors configuration
- Memory management and off-heap storage
- JVM tuning for low GC pause and high throughput
- Buffer sizing and spill to disk thresholds
- Thread model and asynchronous I/O handling
- Backpressure handling in the producer-consumer chain
- Dynamic scaling with Kubernetes and KEDA
- Auto-scaling based on lag, CPU, or memory
- Cost-performance trade-offs in cloud deployments
- Spot instances and preemptible VMs for savings
- Optimizing serialization and deserialization cost
- Batching strategies for sink operations
- Connection pooling for databases and external systems
- Network optimization: compression, batching, retries
- Performance benchmarking and load testing
Module 10: Testing, Validation, and Quality Assurance - Unit testing streaming processors with TestContext
- Mocking sources and sinks for isolated testing
- Test harnesses in Flink and Spark for pipeline simulation
- Generating synthetic data with controlled variability
- Chaos engineering for pipeline resilience testing
- Simulating network partitions and broker failures
- Golden dataset comparison and regression testing
- Schema validation using JSON Schema, Avro, and Protobuf
- Null and outlier detection in data streams
- Completeness checks: expected record counts and types
- Timeliness validation: SLA adherence testing
- Accuracy validation through cross-system reconciliation
- Reprocessing validation and idempotency checks
- Canary deployments for new pipeline versions
- Blue-green and rolling updates for zero downtime
- A/B testing pipeline logic with split traffic
- Validating retention and cleanup policies
- Performance regression testing across versions
- Automating test suites with CI/CD pipelines
- Test coverage reporting and quality gates
Module 11: Security, Compliance, and Governance - Authentication: SASL, OAuth, mTLS for Kafka and Pulsar
- Authorization: ACLs, RBAC, and least privilege access
- Encryption: in-transit with TLS, at-rest with KMS
- Role-based access control for data assets
- Data masking and dynamic filtering by user context
- PII detection and automated classification
- GDPR, CCPA, HIPAA compliance in real-time data
- Right to be forgotten and data deletion workflows
- Automated data retention and lifecycle policies
- Data lineage capture and visualization
- Metadata tagging and business glossary integration
- Data ownership and stewardship models
- Policy enforcement with Open Policy Agent
- Secure pipeline deployment with Infrastructure as Code
- Secrets management with HashiCorp Vault, AWS Secrets Manager
- Network security: VPC, firewalls, private endpoints
- Audit logging and activity tracking
- Incident response planning for data breaches
- Compliance reporting and certification prep
- Zero-trust architecture principles in data pipelines
Module 12: Deployment, CI/CD, and Infrastructure as Code - Containerizing streaming applications with Docker
- Orchestrating with Kubernetes and Helm charts
- Kubernetes operators for Flink and Kafka
- Terraform for provisioning cloud resources
- Managing Kafka clusters with Terraform and Pulumi
- Automating Flink job deployment with CI/CD
- GitOps workflows for pipeline versioning
- CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins
- Environment promotion: dev, staging, prod
- Immutable deployment artifacts and version control
- Rollback procedures and configuration drift prevention
- Parameterization and environment-specific settings
- Dependency management for Python, Java, Scala code
- Artifact storage with Nexus, Artifactory, or S3
- Automated smoke tests post-deployment
- Health check integration with service meshes
- Blue-green and canary deployments for zero downtime
- Feature flags for gradual rollout of new logic
- Infrastructure cost tagging and accountability
- Policy-as-code with Open Policy Agent and Sentinel
Module 13: Real-Time Analytics Applications and Use Cases - Fraud detection pipelines with real-time scoring
- User activity monitoring and behavioral analytics
- Real-time dashboards with live data updates
- Personalization engines and recommendation systems
- Supply chain and logistics tracking in real-time
- IoT telemetry processing and alerting
- Predictive maintenance with sensor data
- Real-time inventory and pricing updates
- Clickstream analysis for digital analytics
- Ad tech: bid request processing and auction systems
- Automated trading and market data feeds
- Customer 360 view with real-time updates
- Compliance monitoring and regulatory alerts
- Network performance monitoring and diagnostics
- Energy grid monitoring and anomaly detection
- Healthcare: real-time patient vitals and alerts
- Cross-border payment tracking and AML flags
- Geofencing and proximity-based triggers
- Dynamic pricing and surge detection
- Real-time sentiment analysis from social feeds
Module 14: Building Your Real-Time Data Solution: Capstone Project - Selecting a real-world use case for your project
- Defining success criteria and KPIs
- Designing the end-to-end architecture
- Creating data flow diagrams and component mapping
- Selecting appropriate technologies based on constraints
- Setting up local development environment with Docker
- Implementing CDC with Debezium and PostgreSQL
- Streaming data into Kafka with proper partitioning
- Processing events with Flink for aggregation and enrichment
- Writing results to Delta Lake with schema evolution
- Building real-time dashboards with Grafana
- Implementing monitoring and alerting
- Adding security: TLS, SASL, and ACLs
- Validating data quality and consistency
- Documenting the system design and trade-offs
- Writing a deployment runbook
- Creating a presentation for technical and business stakeholders
- Review checklist for production readiness
- Submission requirements for certification
- Feedback and improvement plan from mentors
Module 15: Certification, Career Advancement, and Next Steps - Requirements for earning the Certificate of Completion
- Submitting your capstone project for review
- Mentor feedback and revision process
- Receiving your official credential from The Art of Service
- Verifiable certificate with unique ID and project scope
- Adding the certification to LinkedIn and resumes
- Tailoring your portfolio for data engineering roles
- Highlighting real-time expertise in job applications
- Preparing for technical interviews with real-time scenarios
- Common real-time data engineering interview questions
- Whiteboard design: building a real-time fraud system
- Talks and presentations to showcase your knowledge
- Contributing to open source streaming projects
- Joining data engineering communities and forums
- Staying current with emerging tools and trends
- Transitioning to senior or lead data engineering roles
- Architecting enterprise-wide real-time platforms
- Mentoring other engineers and leading initiatives
- Lifetime access to updates and alumni resources
- Ongoing support and career guidance after completion
- Partitioning strategies: key-based, round-robin, custom
- Choosing optimal partition counts for throughput
- Handling skewed data and hot partitions
- Repartitioning and rebalancing in streaming
- Parallelism tuning in Flink, Spark, and Kafka
- Task slots, task managers, and executors configuration
- Memory management and off-heap storage
- JVM tuning for low GC pause and high throughput
- Buffer sizing and spill to disk thresholds
- Thread model and asynchronous I/O handling
- Backpressure handling in the producer-consumer chain
- Dynamic scaling with Kubernetes and KEDA
- Auto-scaling based on lag, CPU, or memory
- Cost-performance trade-offs in cloud deployments
- Spot instances and preemptible VMs for savings
- Optimizing serialization and deserialization cost
- Batching strategies for sink operations
- Connection pooling for databases and external systems
- Network optimization: compression, batching, retries
- Performance benchmarking and load testing
Module 10: Testing, Validation, and Quality Assurance - Unit testing streaming processors with TestContext
- Mocking sources and sinks for isolated testing
- Test harnesses in Flink and Spark for pipeline simulation
- Generating synthetic data with controlled variability
- Chaos engineering for pipeline resilience testing
- Simulating network partitions and broker failures
- Golden dataset comparison and regression testing
- Schema validation using JSON Schema, Avro, and Protobuf
- Null and outlier detection in data streams
- Completeness checks: expected record counts and types
- Timeliness validation: SLA adherence testing
- Accuracy validation through cross-system reconciliation
- Reprocessing validation and idempotency checks
- Canary deployments for new pipeline versions
- Blue-green and rolling updates for zero downtime
- A/B testing pipeline logic with split traffic
- Validating retention and cleanup policies
- Performance regression testing across versions
- Automating test suites with CI/CD pipelines
- Test coverage reporting and quality gates
Module 11: Security, Compliance, and Governance - Authentication: SASL, OAuth, mTLS for Kafka and Pulsar
- Authorization: ACLs, RBAC, and least privilege access
- Encryption: in-transit with TLS, at-rest with KMS
- Role-based access control for data assets
- Data masking and dynamic filtering by user context
- PII detection and automated classification
- GDPR, CCPA, HIPAA compliance in real-time data
- Right to be forgotten and data deletion workflows
- Automated data retention and lifecycle policies
- Data lineage capture and visualization
- Metadata tagging and business glossary integration
- Data ownership and stewardship models
- Policy enforcement with Open Policy Agent
- Secure pipeline deployment with Infrastructure as Code
- Secrets management with HashiCorp Vault, AWS Secrets Manager
- Network security: VPC, firewalls, private endpoints
- Audit logging and activity tracking
- Incident response planning for data breaches
- Compliance reporting and certification prep
- Zero-trust architecture principles in data pipelines
Module 12: Deployment, CI/CD, and Infrastructure as Code - Containerizing streaming applications with Docker
- Orchestrating with Kubernetes and Helm charts
- Kubernetes operators for Flink and Kafka
- Terraform for provisioning cloud resources
- Managing Kafka clusters with Terraform and Pulumi
- Automating Flink job deployment with CI/CD
- GitOps workflows for pipeline versioning
- CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins
- Environment promotion: dev, staging, prod
- Immutable deployment artifacts and version control
- Rollback procedures and configuration drift prevention
- Parameterization and environment-specific settings
- Dependency management for Python, Java, Scala code
- Artifact storage with Nexus, Artifactory, or S3
- Automated smoke tests post-deployment
- Health check integration with service meshes
- Blue-green and canary deployments for zero downtime
- Feature flags for gradual rollout of new logic
- Infrastructure cost tagging and accountability
- Policy-as-code with Open Policy Agent and Sentinel
Module 13: Real-Time Analytics Applications and Use Cases - Fraud detection pipelines with real-time scoring
- User activity monitoring and behavioral analytics
- Real-time dashboards with live data updates
- Personalization engines and recommendation systems
- Supply chain and logistics tracking in real-time
- IoT telemetry processing and alerting
- Predictive maintenance with sensor data
- Real-time inventory and pricing updates
- Clickstream analysis for digital analytics
- Ad tech: bid request processing and auction systems
- Automated trading and market data feeds
- Customer 360 view with real-time updates
- Compliance monitoring and regulatory alerts
- Network performance monitoring and diagnostics
- Energy grid monitoring and anomaly detection
- Healthcare: real-time patient vitals and alerts
- Cross-border payment tracking and AML flags
- Geofencing and proximity-based triggers
- Dynamic pricing and surge detection
- Real-time sentiment analysis from social feeds
Module 14: Building Your Real-Time Data Solution: Capstone Project - Selecting a real-world use case for your project
- Defining success criteria and KPIs
- Designing the end-to-end architecture
- Creating data flow diagrams and component mapping
- Selecting appropriate technologies based on constraints
- Setting up local development environment with Docker
- Implementing CDC with Debezium and PostgreSQL
- Streaming data into Kafka with proper partitioning
- Processing events with Flink for aggregation and enrichment
- Writing results to Delta Lake with schema evolution
- Building real-time dashboards with Grafana
- Implementing monitoring and alerting
- Adding security: TLS, SASL, and ACLs
- Validating data quality and consistency
- Documenting the system design and trade-offs
- Writing a deployment runbook
- Creating a presentation for technical and business stakeholders
- Review checklist for production readiness
- Submission requirements for certification
- Feedback and improvement plan from mentors
Module 15: Certification, Career Advancement, and Next Steps - Requirements for earning the Certificate of Completion
- Submitting your capstone project for review
- Mentor feedback and revision process
- Receiving your official credential from The Art of Service
- Verifiable certificate with unique ID and project scope
- Adding the certification to LinkedIn and resumes
- Tailoring your portfolio for data engineering roles
- Highlighting real-time expertise in job applications
- Preparing for technical interviews with real-time scenarios
- Common real-time data engineering interview questions
- Whiteboard design: building a real-time fraud system
- Talks and presentations to showcase your knowledge
- Contributing to open source streaming projects
- Joining data engineering communities and forums
- Staying current with emerging tools and trends
- Transitioning to senior or lead data engineering roles
- Architecting enterprise-wide real-time platforms
- Mentoring other engineers and leading initiatives
- Lifetime access to updates and alumni resources
- Ongoing support and career guidance after completion
- Authentication: SASL, OAuth, mTLS for Kafka and Pulsar
- Authorization: ACLs, RBAC, and least privilege access
- Encryption: in-transit with TLS, at-rest with KMS
- Role-based access control for data assets
- Data masking and dynamic filtering by user context
- PII detection and automated classification
- GDPR, CCPA, HIPAA compliance in real-time data
- Right to be forgotten and data deletion workflows
- Automated data retention and lifecycle policies
- Data lineage capture and visualization
- Metadata tagging and business glossary integration
- Data ownership and stewardship models
- Policy enforcement with Open Policy Agent
- Secure pipeline deployment with Infrastructure as Code
- Secrets management with HashiCorp Vault, AWS Secrets Manager
- Network security: VPC, firewalls, private endpoints
- Audit logging and activity tracking
- Incident response planning for data breaches
- Compliance reporting and certification prep
- Zero-trust architecture principles in data pipelines
Module 12: Deployment, CI/CD, and Infrastructure as Code - Containerizing streaming applications with Docker
- Orchestrating with Kubernetes and Helm charts
- Kubernetes operators for Flink and Kafka
- Terraform for provisioning cloud resources
- Managing Kafka clusters with Terraform and Pulumi
- Automating Flink job deployment with CI/CD
- GitOps workflows for pipeline versioning
- CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins
- Environment promotion: dev, staging, prod
- Immutable deployment artifacts and version control
- Rollback procedures and configuration drift prevention
- Parameterization and environment-specific settings
- Dependency management for Python, Java, Scala code
- Artifact storage with Nexus, Artifactory, or S3
- Automated smoke tests post-deployment
- Health check integration with service meshes
- Blue-green and canary deployments for zero downtime
- Feature flags for gradual rollout of new logic
- Infrastructure cost tagging and accountability
- Policy-as-code with Open Policy Agent and Sentinel
Module 13: Real-Time Analytics Applications and Use Cases - Fraud detection pipelines with real-time scoring
- User activity monitoring and behavioral analytics
- Real-time dashboards with live data updates
- Personalization engines and recommendation systems
- Supply chain and logistics tracking in real-time
- IoT telemetry processing and alerting
- Predictive maintenance with sensor data
- Real-time inventory and pricing updates
- Clickstream analysis for digital analytics
- Ad tech: bid request processing and auction systems
- Automated trading and market data feeds
- Customer 360 view with real-time updates
- Compliance monitoring and regulatory alerts
- Network performance monitoring and diagnostics
- Energy grid monitoring and anomaly detection
- Healthcare: real-time patient vitals and alerts
- Cross-border payment tracking and AML flags
- Geofencing and proximity-based triggers
- Dynamic pricing and surge detection
- Real-time sentiment analysis from social feeds
Module 14: Building Your Real-Time Data Solution: Capstone Project - Selecting a real-world use case for your project
- Defining success criteria and KPIs
- Designing the end-to-end architecture
- Creating data flow diagrams and component mapping
- Selecting appropriate technologies based on constraints
- Setting up local development environment with Docker
- Implementing CDC with Debezium and PostgreSQL
- Streaming data into Kafka with proper partitioning
- Processing events with Flink for aggregation and enrichment
- Writing results to Delta Lake with schema evolution
- Building real-time dashboards with Grafana
- Implementing monitoring and alerting
- Adding security: TLS, SASL, and ACLs
- Validating data quality and consistency
- Documenting the system design and trade-offs
- Writing a deployment runbook
- Creating a presentation for technical and business stakeholders
- Review checklist for production readiness
- Submission requirements for certification
- Feedback and improvement plan from mentors
Module 15: Certification, Career Advancement, and Next Steps - Requirements for earning the Certificate of Completion
- Submitting your capstone project for review
- Mentor feedback and revision process
- Receiving your official credential from The Art of Service
- Verifiable certificate with unique ID and project scope
- Adding the certification to LinkedIn and resumes
- Tailoring your portfolio for data engineering roles
- Highlighting real-time expertise in job applications
- Preparing for technical interviews with real-time scenarios
- Common real-time data engineering interview questions
- Whiteboard design: building a real-time fraud system
- Talks and presentations to showcase your knowledge
- Contributing to open source streaming projects
- Joining data engineering communities and forums
- Staying current with emerging tools and trends
- Transitioning to senior or lead data engineering roles
- Architecting enterprise-wide real-time platforms
- Mentoring other engineers and leading initiatives
- Lifetime access to updates and alumni resources
- Ongoing support and career guidance after completion
- Fraud detection pipelines with real-time scoring
- User activity monitoring and behavioral analytics
- Real-time dashboards with live data updates
- Personalization engines and recommendation systems
- Supply chain and logistics tracking in real-time
- IoT telemetry processing and alerting
- Predictive maintenance with sensor data
- Real-time inventory and pricing updates
- Clickstream analysis for digital analytics
- Ad tech: bid request processing and auction systems
- Automated trading and market data feeds
- Customer 360 view with real-time updates
- Compliance monitoring and regulatory alerts
- Network performance monitoring and diagnostics
- Energy grid monitoring and anomaly detection
- Healthcare: real-time patient vitals and alerts
- Cross-border payment tracking and AML flags
- Geofencing and proximity-based triggers
- Dynamic pricing and surge detection
- Real-time sentiment analysis from social feeds
Module 14: Building Your Real-Time Data Solution: Capstone Project - Selecting a real-world use case for your project
- Defining success criteria and KPIs
- Designing the end-to-end architecture
- Creating data flow diagrams and component mapping
- Selecting appropriate technologies based on constraints
- Setting up local development environment with Docker
- Implementing CDC with Debezium and PostgreSQL
- Streaming data into Kafka with proper partitioning
- Processing events with Flink for aggregation and enrichment
- Writing results to Delta Lake with schema evolution
- Building real-time dashboards with Grafana
- Implementing monitoring and alerting
- Adding security: TLS, SASL, and ACLs
- Validating data quality and consistency
- Documenting the system design and trade-offs
- Writing a deployment runbook
- Creating a presentation for technical and business stakeholders
- Review checklist for production readiness
- Submission requirements for certification
- Feedback and improvement plan from mentors
Module 15: Certification, Career Advancement, and Next Steps - Requirements for earning the Certificate of Completion
- Submitting your capstone project for review
- Mentor feedback and revision process
- Receiving your official credential from The Art of Service
- Verifiable certificate with unique ID and project scope
- Adding the certification to LinkedIn and resumes
- Tailoring your portfolio for data engineering roles
- Highlighting real-time expertise in job applications
- Preparing for technical interviews with real-time scenarios
- Common real-time data engineering interview questions
- Whiteboard design: building a real-time fraud system
- Talks and presentations to showcase your knowledge
- Contributing to open source streaming projects
- Joining data engineering communities and forums
- Staying current with emerging tools and trends
- Transitioning to senior or lead data engineering roles
- Architecting enterprise-wide real-time platforms
- Mentoring other engineers and leading initiatives
- Lifetime access to updates and alumni resources
- Ongoing support and career guidance after completion
- Requirements for earning the Certificate of Completion
- Submitting your capstone project for review
- Mentor feedback and revision process
- Receiving your official credential from The Art of Service
- Verifiable certificate with unique ID and project scope
- Adding the certification to LinkedIn and resumes
- Tailoring your portfolio for data engineering roles
- Highlighting real-time expertise in job applications
- Preparing for technical interviews with real-time scenarios
- Common real-time data engineering interview questions
- Whiteboard design: building a real-time fraud system
- Talks and presentations to showcase your knowledge
- Contributing to open source streaming projects
- Joining data engineering communities and forums
- Staying current with emerging tools and trends
- Transitioning to senior or lead data engineering roles
- Architecting enterprise-wide real-time platforms
- Mentoring other engineers and leading initiatives
- Lifetime access to updates and alumni resources
- Ongoing support and career guidance after completion