Description

This curriculum spans the technical and operational rigor of a multi-workshop engineering program, covering the design, deployment, and governance of real-time data systems as practiced in large-scale, regulated enterprises.

Module 1: Architecting Real-Time Data Ingestion Pipelines

Selecting between message brokers (Kafka vs Pulsar vs RabbitMQ) based on throughput, durability, and multi-tenancy requirements
Designing schema evolution strategies using Avro or Protobuf with backward and forward compatibility constraints
Implementing idempotent consumers to handle duplicate messages during retries in high-volume streams
Configuring partitioning strategies in Kafka to balance load and ensure event ordering per key
Integrating change data capture (CDC) tools like Debezium with transactional databases without impacting source performance
Setting up dead-letter queues and monitoring for failed message processing in streaming ETL workflows
Securing data in transit and at rest using TLS and encryption key management across ingestion components
Dimensioning cluster resources for auto-scaling based on lag metrics and peak load forecasts

Module 2: Stream Processing Engine Selection and Configuration

Evaluating Flink, Spark Streaming, and Kafka Streams based on latency SLAs and state management needs
Configuring checkpointing intervals and state backends in Flink to balance recovery time and performance
Implementing event-time processing with watermarks to handle late-arriving data in financial monitoring systems
Managing operator state size to prevent out-of-memory failures during prolonged backpressure
Deploying stream applications in Kubernetes with resource limits and health probes for resilience
Choosing between at-least-once and exactly-once processing guarantees based on business impact of duplication
Optimizing window aggregation strategies (tumbling, sliding, session) for real-time KPI dashboards
Debugging and profiling performance bottlenecks using Flink’s web UI and task manager logs

Module 3: Real-Time Feature Engineering for ML Systems

Designing feature stores with low-latency retrieval for online inference in recommendation engines
Synchronizing feature computation between batch and streaming pipelines to prevent training-serving skew
Implementing time-weighted aggregations (e.g., decayed counts) over sliding windows for dynamic user profiles
Versioning feature schemas and tracking lineage for auditability in regulated industries
Managing cache coherence between Redis and feature store databases under high update rates
Validating feature distributions in real-time to detect data drift before model degradation
Securing access to feature endpoints using OAuth2 and attribute-based access control
Estimating compute costs for real-time feature transformations at scale

Module 4: Operationalizing Real-Time Machine Learning Models

Deploying models using TensorFlow Serving or TorchServe with A/B testing and canary rollout strategies
Designing fallback mechanisms for model degradation or timeout scenarios in customer-facing APIs
Instrumenting model inference with tracing to diagnose latency spikes in production
Integrating model monitoring tools to track prediction drift and input distribution shifts
Managing GPU vs CPU allocation for real-time inference workloads based on latency and cost
Implementing request batching without violating end-to-end latency SLAs
Rotating models in production with zero downtime using Kubernetes blue-green deployments
Enforcing model governance policies including approval workflows and audit trails

Module 5: Real-Time Analytics and Dashboarding Infrastructure

Selecting OLAP databases (Druid, ClickHouse, Pinot) based on query patterns and data retention policies
Designing pre-aggregated rollups to accelerate dashboard queries without sacrificing granularity
Implementing row-level security in dashboards for multi-tenant SaaS applications
Configuring data retention and tiered storage to manage costs for high-frequency telemetry
Integrating real-time dashboards with incident management tools using alert webhooks
Optimizing query performance through indexing strategies and partition pruning
Handling schema changes in streaming sources without breaking downstream visualizations
Validating data freshness SLAs using watermark tracking across the pipeline

Module 6: Data Quality and Anomaly Detection in Streaming Workflows

Embedding data validation rules (e.g., range checks, referential integrity) in stream processors
Designing feedback loops to route bad records to remediation queues without blocking pipelines
Implementing statistical anomaly detection on metric time series using exponential smoothing
Calibrating false positive rates in anomaly alerts based on operational burden and severity
Correlating anomalies across multiple data streams to identify root causes
Using synthetic data injection to test detection logic during low-traffic periods
Versioning data quality rules and linking them to regulatory compliance requirements
Automating reprocessing of corrected data into downstream systems

Module 7: Governance, Compliance, and Auditability of Real-Time Systems

Implementing data lineage tracking across streaming components for regulatory audits
Enabling data masking and anonymization in real-time pipelines for GDPR and CCPA compliance
Logging data access patterns and user queries for forensic investigations
Establishing data retention and deletion workflows for personal data in stream state
Conducting DPIAs (Data Protection Impact Assessments) for new real-time use cases
Managing encryption key rotation for data at rest in stateful stream processors
Documenting data provenance from source to insight for stakeholder transparency
Enforcing role-based access control across ingestion, processing, and querying layers

Module 8: Scaling and Cost Optimization of Real-Time Architectures

Right-sizing cluster nodes based on CPU, memory, and network utilization metrics
Implementing autoscaling policies using custom metrics from stream processing frameworks
Negotiating reserved instance pricing for stable baseline workloads on cloud platforms
Optimizing data serialization and compression to reduce network egress costs
Architecting multi-region deployments for disaster recovery without data loss
Consolidating multiple pipelines into shared infrastructure to improve resource utilization
Monitoring and alerting on cost anomalies in cloud billing for real-time workloads
Conducting load testing to validate scalability before major business events

Module 9: Incident Response and Reliability Engineering for Streaming Systems

Defining SLOs and error budgets for real-time pipelines to guide reliability investments
Creating runbooks for common failure modes such as consumer lag and broker outages
Implementing circuit breakers in downstream services to prevent cascading failures
Conducting blameless postmortems after stream processing incidents
Simulating regional outages to test failover procedures and data consistency
Using synthetic transactions to monitor end-to-end pipeline health continuously
Rotating on-call responsibilities with clear escalation paths for production alerts
Integrating observability tools (logs, metrics, traces) into a unified monitoring dashboard