Mastering Apache Spark for Real-Time Data Engineering
You're not just learning a tool. You're claiming a strategic advantage in the high-stakes world of data infrastructure. The pressure is real: systems that can't keep up, dashboards with stale metrics, and pipelines that break under load. While others struggle with batch delays and fragmented architectures, you can step in with confidence - ready to design scalable, low-latency data systems that power real-time decisions. Organisations are investing heavily in streaming architectures. But most engineers are still operating with outdated batch-centric mental models. If you don’t master real-time engineering now, you’ll be sidelined when the next major platform rollout happens - and someone else gets the credit, the budget, and the promotion. Mastering Apache Spark for Real-Time Data Engineering is your direct path from uncertainty to mastery. This is not a gentle overview. It's a precision-engineered curriculum that transforms your ability to build, optimise, and deploy fault-tolerant streaming pipelines using Spark Structured Streaming, Delta Lake, and modern data stack integrations. One recent graduate, Sarah K., a Data Engineer at a Fortune 500 financial services firm, used the patterns in this course to redesign her company's fraud detection pipeline. She reduced processing latency from 15 minutes to under 800 milliseconds - and presented the results to executive leadership with a board-ready architecture diagram and performance validation. She was promoted within three months. Every concept is tied to measurable outcomes. From your first interaction, you'll be applying industry-tested frameworks that align with production-grade engineering standards. No fluff. No filler. Just high-leverage, immediate-impact learning that closes the gap between your current skills and the expectations of top-tier data teams. Here’s how this course is structured to help you get there.Course Format & Delivery Details This is a premium, self-paced learning experience designed specifically for working professionals who need flexibility without sacrificing depth. You gain immediate online access upon registration, with full control over your schedule and learning pace. There are no fixed start dates, no webinars to attend, and no arbitrary deadlines - just clear, on-demand mastery. Most learners complete the core curriculum within 4 to 6 weeks while applying concepts incrementally in their day-to-day roles. Many report building their first production-ready streaming job within the first 10 days. The structure ensures you're not just consuming information - you're implementing, validating, and gaining confidence with every module. You receive lifetime access to all course materials, including every update as Apache Spark, Delta Lake, and the broader ecosystem evolve. As new features like continuous processing enhancements or Catalyst optimiser upgrades are released, updated content is seamlessly integrated - at no additional cost. Access is available 24/7 from any device, with full mobile compatibility. Whether you're reviewing pipeline tuning principles on your phone during a commute or diving deep into checkpoint management on your laptop, your progress is preserved and synchronised. Each learner receives direct guidance through dedicated support channels with industry-experienced instructors. This is not automated chat or forum posting. You’ll get clear, structured feedback on implementation challenges, architecture review requests, and troubleshooting scenarios unique to your environment. Upon successful completion, you will earn a Certificate of Completion issued by The Art of Service - a globally recognised credential trusted by thousands of professionals and hiring managers. This certification validates your hands-on ability to engineer real-time data systems using Apache Spark, and it carries significant weight in technical interviews and internal advancement discussions. Pricing is transparent and straightforward. There are no hidden fees, recurring charges, or surprise add-ons. The listed investment includes everything: curriculum, updates, certificate, and support. We accept all major payment methods, including Visa, Mastercard, and PayPal, ensuring secure and seamless enrollment for professionals worldwide. Your success is guaranteed. If at any point you find the course doesn’t meet your expectations, you’re covered by our 30-day satisfied or refunded promise. There is zero financial risk - just complete access and full confidence in your decision. After enrollment, you will receive a confirmation email. Your access credentials and learning portal details will be delivered separately once your course materials are fully configured, ensuring a reliable and secure learning environment from day one. Will this work for you? Yes - even if you’ve only used Spark in batch mode, even if you're new to event-time processing, or even if your current projects rely on legacy ETL frameworks. The curriculum is engineered to bridge knowledge gaps methodically, using real-world scenarios and step-by-step implementation blueprints. This works even if you're not working with cloud platforms yet. The patterns taught are agnostic to deployment environment and can be applied equally in on-prem, hybrid, or cloud-based architectures. You’ll learn how to adapt configurations whether you're working with HDFS, S3, ADLS, or GCS. With explicit risk reversal, battle-tested content, and enterprise-grade precision, this course removes the guesswork and delivers proven results - no matter your starting point.
Module 1: Foundations of Real-Time Data Engineering - Understanding the shift from batch to real-time data architectures
- Defining real-time: latency expectations across industries
- Event-driven vs. scheduled processing: strategic implications
- Core components of a streaming data pipeline
- Comparing Spark with alternatives: Flink, Kafka Streams, Storm
- Anatomy of a Spark application in production
- The role of data engineers in real-time system ownership
- Key performance indicators for streaming pipelines
- Handling out-of-order, late, and duplicate events
- Introduction to event time, ingestion time, and processing time
- Data quality considerations in streaming contexts
- Operational visibility and monitoring fundamentals
- Architectural trade-offs: throughput vs. latency
- Backpressure detection and mitigation strategies
- Failure modes in real-time systems and recovery principles
Module 2: Apache Spark Architecture Deep Dive - Spark Unified Engine: how one runtime powers batch and streaming
- Resilient Distributed Datasets (RDDs) and their evolution
- DataFrames and Datasets: type safety and optimisation benefits
- Catalyst Optimiser: rule-based and cost-based optimisation
- Tungsten engine: memory management and execution efficiency
- Spark Session and Context configuration best practices
- Cluster modes: client vs cluster deployment considerations
- Driver and executor lifecycle in long-running jobs
- Serialization frameworks: Java, Kryo, and custom serializers
- Memory tuning: heap, off-heap, and garbage collection impact
- Broadcast variables and accumulator patterns
- Partitioning strategies and shuffle mechanics
- Wide vs narrow transformations in streaming contexts
- Execution plans: interpreting physical, logical, and resolved plans
- Spark’s cost-based optimiser: statistics collection and usage
Module 3: Structured Streaming Core Concepts - Structured Streaming vs DStreams: architecture comparison
- The micro-batch execution model explained
- Continuous processing mode: capabilities and limitations
- Input sources: Kafka, JSON, CSV, Parquet, text streams
- Output sinks: console, memory, file, Kafka, JDBC
- Query management: starting, monitoring, stopping streaming queries
- Watermarking: defining state retention and late data thresholds
- Event-time aggregation with watermarks
- Handling late data: allowedDelay and state cleanup
- Incremental processing with incremental checkpointing
- State management: key-value stores in streaming operations
- Aggregation over hopping, sliding, and tumbling windows
- Joining streaming and static DataFrames
- Streaming-stream joins: practical patterns and limitations
- Exactly-once semantics: how Spark ensures consistency
Module 4: Cluster Configuration and Deployment - Standalone vs YARN vs Kubernetes: deployment pros and cons
- Kubernetes operator for Spark: modern orchestration
- Configuring Spark on EMR, Databricks, Dataproc
- Resource allocation: driver and executor sizing
- Dynamic allocation: scaling executors based on load
- Configuring high availability for driver recovery
- Checkpointing: directory structure and recovery process
- Monitoring cluster health through resource managers
- Security: authentication, authorisation, and encryption
- Network configuration for low-latency data flow
- Graceful shutdown procedures for minimal data loss
- Log aggregation strategies with ELK or Splunk
- Resource isolation and multi-tenancy considerations
- Cost optimisation in cloud-based clusters
- Disaster recovery planning for streaming workloads
Module 5: Performance Tuning and Optimisation - Identifying bottlenecks using Spark UI metrics
- Monitoring input rate, processing rate, and batch duration
- Tuning micro-batch intervals for optimal throughput
- Executor memory tuning: spark.executor.memoryOverhead
- Garbage collection tuning for low-pause applications
- Shuffle partition sizing and autoscaling
- Skew handling: salting and custom partitioners
- Data skew detection using histogram analysis
- Broadcast join thresholds and auto-broadcast settings
- Join optimisation: broadcast vs shuffle vs sort-merge
- Caching strategies for frequently accessed datasets
- File size optimisation in streaming sinks
- Query plan improvements using hints and repartitioning
- Coalescing small files in streaming output
- Cost-based optimisation with table statistics
Module 6: Fault Tolerance and Reliability - Checkpointing: exactly-one semantics and fault recovery
- Checkpoint location best practices: durability and access patterns
- Handling source failures: Kafka rebalances, network drops
- Sink resiliency: retry logic and error handling
- Idempotent write patterns for safe reprocessing
- At-least-once vs exactly-once sink guarantees
- Monitoring for data loss and duplication
- End-to-end latency tracking and measurement
- Data lineage and traceability in streaming jobs
- Idempotent processing: deduplication with event keys
- POJO and Avro schema evolution strategies
- Handling schema drift in JSON and semi-structured data
- Recovery from corrupted checkpoint files
- Testing recovery scenarios in staging environments
- Ensuring consistency across distributed components
Module 7: Streaming Data Sources and Sinks - Kafka integration: subscribing to topics by pattern
- Kafka SSL, SASL, and security configuration
- Reading Avro from Confluent Schema Registry
- Writing structured streams to Kafka topics
- File source: monitoring directories for new data
- Cloud storage: reading from S3, ADLS, GCS with event triggers
- Socket source: use cases and limitations
- Rate source for stress testing and benchmarking
- JDBC sink: upsert patterns with merge operations
- Delta Lake as a streaming sink: ACID guarantees
- Console and memory sinks for development and testing
- Custom sink development: implementing ForeachWriter
- Writing to Elasticsearch with bulk indexing
- Integration with Amazon Kinesis Data Streams
- Pulsar and RabbitMQ connector patterns
Module 8: State Management and Advanced Operations - State store backends: RocksDB vs in-memory options
- Configuring state cleanup policies
- Custom state management using mapGroupsWithState
- Using applyInPandasWithState for Python workloads
- Session window operations with dynamic gaps
- Sessionisation of user event streams
- Pattern detection with sequence matching
- Fraud detection using time-based event sequences
- Session expiry and timeout handling
- Aggregating over user-defined window functions
- Processing time triggers for early results
- Delta watermark propagation in multi-stage pipelines
- Handling timezone-aware event timestamps
- Custom watermark assignment per event
- State expiration and TTL configuration
Module 9: Integration with Modern Data Stack - Delta Lake: unified batch and streaming layer
- Merging streaming results with MERGE INTO syntax
- Time travel and versioning in Delta tables
- Z-ordering for query performance optimisation
- Optimising Delta with VACUUM and OPTIMIZE
- Schema enforcement and evolution in streaming writes
- Unity Catalog integration for data governance
- Lineage tracking across Spark and metadata layers
- Integration with Apache Iceberg and Hudi
- Streaming into medallion architecture: bronze, silver, gold
- Metadata management with Apache Atlas
- Event time alignment across data zones
- Scheduling dependencies with Apache Airflow
- Orchestration using Prefect and Dagster
- Event validation using Great Expectations
Module 10: Monitoring, Observability, and Alerting - Spark UI: interpreting streaming query details
- Key metrics: input rate, processing rate, latency
- Streaming QueryListener for custom monitoring
- Integrating with Prometheus and Grafana
- Pushing metrics to Datadog or New Relic
- Custom counters and gauges using metrics system
- Logging best practices: structured JSON output
- Centralised log aggregation with ELK stack
- Alerting on lag, backpressure, or failures
- Building a dashboard for pipeline health
- SLOs and SLIs for streaming systems
- Detecting data drift in continuous pipelines
- Error tracking with Sentry or similar tools
- Correlating logs using trace IDs and request contexts
- Audit logging for compliance and governance
Module 11: Testing and Quality Assurance - Unit testing streaming logic with Spark testing tools
- Mocking Kafka and file sources for local testing
- Testing watermark and late data handling
- Validating stateful operations using controlled input
- Golden dataset testing with expected outputs
- Data validation using row-level assertions
- Schema compatibility checks in pipelines
- Testing idempotency and recovery scenarios
- Performance benchmarks with synthetic loads
- Load testing with large-scale event generation
- Checking for memory leaks in long-running jobs
- Integration testing across sink and downstream systems
- Automating tests using CI/CD pipelines
- Snapshot testing for structured output validation
- Ensuring reproducibility in test environments
Module 12: Production Patterns and Anti-Patterns - Avoiding unbounded state growth in aggregations
- Managing micro-batch sizes for stability
- Choosing between append, update, and complete output modes
- Using foreachBatch for complex sink operations
- Idempotent writes to databases with deduplication keys
- Backfilling streaming pipelines: strategies and tools
- Zero-downtime deployments with versioned processing
- Blue-green deployment of streaming applications
- Pipeline versioning with artifact tagging
- Handling config changes without restarting
- Feature flags in data processing logic
- Schema migration during live operations
- Graceful degradation under high load
- Rate limiting aggressive consumers
- Avoiding common serialisation and classpath issues
Module 13: Security and Governance - Authentication: Kerberos, OAuth, AWS IAM roles
- Authorisation: file system, table, and column-level access
- Encryption in transit: TLS for Kafka and storage
- Encryption at rest: managing keys and volumes
- Data masking and redaction in streaming output
- Audit trails for data access and modification
- PII detection and handling in real-time flows
- Compliance with GDPR, CCPA, HIPAA requirements
- Row and column filtering based on user roles
- Secure credential management with vaults
- Network segmentation and firewall rules
- Monitoring for unauthorised access attempts
- Data retention policies in streaming sinks
- Secure logging: masking sensitive payloads
- Role-based access in Databricks and cloud platforms
Module 14: Advanced Use Cases and Cross-Industry Applications - Fraud detection: real-time anomaly scoring
- IoT telemetry: processing sensor data streams
- Clickstream analysis for user journey mapping
- Real-time pricing engines in e-commerce
- Supply chain event tracking with geospatial context
- Healthcare monitoring: vital sign streaming
- Telecom call detail record processing
- Ad tech: bidstream analysis and optimisation
- Energy grid monitoring and fault detection
- Financial market data: tick processing and aggregation
- Social sentiment tracking from public feeds
- Server log analysis for anomaly detection
- Real-time inventory updates and reconciliation
- Location-based alerts and geofencing
- Automotive telematics and predictive diagnostics
Module 15: Certification, Career Advancement, and Next Steps - Preparing for the final assessment: format and expectations
- Project submission: building a production-grade pipeline
- Reviewing performance, reliability, and observability
- Demonstrating watermark, recovery, and idempotency
- Validating schema evolution and error handling
- Documentation standards for engineering artefacts
- Peer review framework for best practice alignment
- Earning your Certificate of Completion from The Art of Service
- Adding certification to LinkedIn and professional profiles
- How hiring managers view The Art of Service credentials
- Using your project as a portfolio piece
- Salary benchmarks for real-time data engineers
- Negotiating promotions based on new capabilities
- Transitioning into roles with higher responsibility
- Continuing education paths in data architecture
- Understanding the shift from batch to real-time data architectures
- Defining real-time: latency expectations across industries
- Event-driven vs. scheduled processing: strategic implications
- Core components of a streaming data pipeline
- Comparing Spark with alternatives: Flink, Kafka Streams, Storm
- Anatomy of a Spark application in production
- The role of data engineers in real-time system ownership
- Key performance indicators for streaming pipelines
- Handling out-of-order, late, and duplicate events
- Introduction to event time, ingestion time, and processing time
- Data quality considerations in streaming contexts
- Operational visibility and monitoring fundamentals
- Architectural trade-offs: throughput vs. latency
- Backpressure detection and mitigation strategies
- Failure modes in real-time systems and recovery principles
Module 2: Apache Spark Architecture Deep Dive - Spark Unified Engine: how one runtime powers batch and streaming
- Resilient Distributed Datasets (RDDs) and their evolution
- DataFrames and Datasets: type safety and optimisation benefits
- Catalyst Optimiser: rule-based and cost-based optimisation
- Tungsten engine: memory management and execution efficiency
- Spark Session and Context configuration best practices
- Cluster modes: client vs cluster deployment considerations
- Driver and executor lifecycle in long-running jobs
- Serialization frameworks: Java, Kryo, and custom serializers
- Memory tuning: heap, off-heap, and garbage collection impact
- Broadcast variables and accumulator patterns
- Partitioning strategies and shuffle mechanics
- Wide vs narrow transformations in streaming contexts
- Execution plans: interpreting physical, logical, and resolved plans
- Spark’s cost-based optimiser: statistics collection and usage
Module 3: Structured Streaming Core Concepts - Structured Streaming vs DStreams: architecture comparison
- The micro-batch execution model explained
- Continuous processing mode: capabilities and limitations
- Input sources: Kafka, JSON, CSV, Parquet, text streams
- Output sinks: console, memory, file, Kafka, JDBC
- Query management: starting, monitoring, stopping streaming queries
- Watermarking: defining state retention and late data thresholds
- Event-time aggregation with watermarks
- Handling late data: allowedDelay and state cleanup
- Incremental processing with incremental checkpointing
- State management: key-value stores in streaming operations
- Aggregation over hopping, sliding, and tumbling windows
- Joining streaming and static DataFrames
- Streaming-stream joins: practical patterns and limitations
- Exactly-once semantics: how Spark ensures consistency
Module 4: Cluster Configuration and Deployment - Standalone vs YARN vs Kubernetes: deployment pros and cons
- Kubernetes operator for Spark: modern orchestration
- Configuring Spark on EMR, Databricks, Dataproc
- Resource allocation: driver and executor sizing
- Dynamic allocation: scaling executors based on load
- Configuring high availability for driver recovery
- Checkpointing: directory structure and recovery process
- Monitoring cluster health through resource managers
- Security: authentication, authorisation, and encryption
- Network configuration for low-latency data flow
- Graceful shutdown procedures for minimal data loss
- Log aggregation strategies with ELK or Splunk
- Resource isolation and multi-tenancy considerations
- Cost optimisation in cloud-based clusters
- Disaster recovery planning for streaming workloads
Module 5: Performance Tuning and Optimisation - Identifying bottlenecks using Spark UI metrics
- Monitoring input rate, processing rate, and batch duration
- Tuning micro-batch intervals for optimal throughput
- Executor memory tuning: spark.executor.memoryOverhead
- Garbage collection tuning for low-pause applications
- Shuffle partition sizing and autoscaling
- Skew handling: salting and custom partitioners
- Data skew detection using histogram analysis
- Broadcast join thresholds and auto-broadcast settings
- Join optimisation: broadcast vs shuffle vs sort-merge
- Caching strategies for frequently accessed datasets
- File size optimisation in streaming sinks
- Query plan improvements using hints and repartitioning
- Coalescing small files in streaming output
- Cost-based optimisation with table statistics
Module 6: Fault Tolerance and Reliability - Checkpointing: exactly-one semantics and fault recovery
- Checkpoint location best practices: durability and access patterns
- Handling source failures: Kafka rebalances, network drops
- Sink resiliency: retry logic and error handling
- Idempotent write patterns for safe reprocessing
- At-least-once vs exactly-once sink guarantees
- Monitoring for data loss and duplication
- End-to-end latency tracking and measurement
- Data lineage and traceability in streaming jobs
- Idempotent processing: deduplication with event keys
- POJO and Avro schema evolution strategies
- Handling schema drift in JSON and semi-structured data
- Recovery from corrupted checkpoint files
- Testing recovery scenarios in staging environments
- Ensuring consistency across distributed components
Module 7: Streaming Data Sources and Sinks - Kafka integration: subscribing to topics by pattern
- Kafka SSL, SASL, and security configuration
- Reading Avro from Confluent Schema Registry
- Writing structured streams to Kafka topics
- File source: monitoring directories for new data
- Cloud storage: reading from S3, ADLS, GCS with event triggers
- Socket source: use cases and limitations
- Rate source for stress testing and benchmarking
- JDBC sink: upsert patterns with merge operations
- Delta Lake as a streaming sink: ACID guarantees
- Console and memory sinks for development and testing
- Custom sink development: implementing ForeachWriter
- Writing to Elasticsearch with bulk indexing
- Integration with Amazon Kinesis Data Streams
- Pulsar and RabbitMQ connector patterns
Module 8: State Management and Advanced Operations - State store backends: RocksDB vs in-memory options
- Configuring state cleanup policies
- Custom state management using mapGroupsWithState
- Using applyInPandasWithState for Python workloads
- Session window operations with dynamic gaps
- Sessionisation of user event streams
- Pattern detection with sequence matching
- Fraud detection using time-based event sequences
- Session expiry and timeout handling
- Aggregating over user-defined window functions
- Processing time triggers for early results
- Delta watermark propagation in multi-stage pipelines
- Handling timezone-aware event timestamps
- Custom watermark assignment per event
- State expiration and TTL configuration
Module 9: Integration with Modern Data Stack - Delta Lake: unified batch and streaming layer
- Merging streaming results with MERGE INTO syntax
- Time travel and versioning in Delta tables
- Z-ordering for query performance optimisation
- Optimising Delta with VACUUM and OPTIMIZE
- Schema enforcement and evolution in streaming writes
- Unity Catalog integration for data governance
- Lineage tracking across Spark and metadata layers
- Integration with Apache Iceberg and Hudi
- Streaming into medallion architecture: bronze, silver, gold
- Metadata management with Apache Atlas
- Event time alignment across data zones
- Scheduling dependencies with Apache Airflow
- Orchestration using Prefect and Dagster
- Event validation using Great Expectations
Module 10: Monitoring, Observability, and Alerting - Spark UI: interpreting streaming query details
- Key metrics: input rate, processing rate, latency
- Streaming QueryListener for custom monitoring
- Integrating with Prometheus and Grafana
- Pushing metrics to Datadog or New Relic
- Custom counters and gauges using metrics system
- Logging best practices: structured JSON output
- Centralised log aggregation with ELK stack
- Alerting on lag, backpressure, or failures
- Building a dashboard for pipeline health
- SLOs and SLIs for streaming systems
- Detecting data drift in continuous pipelines
- Error tracking with Sentry or similar tools
- Correlating logs using trace IDs and request contexts
- Audit logging for compliance and governance
Module 11: Testing and Quality Assurance - Unit testing streaming logic with Spark testing tools
- Mocking Kafka and file sources for local testing
- Testing watermark and late data handling
- Validating stateful operations using controlled input
- Golden dataset testing with expected outputs
- Data validation using row-level assertions
- Schema compatibility checks in pipelines
- Testing idempotency and recovery scenarios
- Performance benchmarks with synthetic loads
- Load testing with large-scale event generation
- Checking for memory leaks in long-running jobs
- Integration testing across sink and downstream systems
- Automating tests using CI/CD pipelines
- Snapshot testing for structured output validation
- Ensuring reproducibility in test environments
Module 12: Production Patterns and Anti-Patterns - Avoiding unbounded state growth in aggregations
- Managing micro-batch sizes for stability
- Choosing between append, update, and complete output modes
- Using foreachBatch for complex sink operations
- Idempotent writes to databases with deduplication keys
- Backfilling streaming pipelines: strategies and tools
- Zero-downtime deployments with versioned processing
- Blue-green deployment of streaming applications
- Pipeline versioning with artifact tagging
- Handling config changes without restarting
- Feature flags in data processing logic
- Schema migration during live operations
- Graceful degradation under high load
- Rate limiting aggressive consumers
- Avoiding common serialisation and classpath issues
Module 13: Security and Governance - Authentication: Kerberos, OAuth, AWS IAM roles
- Authorisation: file system, table, and column-level access
- Encryption in transit: TLS for Kafka and storage
- Encryption at rest: managing keys and volumes
- Data masking and redaction in streaming output
- Audit trails for data access and modification
- PII detection and handling in real-time flows
- Compliance with GDPR, CCPA, HIPAA requirements
- Row and column filtering based on user roles
- Secure credential management with vaults
- Network segmentation and firewall rules
- Monitoring for unauthorised access attempts
- Data retention policies in streaming sinks
- Secure logging: masking sensitive payloads
- Role-based access in Databricks and cloud platforms
Module 14: Advanced Use Cases and Cross-Industry Applications - Fraud detection: real-time anomaly scoring
- IoT telemetry: processing sensor data streams
- Clickstream analysis for user journey mapping
- Real-time pricing engines in e-commerce
- Supply chain event tracking with geospatial context
- Healthcare monitoring: vital sign streaming
- Telecom call detail record processing
- Ad tech: bidstream analysis and optimisation
- Energy grid monitoring and fault detection
- Financial market data: tick processing and aggregation
- Social sentiment tracking from public feeds
- Server log analysis for anomaly detection
- Real-time inventory updates and reconciliation
- Location-based alerts and geofencing
- Automotive telematics and predictive diagnostics
Module 15: Certification, Career Advancement, and Next Steps - Preparing for the final assessment: format and expectations
- Project submission: building a production-grade pipeline
- Reviewing performance, reliability, and observability
- Demonstrating watermark, recovery, and idempotency
- Validating schema evolution and error handling
- Documentation standards for engineering artefacts
- Peer review framework for best practice alignment
- Earning your Certificate of Completion from The Art of Service
- Adding certification to LinkedIn and professional profiles
- How hiring managers view The Art of Service credentials
- Using your project as a portfolio piece
- Salary benchmarks for real-time data engineers
- Negotiating promotions based on new capabilities
- Transitioning into roles with higher responsibility
- Continuing education paths in data architecture
- Structured Streaming vs DStreams: architecture comparison
- The micro-batch execution model explained
- Continuous processing mode: capabilities and limitations
- Input sources: Kafka, JSON, CSV, Parquet, text streams
- Output sinks: console, memory, file, Kafka, JDBC
- Query management: starting, monitoring, stopping streaming queries
- Watermarking: defining state retention and late data thresholds
- Event-time aggregation with watermarks
- Handling late data: allowedDelay and state cleanup
- Incremental processing with incremental checkpointing
- State management: key-value stores in streaming operations
- Aggregation over hopping, sliding, and tumbling windows
- Joining streaming and static DataFrames
- Streaming-stream joins: practical patterns and limitations
- Exactly-once semantics: how Spark ensures consistency
Module 4: Cluster Configuration and Deployment - Standalone vs YARN vs Kubernetes: deployment pros and cons
- Kubernetes operator for Spark: modern orchestration
- Configuring Spark on EMR, Databricks, Dataproc
- Resource allocation: driver and executor sizing
- Dynamic allocation: scaling executors based on load
- Configuring high availability for driver recovery
- Checkpointing: directory structure and recovery process
- Monitoring cluster health through resource managers
- Security: authentication, authorisation, and encryption
- Network configuration for low-latency data flow
- Graceful shutdown procedures for minimal data loss
- Log aggregation strategies with ELK or Splunk
- Resource isolation and multi-tenancy considerations
- Cost optimisation in cloud-based clusters
- Disaster recovery planning for streaming workloads
Module 5: Performance Tuning and Optimisation - Identifying bottlenecks using Spark UI metrics
- Monitoring input rate, processing rate, and batch duration
- Tuning micro-batch intervals for optimal throughput
- Executor memory tuning: spark.executor.memoryOverhead
- Garbage collection tuning for low-pause applications
- Shuffle partition sizing and autoscaling
- Skew handling: salting and custom partitioners
- Data skew detection using histogram analysis
- Broadcast join thresholds and auto-broadcast settings
- Join optimisation: broadcast vs shuffle vs sort-merge
- Caching strategies for frequently accessed datasets
- File size optimisation in streaming sinks
- Query plan improvements using hints and repartitioning
- Coalescing small files in streaming output
- Cost-based optimisation with table statistics
Module 6: Fault Tolerance and Reliability - Checkpointing: exactly-one semantics and fault recovery
- Checkpoint location best practices: durability and access patterns
- Handling source failures: Kafka rebalances, network drops
- Sink resiliency: retry logic and error handling
- Idempotent write patterns for safe reprocessing
- At-least-once vs exactly-once sink guarantees
- Monitoring for data loss and duplication
- End-to-end latency tracking and measurement
- Data lineage and traceability in streaming jobs
- Idempotent processing: deduplication with event keys
- POJO and Avro schema evolution strategies
- Handling schema drift in JSON and semi-structured data
- Recovery from corrupted checkpoint files
- Testing recovery scenarios in staging environments
- Ensuring consistency across distributed components
Module 7: Streaming Data Sources and Sinks - Kafka integration: subscribing to topics by pattern
- Kafka SSL, SASL, and security configuration
- Reading Avro from Confluent Schema Registry
- Writing structured streams to Kafka topics
- File source: monitoring directories for new data
- Cloud storage: reading from S3, ADLS, GCS with event triggers
- Socket source: use cases and limitations
- Rate source for stress testing and benchmarking
- JDBC sink: upsert patterns with merge operations
- Delta Lake as a streaming sink: ACID guarantees
- Console and memory sinks for development and testing
- Custom sink development: implementing ForeachWriter
- Writing to Elasticsearch with bulk indexing
- Integration with Amazon Kinesis Data Streams
- Pulsar and RabbitMQ connector patterns
Module 8: State Management and Advanced Operations - State store backends: RocksDB vs in-memory options
- Configuring state cleanup policies
- Custom state management using mapGroupsWithState
- Using applyInPandasWithState for Python workloads
- Session window operations with dynamic gaps
- Sessionisation of user event streams
- Pattern detection with sequence matching
- Fraud detection using time-based event sequences
- Session expiry and timeout handling
- Aggregating over user-defined window functions
- Processing time triggers for early results
- Delta watermark propagation in multi-stage pipelines
- Handling timezone-aware event timestamps
- Custom watermark assignment per event
- State expiration and TTL configuration
Module 9: Integration with Modern Data Stack - Delta Lake: unified batch and streaming layer
- Merging streaming results with MERGE INTO syntax
- Time travel and versioning in Delta tables
- Z-ordering for query performance optimisation
- Optimising Delta with VACUUM and OPTIMIZE
- Schema enforcement and evolution in streaming writes
- Unity Catalog integration for data governance
- Lineage tracking across Spark and metadata layers
- Integration with Apache Iceberg and Hudi
- Streaming into medallion architecture: bronze, silver, gold
- Metadata management with Apache Atlas
- Event time alignment across data zones
- Scheduling dependencies with Apache Airflow
- Orchestration using Prefect and Dagster
- Event validation using Great Expectations
Module 10: Monitoring, Observability, and Alerting - Spark UI: interpreting streaming query details
- Key metrics: input rate, processing rate, latency
- Streaming QueryListener for custom monitoring
- Integrating with Prometheus and Grafana
- Pushing metrics to Datadog or New Relic
- Custom counters and gauges using metrics system
- Logging best practices: structured JSON output
- Centralised log aggregation with ELK stack
- Alerting on lag, backpressure, or failures
- Building a dashboard for pipeline health
- SLOs and SLIs for streaming systems
- Detecting data drift in continuous pipelines
- Error tracking with Sentry or similar tools
- Correlating logs using trace IDs and request contexts
- Audit logging for compliance and governance
Module 11: Testing and Quality Assurance - Unit testing streaming logic with Spark testing tools
- Mocking Kafka and file sources for local testing
- Testing watermark and late data handling
- Validating stateful operations using controlled input
- Golden dataset testing with expected outputs
- Data validation using row-level assertions
- Schema compatibility checks in pipelines
- Testing idempotency and recovery scenarios
- Performance benchmarks with synthetic loads
- Load testing with large-scale event generation
- Checking for memory leaks in long-running jobs
- Integration testing across sink and downstream systems
- Automating tests using CI/CD pipelines
- Snapshot testing for structured output validation
- Ensuring reproducibility in test environments
Module 12: Production Patterns and Anti-Patterns - Avoiding unbounded state growth in aggregations
- Managing micro-batch sizes for stability
- Choosing between append, update, and complete output modes
- Using foreachBatch for complex sink operations
- Idempotent writes to databases with deduplication keys
- Backfilling streaming pipelines: strategies and tools
- Zero-downtime deployments with versioned processing
- Blue-green deployment of streaming applications
- Pipeline versioning with artifact tagging
- Handling config changes without restarting
- Feature flags in data processing logic
- Schema migration during live operations
- Graceful degradation under high load
- Rate limiting aggressive consumers
- Avoiding common serialisation and classpath issues
Module 13: Security and Governance - Authentication: Kerberos, OAuth, AWS IAM roles
- Authorisation: file system, table, and column-level access
- Encryption in transit: TLS for Kafka and storage
- Encryption at rest: managing keys and volumes
- Data masking and redaction in streaming output
- Audit trails for data access and modification
- PII detection and handling in real-time flows
- Compliance with GDPR, CCPA, HIPAA requirements
- Row and column filtering based on user roles
- Secure credential management with vaults
- Network segmentation and firewall rules
- Monitoring for unauthorised access attempts
- Data retention policies in streaming sinks
- Secure logging: masking sensitive payloads
- Role-based access in Databricks and cloud platforms
Module 14: Advanced Use Cases and Cross-Industry Applications - Fraud detection: real-time anomaly scoring
- IoT telemetry: processing sensor data streams
- Clickstream analysis for user journey mapping
- Real-time pricing engines in e-commerce
- Supply chain event tracking with geospatial context
- Healthcare monitoring: vital sign streaming
- Telecom call detail record processing
- Ad tech: bidstream analysis and optimisation
- Energy grid monitoring and fault detection
- Financial market data: tick processing and aggregation
- Social sentiment tracking from public feeds
- Server log analysis for anomaly detection
- Real-time inventory updates and reconciliation
- Location-based alerts and geofencing
- Automotive telematics and predictive diagnostics
Module 15: Certification, Career Advancement, and Next Steps - Preparing for the final assessment: format and expectations
- Project submission: building a production-grade pipeline
- Reviewing performance, reliability, and observability
- Demonstrating watermark, recovery, and idempotency
- Validating schema evolution and error handling
- Documentation standards for engineering artefacts
- Peer review framework for best practice alignment
- Earning your Certificate of Completion from The Art of Service
- Adding certification to LinkedIn and professional profiles
- How hiring managers view The Art of Service credentials
- Using your project as a portfolio piece
- Salary benchmarks for real-time data engineers
- Negotiating promotions based on new capabilities
- Transitioning into roles with higher responsibility
- Continuing education paths in data architecture
- Identifying bottlenecks using Spark UI metrics
- Monitoring input rate, processing rate, and batch duration
- Tuning micro-batch intervals for optimal throughput
- Executor memory tuning: spark.executor.memoryOverhead
- Garbage collection tuning for low-pause applications
- Shuffle partition sizing and autoscaling
- Skew handling: salting and custom partitioners
- Data skew detection using histogram analysis
- Broadcast join thresholds and auto-broadcast settings
- Join optimisation: broadcast vs shuffle vs sort-merge
- Caching strategies for frequently accessed datasets
- File size optimisation in streaming sinks
- Query plan improvements using hints and repartitioning
- Coalescing small files in streaming output
- Cost-based optimisation with table statistics
Module 6: Fault Tolerance and Reliability - Checkpointing: exactly-one semantics and fault recovery
- Checkpoint location best practices: durability and access patterns
- Handling source failures: Kafka rebalances, network drops
- Sink resiliency: retry logic and error handling
- Idempotent write patterns for safe reprocessing
- At-least-once vs exactly-once sink guarantees
- Monitoring for data loss and duplication
- End-to-end latency tracking and measurement
- Data lineage and traceability in streaming jobs
- Idempotent processing: deduplication with event keys
- POJO and Avro schema evolution strategies
- Handling schema drift in JSON and semi-structured data
- Recovery from corrupted checkpoint files
- Testing recovery scenarios in staging environments
- Ensuring consistency across distributed components
Module 7: Streaming Data Sources and Sinks - Kafka integration: subscribing to topics by pattern
- Kafka SSL, SASL, and security configuration
- Reading Avro from Confluent Schema Registry
- Writing structured streams to Kafka topics
- File source: monitoring directories for new data
- Cloud storage: reading from S3, ADLS, GCS with event triggers
- Socket source: use cases and limitations
- Rate source for stress testing and benchmarking
- JDBC sink: upsert patterns with merge operations
- Delta Lake as a streaming sink: ACID guarantees
- Console and memory sinks for development and testing
- Custom sink development: implementing ForeachWriter
- Writing to Elasticsearch with bulk indexing
- Integration with Amazon Kinesis Data Streams
- Pulsar and RabbitMQ connector patterns
Module 8: State Management and Advanced Operations - State store backends: RocksDB vs in-memory options
- Configuring state cleanup policies
- Custom state management using mapGroupsWithState
- Using applyInPandasWithState for Python workloads
- Session window operations with dynamic gaps
- Sessionisation of user event streams
- Pattern detection with sequence matching
- Fraud detection using time-based event sequences
- Session expiry and timeout handling
- Aggregating over user-defined window functions
- Processing time triggers for early results
- Delta watermark propagation in multi-stage pipelines
- Handling timezone-aware event timestamps
- Custom watermark assignment per event
- State expiration and TTL configuration
Module 9: Integration with Modern Data Stack - Delta Lake: unified batch and streaming layer
- Merging streaming results with MERGE INTO syntax
- Time travel and versioning in Delta tables
- Z-ordering for query performance optimisation
- Optimising Delta with VACUUM and OPTIMIZE
- Schema enforcement and evolution in streaming writes
- Unity Catalog integration for data governance
- Lineage tracking across Spark and metadata layers
- Integration with Apache Iceberg and Hudi
- Streaming into medallion architecture: bronze, silver, gold
- Metadata management with Apache Atlas
- Event time alignment across data zones
- Scheduling dependencies with Apache Airflow
- Orchestration using Prefect and Dagster
- Event validation using Great Expectations
Module 10: Monitoring, Observability, and Alerting - Spark UI: interpreting streaming query details
- Key metrics: input rate, processing rate, latency
- Streaming QueryListener for custom monitoring
- Integrating with Prometheus and Grafana
- Pushing metrics to Datadog or New Relic
- Custom counters and gauges using metrics system
- Logging best practices: structured JSON output
- Centralised log aggregation with ELK stack
- Alerting on lag, backpressure, or failures
- Building a dashboard for pipeline health
- SLOs and SLIs for streaming systems
- Detecting data drift in continuous pipelines
- Error tracking with Sentry or similar tools
- Correlating logs using trace IDs and request contexts
- Audit logging for compliance and governance
Module 11: Testing and Quality Assurance - Unit testing streaming logic with Spark testing tools
- Mocking Kafka and file sources for local testing
- Testing watermark and late data handling
- Validating stateful operations using controlled input
- Golden dataset testing with expected outputs
- Data validation using row-level assertions
- Schema compatibility checks in pipelines
- Testing idempotency and recovery scenarios
- Performance benchmarks with synthetic loads
- Load testing with large-scale event generation
- Checking for memory leaks in long-running jobs
- Integration testing across sink and downstream systems
- Automating tests using CI/CD pipelines
- Snapshot testing for structured output validation
- Ensuring reproducibility in test environments
Module 12: Production Patterns and Anti-Patterns - Avoiding unbounded state growth in aggregations
- Managing micro-batch sizes for stability
- Choosing between append, update, and complete output modes
- Using foreachBatch for complex sink operations
- Idempotent writes to databases with deduplication keys
- Backfilling streaming pipelines: strategies and tools
- Zero-downtime deployments with versioned processing
- Blue-green deployment of streaming applications
- Pipeline versioning with artifact tagging
- Handling config changes without restarting
- Feature flags in data processing logic
- Schema migration during live operations
- Graceful degradation under high load
- Rate limiting aggressive consumers
- Avoiding common serialisation and classpath issues
Module 13: Security and Governance - Authentication: Kerberos, OAuth, AWS IAM roles
- Authorisation: file system, table, and column-level access
- Encryption in transit: TLS for Kafka and storage
- Encryption at rest: managing keys and volumes
- Data masking and redaction in streaming output
- Audit trails for data access and modification
- PII detection and handling in real-time flows
- Compliance with GDPR, CCPA, HIPAA requirements
- Row and column filtering based on user roles
- Secure credential management with vaults
- Network segmentation and firewall rules
- Monitoring for unauthorised access attempts
- Data retention policies in streaming sinks
- Secure logging: masking sensitive payloads
- Role-based access in Databricks and cloud platforms
Module 14: Advanced Use Cases and Cross-Industry Applications - Fraud detection: real-time anomaly scoring
- IoT telemetry: processing sensor data streams
- Clickstream analysis for user journey mapping
- Real-time pricing engines in e-commerce
- Supply chain event tracking with geospatial context
- Healthcare monitoring: vital sign streaming
- Telecom call detail record processing
- Ad tech: bidstream analysis and optimisation
- Energy grid monitoring and fault detection
- Financial market data: tick processing and aggregation
- Social sentiment tracking from public feeds
- Server log analysis for anomaly detection
- Real-time inventory updates and reconciliation
- Location-based alerts and geofencing
- Automotive telematics and predictive diagnostics
Module 15: Certification, Career Advancement, and Next Steps - Preparing for the final assessment: format and expectations
- Project submission: building a production-grade pipeline
- Reviewing performance, reliability, and observability
- Demonstrating watermark, recovery, and idempotency
- Validating schema evolution and error handling
- Documentation standards for engineering artefacts
- Peer review framework for best practice alignment
- Earning your Certificate of Completion from The Art of Service
- Adding certification to LinkedIn and professional profiles
- How hiring managers view The Art of Service credentials
- Using your project as a portfolio piece
- Salary benchmarks for real-time data engineers
- Negotiating promotions based on new capabilities
- Transitioning into roles with higher responsibility
- Continuing education paths in data architecture
- Kafka integration: subscribing to topics by pattern
- Kafka SSL, SASL, and security configuration
- Reading Avro from Confluent Schema Registry
- Writing structured streams to Kafka topics
- File source: monitoring directories for new data
- Cloud storage: reading from S3, ADLS, GCS with event triggers
- Socket source: use cases and limitations
- Rate source for stress testing and benchmarking
- JDBC sink: upsert patterns with merge operations
- Delta Lake as a streaming sink: ACID guarantees
- Console and memory sinks for development and testing
- Custom sink development: implementing ForeachWriter
- Writing to Elasticsearch with bulk indexing
- Integration with Amazon Kinesis Data Streams
- Pulsar and RabbitMQ connector patterns
Module 8: State Management and Advanced Operations - State store backends: RocksDB vs in-memory options
- Configuring state cleanup policies
- Custom state management using mapGroupsWithState
- Using applyInPandasWithState for Python workloads
- Session window operations with dynamic gaps
- Sessionisation of user event streams
- Pattern detection with sequence matching
- Fraud detection using time-based event sequences
- Session expiry and timeout handling
- Aggregating over user-defined window functions
- Processing time triggers for early results
- Delta watermark propagation in multi-stage pipelines
- Handling timezone-aware event timestamps
- Custom watermark assignment per event
- State expiration and TTL configuration
Module 9: Integration with Modern Data Stack - Delta Lake: unified batch and streaming layer
- Merging streaming results with MERGE INTO syntax
- Time travel and versioning in Delta tables
- Z-ordering for query performance optimisation
- Optimising Delta with VACUUM and OPTIMIZE
- Schema enforcement and evolution in streaming writes
- Unity Catalog integration for data governance
- Lineage tracking across Spark and metadata layers
- Integration with Apache Iceberg and Hudi
- Streaming into medallion architecture: bronze, silver, gold
- Metadata management with Apache Atlas
- Event time alignment across data zones
- Scheduling dependencies with Apache Airflow
- Orchestration using Prefect and Dagster
- Event validation using Great Expectations
Module 10: Monitoring, Observability, and Alerting - Spark UI: interpreting streaming query details
- Key metrics: input rate, processing rate, latency
- Streaming QueryListener for custom monitoring
- Integrating with Prometheus and Grafana
- Pushing metrics to Datadog or New Relic
- Custom counters and gauges using metrics system
- Logging best practices: structured JSON output
- Centralised log aggregation with ELK stack
- Alerting on lag, backpressure, or failures
- Building a dashboard for pipeline health
- SLOs and SLIs for streaming systems
- Detecting data drift in continuous pipelines
- Error tracking with Sentry or similar tools
- Correlating logs using trace IDs and request contexts
- Audit logging for compliance and governance
Module 11: Testing and Quality Assurance - Unit testing streaming logic with Spark testing tools
- Mocking Kafka and file sources for local testing
- Testing watermark and late data handling
- Validating stateful operations using controlled input
- Golden dataset testing with expected outputs
- Data validation using row-level assertions
- Schema compatibility checks in pipelines
- Testing idempotency and recovery scenarios
- Performance benchmarks with synthetic loads
- Load testing with large-scale event generation
- Checking for memory leaks in long-running jobs
- Integration testing across sink and downstream systems
- Automating tests using CI/CD pipelines
- Snapshot testing for structured output validation
- Ensuring reproducibility in test environments
Module 12: Production Patterns and Anti-Patterns - Avoiding unbounded state growth in aggregations
- Managing micro-batch sizes for stability
- Choosing between append, update, and complete output modes
- Using foreachBatch for complex sink operations
- Idempotent writes to databases with deduplication keys
- Backfilling streaming pipelines: strategies and tools
- Zero-downtime deployments with versioned processing
- Blue-green deployment of streaming applications
- Pipeline versioning with artifact tagging
- Handling config changes without restarting
- Feature flags in data processing logic
- Schema migration during live operations
- Graceful degradation under high load
- Rate limiting aggressive consumers
- Avoiding common serialisation and classpath issues
Module 13: Security and Governance - Authentication: Kerberos, OAuth, AWS IAM roles
- Authorisation: file system, table, and column-level access
- Encryption in transit: TLS for Kafka and storage
- Encryption at rest: managing keys and volumes
- Data masking and redaction in streaming output
- Audit trails for data access and modification
- PII detection and handling in real-time flows
- Compliance with GDPR, CCPA, HIPAA requirements
- Row and column filtering based on user roles
- Secure credential management with vaults
- Network segmentation and firewall rules
- Monitoring for unauthorised access attempts
- Data retention policies in streaming sinks
- Secure logging: masking sensitive payloads
- Role-based access in Databricks and cloud platforms
Module 14: Advanced Use Cases and Cross-Industry Applications - Fraud detection: real-time anomaly scoring
- IoT telemetry: processing sensor data streams
- Clickstream analysis for user journey mapping
- Real-time pricing engines in e-commerce
- Supply chain event tracking with geospatial context
- Healthcare monitoring: vital sign streaming
- Telecom call detail record processing
- Ad tech: bidstream analysis and optimisation
- Energy grid monitoring and fault detection
- Financial market data: tick processing and aggregation
- Social sentiment tracking from public feeds
- Server log analysis for anomaly detection
- Real-time inventory updates and reconciliation
- Location-based alerts and geofencing
- Automotive telematics and predictive diagnostics
Module 15: Certification, Career Advancement, and Next Steps - Preparing for the final assessment: format and expectations
- Project submission: building a production-grade pipeline
- Reviewing performance, reliability, and observability
- Demonstrating watermark, recovery, and idempotency
- Validating schema evolution and error handling
- Documentation standards for engineering artefacts
- Peer review framework for best practice alignment
- Earning your Certificate of Completion from The Art of Service
- Adding certification to LinkedIn and professional profiles
- How hiring managers view The Art of Service credentials
- Using your project as a portfolio piece
- Salary benchmarks for real-time data engineers
- Negotiating promotions based on new capabilities
- Transitioning into roles with higher responsibility
- Continuing education paths in data architecture
- Delta Lake: unified batch and streaming layer
- Merging streaming results with MERGE INTO syntax
- Time travel and versioning in Delta tables
- Z-ordering for query performance optimisation
- Optimising Delta with VACUUM and OPTIMIZE
- Schema enforcement and evolution in streaming writes
- Unity Catalog integration for data governance
- Lineage tracking across Spark and metadata layers
- Integration with Apache Iceberg and Hudi
- Streaming into medallion architecture: bronze, silver, gold
- Metadata management with Apache Atlas
- Event time alignment across data zones
- Scheduling dependencies with Apache Airflow
- Orchestration using Prefect and Dagster
- Event validation using Great Expectations
Module 10: Monitoring, Observability, and Alerting - Spark UI: interpreting streaming query details
- Key metrics: input rate, processing rate, latency
- Streaming QueryListener for custom monitoring
- Integrating with Prometheus and Grafana
- Pushing metrics to Datadog or New Relic
- Custom counters and gauges using metrics system
- Logging best practices: structured JSON output
- Centralised log aggregation with ELK stack
- Alerting on lag, backpressure, or failures
- Building a dashboard for pipeline health
- SLOs and SLIs for streaming systems
- Detecting data drift in continuous pipelines
- Error tracking with Sentry or similar tools
- Correlating logs using trace IDs and request contexts
- Audit logging for compliance and governance
Module 11: Testing and Quality Assurance - Unit testing streaming logic with Spark testing tools
- Mocking Kafka and file sources for local testing
- Testing watermark and late data handling
- Validating stateful operations using controlled input
- Golden dataset testing with expected outputs
- Data validation using row-level assertions
- Schema compatibility checks in pipelines
- Testing idempotency and recovery scenarios
- Performance benchmarks with synthetic loads
- Load testing with large-scale event generation
- Checking for memory leaks in long-running jobs
- Integration testing across sink and downstream systems
- Automating tests using CI/CD pipelines
- Snapshot testing for structured output validation
- Ensuring reproducibility in test environments
Module 12: Production Patterns and Anti-Patterns - Avoiding unbounded state growth in aggregations
- Managing micro-batch sizes for stability
- Choosing between append, update, and complete output modes
- Using foreachBatch for complex sink operations
- Idempotent writes to databases with deduplication keys
- Backfilling streaming pipelines: strategies and tools
- Zero-downtime deployments with versioned processing
- Blue-green deployment of streaming applications
- Pipeline versioning with artifact tagging
- Handling config changes without restarting
- Feature flags in data processing logic
- Schema migration during live operations
- Graceful degradation under high load
- Rate limiting aggressive consumers
- Avoiding common serialisation and classpath issues
Module 13: Security and Governance - Authentication: Kerberos, OAuth, AWS IAM roles
- Authorisation: file system, table, and column-level access
- Encryption in transit: TLS for Kafka and storage
- Encryption at rest: managing keys and volumes
- Data masking and redaction in streaming output
- Audit trails for data access and modification
- PII detection and handling in real-time flows
- Compliance with GDPR, CCPA, HIPAA requirements
- Row and column filtering based on user roles
- Secure credential management with vaults
- Network segmentation and firewall rules
- Monitoring for unauthorised access attempts
- Data retention policies in streaming sinks
- Secure logging: masking sensitive payloads
- Role-based access in Databricks and cloud platforms
Module 14: Advanced Use Cases and Cross-Industry Applications - Fraud detection: real-time anomaly scoring
- IoT telemetry: processing sensor data streams
- Clickstream analysis for user journey mapping
- Real-time pricing engines in e-commerce
- Supply chain event tracking with geospatial context
- Healthcare monitoring: vital sign streaming
- Telecom call detail record processing
- Ad tech: bidstream analysis and optimisation
- Energy grid monitoring and fault detection
- Financial market data: tick processing and aggregation
- Social sentiment tracking from public feeds
- Server log analysis for anomaly detection
- Real-time inventory updates and reconciliation
- Location-based alerts and geofencing
- Automotive telematics and predictive diagnostics
Module 15: Certification, Career Advancement, and Next Steps - Preparing for the final assessment: format and expectations
- Project submission: building a production-grade pipeline
- Reviewing performance, reliability, and observability
- Demonstrating watermark, recovery, and idempotency
- Validating schema evolution and error handling
- Documentation standards for engineering artefacts
- Peer review framework for best practice alignment
- Earning your Certificate of Completion from The Art of Service
- Adding certification to LinkedIn and professional profiles
- How hiring managers view The Art of Service credentials
- Using your project as a portfolio piece
- Salary benchmarks for real-time data engineers
- Negotiating promotions based on new capabilities
- Transitioning into roles with higher responsibility
- Continuing education paths in data architecture
- Unit testing streaming logic with Spark testing tools
- Mocking Kafka and file sources for local testing
- Testing watermark and late data handling
- Validating stateful operations using controlled input
- Golden dataset testing with expected outputs
- Data validation using row-level assertions
- Schema compatibility checks in pipelines
- Testing idempotency and recovery scenarios
- Performance benchmarks with synthetic loads
- Load testing with large-scale event generation
- Checking for memory leaks in long-running jobs
- Integration testing across sink and downstream systems
- Automating tests using CI/CD pipelines
- Snapshot testing for structured output validation
- Ensuring reproducibility in test environments
Module 12: Production Patterns and Anti-Patterns - Avoiding unbounded state growth in aggregations
- Managing micro-batch sizes for stability
- Choosing between append, update, and complete output modes
- Using foreachBatch for complex sink operations
- Idempotent writes to databases with deduplication keys
- Backfilling streaming pipelines: strategies and tools
- Zero-downtime deployments with versioned processing
- Blue-green deployment of streaming applications
- Pipeline versioning with artifact tagging
- Handling config changes without restarting
- Feature flags in data processing logic
- Schema migration during live operations
- Graceful degradation under high load
- Rate limiting aggressive consumers
- Avoiding common serialisation and classpath issues
Module 13: Security and Governance - Authentication: Kerberos, OAuth, AWS IAM roles
- Authorisation: file system, table, and column-level access
- Encryption in transit: TLS for Kafka and storage
- Encryption at rest: managing keys and volumes
- Data masking and redaction in streaming output
- Audit trails for data access and modification
- PII detection and handling in real-time flows
- Compliance with GDPR, CCPA, HIPAA requirements
- Row and column filtering based on user roles
- Secure credential management with vaults
- Network segmentation and firewall rules
- Monitoring for unauthorised access attempts
- Data retention policies in streaming sinks
- Secure logging: masking sensitive payloads
- Role-based access in Databricks and cloud platforms
Module 14: Advanced Use Cases and Cross-Industry Applications - Fraud detection: real-time anomaly scoring
- IoT telemetry: processing sensor data streams
- Clickstream analysis for user journey mapping
- Real-time pricing engines in e-commerce
- Supply chain event tracking with geospatial context
- Healthcare monitoring: vital sign streaming
- Telecom call detail record processing
- Ad tech: bidstream analysis and optimisation
- Energy grid monitoring and fault detection
- Financial market data: tick processing and aggregation
- Social sentiment tracking from public feeds
- Server log analysis for anomaly detection
- Real-time inventory updates and reconciliation
- Location-based alerts and geofencing
- Automotive telematics and predictive diagnostics
Module 15: Certification, Career Advancement, and Next Steps - Preparing for the final assessment: format and expectations
- Project submission: building a production-grade pipeline
- Reviewing performance, reliability, and observability
- Demonstrating watermark, recovery, and idempotency
- Validating schema evolution and error handling
- Documentation standards for engineering artefacts
- Peer review framework for best practice alignment
- Earning your Certificate of Completion from The Art of Service
- Adding certification to LinkedIn and professional profiles
- How hiring managers view The Art of Service credentials
- Using your project as a portfolio piece
- Salary benchmarks for real-time data engineers
- Negotiating promotions based on new capabilities
- Transitioning into roles with higher responsibility
- Continuing education paths in data architecture
- Authentication: Kerberos, OAuth, AWS IAM roles
- Authorisation: file system, table, and column-level access
- Encryption in transit: TLS for Kafka and storage
- Encryption at rest: managing keys and volumes
- Data masking and redaction in streaming output
- Audit trails for data access and modification
- PII detection and handling in real-time flows
- Compliance with GDPR, CCPA, HIPAA requirements
- Row and column filtering based on user roles
- Secure credential management with vaults
- Network segmentation and firewall rules
- Monitoring for unauthorised access attempts
- Data retention policies in streaming sinks
- Secure logging: masking sensitive payloads
- Role-based access in Databricks and cloud platforms
Module 14: Advanced Use Cases and Cross-Industry Applications - Fraud detection: real-time anomaly scoring
- IoT telemetry: processing sensor data streams
- Clickstream analysis for user journey mapping
- Real-time pricing engines in e-commerce
- Supply chain event tracking with geospatial context
- Healthcare monitoring: vital sign streaming
- Telecom call detail record processing
- Ad tech: bidstream analysis and optimisation
- Energy grid monitoring and fault detection
- Financial market data: tick processing and aggregation
- Social sentiment tracking from public feeds
- Server log analysis for anomaly detection
- Real-time inventory updates and reconciliation
- Location-based alerts and geofencing
- Automotive telematics and predictive diagnostics
Module 15: Certification, Career Advancement, and Next Steps - Preparing for the final assessment: format and expectations
- Project submission: building a production-grade pipeline
- Reviewing performance, reliability, and observability
- Demonstrating watermark, recovery, and idempotency
- Validating schema evolution and error handling
- Documentation standards for engineering artefacts
- Peer review framework for best practice alignment
- Earning your Certificate of Completion from The Art of Service
- Adding certification to LinkedIn and professional profiles
- How hiring managers view The Art of Service credentials
- Using your project as a portfolio piece
- Salary benchmarks for real-time data engineers
- Negotiating promotions based on new capabilities
- Transitioning into roles with higher responsibility
- Continuing education paths in data architecture
- Preparing for the final assessment: format and expectations
- Project submission: building a production-grade pipeline
- Reviewing performance, reliability, and observability
- Demonstrating watermark, recovery, and idempotency
- Validating schema evolution and error handling
- Documentation standards for engineering artefacts
- Peer review framework for best practice alignment
- Earning your Certificate of Completion from The Art of Service
- Adding certification to LinkedIn and professional profiles
- How hiring managers view The Art of Service credentials
- Using your project as a portfolio piece
- Salary benchmarks for real-time data engineers
- Negotiating promotions based on new capabilities
- Transitioning into roles with higher responsibility
- Continuing education paths in data architecture