This curriculum spans the technical and operational complexity of a multi-workshop program focused on building and maintaining enterprise-grade streaming data platforms, comparable to advisory engagements for real-time data infrastructure in large-scale, regulated organisations.
Module 1: Architecting Real-Time Data Ingestion Pipelines
- Designing schema evolution strategies in Kafka topics to support backward and forward compatibility during event format changes.
- Selecting between pull-based (Kafka Connect) and push-based (custom producers) ingestion based on source system capabilities and latency requirements.
- Implementing backpressure handling in stream processors to prevent downstream system overload during traffic spikes.
- Configuring partitioning strategies in message queues to balance parallelism and data locality for downstream consumers.
- Evaluating the trade-off between message durability (replication factor, ack settings) and throughput in distributed messaging systems.
- Integrating change data capture (CDC) tools like Debezium with legacy databases without impacting transactional performance.
Module 2: Streaming Compute Engine Selection and Configuration
- Choosing between stateful streaming frameworks (Flink, Spark Streaming) based on exactly-once processing requirements and fault tolerance needs.
- Configuring windowing semantics (tumbling, sliding, session) to align with business SLAs for anomaly detection and aggregation.
- Managing state backend storage (RocksDB vs. in-memory) in Flink based on state size and recovery time objectives (RTO).
- Implementing watermark strategies to handle late-arriving data while minimizing processing delays.
- Optimizing parallelism and task slot allocation to prevent resource contention in cluster environments.
- Deploying streaming jobs in high-availability mode with checkpointing enabled across distributed storage.
Module 3: Real-Time Data Modeling and Schema Management
- Defining canonical event schemas using Avro or Protobuf to enforce consistency across microservices.
- Implementing schema registry governance with versioning, compatibility checks, and access controls.
- Resolving schema conflicts during event migration when multiple teams publish to shared topics.
- Designing denormalized data models for real-time dashboards to reduce query latency in serving layers.
- Embedding metadata (source, lineage, sensitivity tags) within event payloads for auditability.
- Managing schema deprecation and retirement processes in coordination with downstream consumers.
Module 4: Observability and Monitoring of Streaming Systems
- Instrumenting custom metrics (lag, throughput, error rates) in stream processors for proactive alerting.
- Correlating log entries across distributed components using trace IDs propagated through event headers.
- Setting up dynamic alert thresholds based on historical baselines to reduce false positives.
- Diagnosing backpressure root causes by analyzing consumer group lag and processing time metrics.
- Integrating streaming job metrics into centralized monitoring platforms (Prometheus, Grafana).
- Implementing health checks for state stores and checkpointing operations in managed streaming environments.
Module 5: Data Quality and Anomaly Detection in Motion
- Deploying real-time data validation rules (schema conformance, null checks, range constraints) at ingestion points.
- Implementing probabilistic data profiling (cardinality, distribution) on streaming windows to detect drift.
- Using statistical process control (SPC) charts to flag anomalies in metric time series without manual thresholds.
- Routing malformed records to quarantine topics with metadata for root cause analysis.
- Configuring deduplication logic using event keys and time windows to handle producer retries.
- Logging and escalating data quality incidents to responsible teams via automated ticketing integrations.
Module 6: Security and Compliance in Real-Time Data Flows
- Enforcing TLS encryption and mutual authentication between producers, brokers, and consumers.
- Implementing field-level encryption for sensitive data (PII) within event payloads using envelope encryption.
- Applying role-based access control (RBAC) to Kafka topics and Flink jobs based on data classification.
- Auditing data access patterns in real-time pipelines for compliance with GDPR or CCPA.
- Masking sensitive fields in logs and monitoring interfaces based on user entitlements.
- Designing data retention policies aligned with legal hold requirements for event streams.
Module 7: Integration with Decision Systems and Operational Workflows
- Triggering automated remediation workflows (e.g., service restart) based on real-time anomaly detection.
- Feeding real-time KPIs into operational dashboards with sub-second latency requirements.
- Integrating streaming alerts with incident management systems (PagerDuty, Opsgenie) using enrichment hooks.
- Synchronizing real-time model scoring outputs with customer engagement platforms (CRM, CDP).
- Coordinating event-driven updates across distributed systems using sagas and compensating actions.
- Validating end-to-end data freshness SLAs from source to consumption point using synthetic probes.
Module 8: Scaling and Cost Optimization of Real-Time Infrastructure
- Right-sizing Kafka broker clusters based on message volume, retention period, and I/O patterns.
- Implementing autoscaling policies for streaming compute resources based on lag and CPU utilization.
- Choosing between cloud-managed (Confluent Cloud, MSK) and self-hosted streaming platforms based on operational overhead.
- Compressing event payloads (Snappy, Zstandard) to reduce network and storage costs.
- Partitioning data by tenant or region to enable isolation and targeted scaling in multi-tenant systems.
- Conducting cost attribution for streaming pipelines by tagging resources with project and team identifiers.