Data Science in Big Data

This curriculum spans the technical breadth of a multi-workshop program for data engineering and machine learning operations, covering the design, deployment, and governance of production-scale data systems as typically encountered in large-scale cloud analytics and AI initiatives.

Module 1: Architecting Scalable Data Ingestion Pipelines

• Designing idempotent data ingestion workflows to handle duplicate messages from distributed sources like Kafka or Kinesis.
• Selecting between batch and micro-batch ingestion based on SLA requirements and source system capabilities.
• Implementing schema validation and schema evolution strategies using Avro or Protobuf in streaming pipelines.
• Configuring backpressure handling in Spark Streaming or Flink to prevent consumer lag under load spikes.
• Securing data in transit using TLS and managing credential rotation for cloud storage endpoints (e.g., S3, ADLS).
• Partitioning raw data by time and source to optimize query performance and lifecycle management in data lakes.
• Monitoring ingestion latency and error rates using structured logging and distributed tracing.
• Choosing between push and pull ingestion models for third-party API integrations with rate limits and quotas.

Module 2: Distributed Data Storage and Lakehouse Design

• Implementing ACID transactions in Delta Lake or Apache Iceberg to ensure consistency during concurrent writes.
• Designing partitioning and bucketing strategies in Parquet or ORC formats to reduce query scan times.
• Managing metadata performance at scale using centralized catalog services like AWS Glue or Unity Catalog.
• Enforcing data retention and GDPR compliance through automated lifecycle policies on object storage.
• Optimizing storage costs by tiering cold data to lower-cost storage classes (e.g., S3 Glacier, Blob Archive).
• Implementing fine-grained access control using column- and row-level security in data lakehouses.
• Handling schema drift in evolving datasets using schema inference with validation guardrails.
• Configuring replication and disaster recovery for multi-region data availability.

Module 3: Large-Scale Data Processing with Spark and Flink

• Tuning Spark executor memory and core allocation to balance parallelism and JVM garbage collection overhead.
• Managing shuffle partitions to avoid skew and optimize resource utilization in join operations.
• Implementing broadcast joins for small lookup tables to reduce shuffle traffic in ETL jobs.
• Using checkpointing and savepoints in Flink for fault tolerance in stateful stream processing.
• Optimizing data serialization with Kryo or FST to reduce network and memory footprint.
• Instrumenting Spark UI and Flink Web UI metrics to diagnose performance bottlenecks in production jobs.
• Configuring dynamic allocation and speculative execution to handle straggler tasks in heterogeneous clusters.
• Integrating custom UDFs with type safety and performance considerations in PySpark or Scala.

Module 4: Feature Engineering at Scale

• Building feature stores with versioned datasets to ensure reproducibility across model training and serving.
• Implementing time-based aggregation windows for feature computation to prevent data leakage.
• Scheduling and orchestrating feature computation pipelines using Airflow or Prefect with dependency resolution.
• Storing precomputed features in low-latency stores (e.g., Redis, Cassandra) for real-time inference.
• Managing feature drift detection by monitoring statistical properties over sliding time windows.
• Normalizing and encoding high-cardinality categorical features using distributed preprocessing.
• Handling missing data in streaming feature pipelines with imputation strategies tied to business logic.
• Documenting feature lineage from raw data to model input for audit and compliance purposes.

Module 5: Machine Learning Model Development on Big Data

• Selecting between distributed ML frameworks (e.g., Spark MLlib, XGBoost on Dask, TensorFlow Extended) based on data size and model complexity.
• Implementing distributed hyperparameter tuning using Bayesian optimization with Ray Tune or Hyperopt.
• Partitioning training data by time to simulate real-world model evaluation and avoid look-ahead bias.
• Managing class imbalance in large datasets using stratified sampling or weighted loss functions.
• Validating model performance across segments (e.g., geography, user cohort) to detect bias and fairness issues.
• Reducing dimensionality in high-cardinality feature spaces using PCA or autoencoders in distributed environments.
• Integrating custom loss functions in deep learning models for domain-specific optimization objectives.
• Logging model metrics, parameters, and artifacts using MLflow or Weights & Biases in multi-user environments.

Module 6: Model Deployment and Real-Time Inference

• Containerizing models using Docker and serving via Kubernetes with horizontal pod autoscaling.
• Choosing between online, batch, and streaming inference based on latency and throughput requirements.
• Implementing A/B testing and shadow mode deployment to validate model behavior in production.
• Designing model rollback procedures with versioned artifact storage and configuration management.
• Integrating feature transformation logic into serving pipelines to ensure consistency with training.
• Monitoring prediction latency and error rates under variable load using Prometheus and Grafana.
• Securing model endpoints with API gateways, rate limiting, and mutual TLS authentication.
• Optimizing inference performance using model quantization or ONNX runtime for CPU-bound environments.

Module 7: Data and Model Governance

• Implementing data classification and tagging to enforce handling policies for PII and sensitive attributes.
• Establishing data ownership and stewardship roles within cross-functional teams.
• Creating audit trails for data access and model predictions to support regulatory compliance.
• Managing consent and data subject rights fulfillment in automated data processing systems.
• Documenting data provenance and model decision logic for explainability and regulatory audits.
• Enforcing model validation gates in CI/CD pipelines using statistical and business rule checks.
• Conducting bias and fairness assessments using tools like AIF360 across demographic groups.
• Integrating with enterprise data governance platforms (e.g., Collibra, Alation) for metadata synchronization.

Module 8: Monitoring, Observability, and Incident Response

• Setting up anomaly detection on data pipeline metrics (e.g., row counts, latency) using statistical thresholds.
• Implementing structured logging with correlation IDs to trace data flow across microservices.
• Creating alerting rules for data quality violations (e.g., null rates, distribution shifts) with escalation paths.
• Instrumenting model performance decay detection using drift metrics (e.g., PSI, KL divergence).
• Conducting root cause analysis for pipeline failures using log aggregation and distributed tracing tools.
• Managing incident response playbooks for data corruption, model degradation, or service outages.
• Performing capacity planning and load testing for peak data ingestion and query workloads.
• Rotating credentials and certificates automatically using secret management systems (e.g., HashiCorp Vault).

Module 9: Cost Optimization and Resource Management

• Right-sizing cluster resources based on historical utilization patterns and workload forecasting.
• Using spot instances or preemptible VMs for fault-tolerant batch workloads with checkpointing.
• Implementing auto-scaling policies for streaming and batch processing clusters.
• Compressing intermediate data and optimizing shuffle spills to reduce I/O costs.
• Tracking cost attribution by team, project, or pipeline using cloud billing tags and allocation IDs.
• Archiving or deleting stale datasets and model artifacts to reduce storage expenditure.
• Evaluating total cost of ownership (TCO) when choosing between managed and self-hosted services.
• Optimizing query performance through materialized views, caching, and indexing strategies.