Description

This curriculum spans the technical and operational rigor of a multi-workshop program focused on production-grade big data systems, addressing the same depth of architectural decision-making, operational trade-offs, and cross-functional coordination required in enterprise data platform migrations and internal capability builds.

Module 1: Data Ingestion Architecture at Scale

Selecting between batch and streaming ingestion based on SLA requirements and source system capabilities
Designing idempotent ingestion pipelines to handle duplicate messages from unreliable sources
Implementing schema validation at ingestion to prevent downstream processing failures
Choosing between pull and push ingestion models based on source system load tolerance
Configuring backpressure mechanisms in Kafka consumers to prevent consumer lag and system overload
Partitioning strategies for distributed ingestion to ensure even data distribution and parallel processing
Handling schema evolution during ingestion using schema registry and versioning
Securing data in transit using TLS and managing certificate rotation across ingestion components

Module 2: Distributed Storage Systems and Data Layout

Selecting file formats (Parquet, ORC, Avro) based on query patterns, compression, and schema evolution needs
Implementing partitioning and bucketing strategies to optimize query performance on petabyte-scale datasets
Managing storage tiering between hot, warm, and cold storage based on access frequency and cost
Designing lifecycle policies for automatic data archival and deletion to meet compliance
Optimizing data layout for locality in distributed file systems like HDFS or cloud object stores
Handling small file problems in distributed storage through compaction and merging jobs
Configuring replication factors in HDFS or S3 equivalents based on durability and performance trade-offs
Implementing object tagging and metadata indexing for governance and auditability

Module 3: Data Processing Frameworks and Execution Models

Choosing between Spark, Flink, and Beam based on latency, state management, and ecosystem integration
Tuning Spark executor memory and core allocation to balance resource utilization and GC overhead
Managing shuffle partitions to avoid skew and optimize disk I/O in distributed processing
Implementing checkpointing in streaming jobs to ensure fault tolerance and state recovery
Deciding between micro-batch and continuous processing based on end-to-end latency requirements
Optimizing broadcast joins versus shuffled joins based on dataset size and cluster topology
Configuring dynamic allocation in Spark clusters to respond to workload variability
Handling backpressure in streaming applications to maintain processing stability under load spikes

Module 4: Data Quality and Observability

Defining and measuring data quality dimensions (completeness, accuracy, timeliness) per domain
Implementing automated anomaly detection on data distributions using statistical thresholds
Instrumenting pipelines with structured logging and distributed tracing for root cause analysis
Setting up data freshness alerts based on watermark deviation in streaming systems
Creating data lineage graphs to track transformations from source to consumption
Integrating data profiling into CI/CD pipelines to catch regressions before deployment
Managing false positive rates in data quality rules to avoid alert fatigue
Establishing data ownership and escalation paths for data incident response

Module 5: Metadata Management and Cataloging

Selecting between open-source (Atlas, DataHub) and commercial metadata solutions based on integration needs
Automating metadata extraction from ETL jobs, query logs, and schema registries
Implementing classification and tagging policies for sensitive data discovery
Designing search and discovery interfaces for business and technical users
Synchronizing metadata across environments (dev, staging, prod) to prevent drift
Managing versioned schema history and linking to associated datasets
Enforcing metadata completeness as a gate in deployment pipelines
Integrating metadata with access control systems for attribute-based policies

Module 6: Security, Access Control, and Compliance

Implementing column- and row-level security in query engines like Presto or Spark SQL
Managing secrets and credentials using centralized vaults with rotation policies
Enforcing encryption at rest and in transit across all data layers
Designing audit trails for data access and modification events with retention policies
Mapping data processing activities to GDPR, CCPA, or HIPAA compliance requirements
Implementing data masking and tokenization for non-production environments
Configuring role-based access control (RBAC) aligned with organizational structure
Conducting periodic access reviews and certification for data entitlements

Module 7: Scalable Data Serving and Query Optimization

Selecting serving layers (OLAP, data warehouses, lakehouses) based on query patterns and latency
Designing materialized views and aggregates to accelerate common analytical queries
Optimizing query performance through indexing, statistics collection, and predicate pushdown
Managing concurrency and resource isolation in shared query engines
Implementing result caching strategies at application and engine levels
Partition pruning and filter optimization in distributed query planners
Right-sizing cluster resources for interactive versus batch query workloads
Monitoring query performance trends and identifying resource-intensive patterns

Module 8: Governance, Stewardship, and Lifecycle Management

Defining data ownership and stewardship roles across business units and domains
Establishing data classification policies based on sensitivity and regulatory impact
Implementing data retention and deletion workflows with legal hold capabilities
Creating data change management processes for schema and pipeline modifications
Managing cross-border data transfer restrictions in global deployments
Documenting data lineage and business definitions in a central glossary
Enforcing data governance policies through automated pipeline checks
Conducting regular data inventory and risk assessment audits

Module 9: Performance Monitoring and Cost Optimization

Instrumenting pipelines with custom metrics for data volume, latency, and error rates
Correlating processing costs with business value to prioritize optimization efforts
Right-sizing compute clusters based on historical utilization and forecasting
Identifying and eliminating orphaned or unused datasets and pipelines
Implementing auto-scaling policies for cloud-based processing frameworks
Allocating costs by team, project, or business unit using tagging and labeling
Optimizing file sizes and compression to reduce storage and I/O costs
Conducting regular cost reviews with engineering and finance stakeholders