Description

This curriculum spans the design and operational rigor of a multi-workshop technical enablement program for data engineering teams, covering the breadth of decisions and trade-offs involved in building and maintaining enterprise-scale data platforms.

Module 1: Architecting Scalable Data Ingestion Pipelines

Designing idempotent ingestion workflows to handle duplicate messages from high-throughput sources like Kafka or Kinesis
Selecting between batch and streaming ingestion based on SLA requirements and source system capabilities
Implementing backpressure mechanisms in Spark Streaming or Flink to prevent consumer overload during traffic spikes
Configuring secure, authenticated data transfer from on-premises databases to cloud data lakes using encrypted tunnels
Choosing appropriate serialization formats (Avro vs. JSON vs. Protobuf) based on schema evolution and parsing performance
Partitioning strategies for ingested data to optimize downstream query performance in distributed storage systems
Monitoring data freshness and latency across ingestion stages using custom metrics and alerting thresholds
Handling schema drift in semi-structured data by implementing schema validation and auto-registration in a schema registry

Module 2: Distributed Storage Design and Optimization

Selecting file formats (Parquet, ORC, Delta Lake) based on query patterns, ACID requirements, and compute engine compatibility
Implementing partitioning and bucketing strategies in Hive-style tables to reduce scan overhead in petabyte-scale datasets
Designing lifecycle policies for tiered storage (hot, cold, archive) using S3 Intelligent Tiering or equivalent cloud services
Configuring replication and erasure coding in HDFS or object storage to balance durability and storage cost
Enabling compression algorithms (Snappy, Zstandard) based on CPU overhead and I/O reduction trade-offs
Implementing column-level encryption or field masking for sensitive data at rest in shared storage environments
Validating data integrity using checksums and manifest files after large-scale ETL operations
Managing metadata consistency in distributed file systems during concurrent write operations from multiple clusters

Module 3: Large-Scale Data Processing with Distributed Engines

Tuning Spark executor memory and core allocation to minimize garbage collection and maximize parallelism
Optimizing shuffle behavior by adjusting partition counts and enabling shuffle service in YARN or Kubernetes
Choosing between DataFrame API and RDD based on optimization needs and debugging complexity
Implementing broadcast joins for small dimension tables to reduce shuffle overhead in Spark SQL
Configuring speculative execution to mitigate straggler tasks in heterogeneous cluster environments
Debugging stage failures using Spark UI metrics, GC logs, and event log analysis
Managing dynamic resource allocation to scale cluster size based on workload demand
Integrating custom UDFs with type safety and performance considerations in PySpark or Scala

Module 4: Real-Time Stream Processing Architecture

Designing event-time processing with watermarks to handle late-arriving data in Flink or Spark Structured Streaming
Implementing exactly-once semantics using checkpointing and two-phase commit in sink operations
Choosing windowing strategies (tumbling, sliding, session) based on business SLAs and data arrival patterns
Scaling stateful stream applications by tuning key-by partitioning and managing state backend (RocksDB vs. heap)
Integrating stream processing jobs with external systems (databases, caches) using async I/O to avoid blocking
Monitoring processing lag and throughput to detect consumer degradation in real-time pipelines
Handling schema evolution in streaming data by integrating schema registry with deserialization logic
Isolating and testing state recovery behavior during controlled job restarts and failures

Module 5: Data Quality and Observability at Scale

Implementing schema conformance checks at ingestion using Deequ or Great Expectations in batch pipelines
Designing statistical profiling jobs to detect anomalies in data distributions over time
Setting up automated alerting for null rate thresholds, value range violations, or unexpected cardinality shifts
Integrating data lineage tracking with metadata stores to trace root causes of quality issues
Building reconciliation reports between source and target systems to validate ETL accuracy
Instrumenting pipelines with custom metrics exported to Prometheus or Datadog for operational visibility
Creating synthetic data generators to simulate edge cases in testing environments
Managing data quality rule lifecycle across development, staging, and production environments

Module 6: Security, Access Control, and Compliance

Implementing fine-grained access control in data lakes using Apache Ranger or AWS Lake Formation policies
Enforcing column- and row-level security in SQL engines like Presto or Spark with dynamic filtering
Integrating Kerberos or OAuth2 for secure authentication in multi-tenant cluster environments
Masking PII fields in query results using UDFs or view-layer transformations for non-privileged roles
Auditing data access patterns and query logs to meet regulatory requirements like GDPR or HIPAA
Rotating encryption keys and credentials using secret management tools (Hashicorp Vault, AWS Secrets Manager)
Validating end-to-end encryption in transit for data moving between services using mTLS
Conducting periodic access reviews and privilege revocation for stale service accounts

Module 7: Orchestration and Workflow Management

Designing DAGs in Airflow with appropriate task boundaries and retry strategies for idempotency
Managing cross-DAG dependencies using external task sensors or message-based triggers
Securing Airflow metadata database and web server with role-based access and network isolation
Parameterizing workflows to support multi-environment deployment (dev, staging, prod) without code duplication
Implementing SLA monitoring and failure notifications using custom callbacks and alert integrations
Scaling Airflow components (workers, schedulers) using Kubernetes operators for high availability
Version-controlling DAG code and managing deployment via CI/CD pipelines with rollback capability
Handling long-running tasks by delegating to external systems and polling for completion

Module 8: Performance Tuning and Cost Optimization

Right-sizing cluster configurations based on historical utilization metrics and workload patterns
Implementing spot instance usage with checkpointing to reduce compute costs in cloud environments
Optimizing query performance through predicate pushdown, column pruning, and indexing strategies
Consolidating small files in object storage using compaction jobs to improve read efficiency
Using materialized views or pre-aggregated tables to accelerate frequent analytical queries
Monitoring and eliminating resource contention in shared clusters using queue management in YARN
Conducting cost attribution by tagging jobs with project, team, or cost center identifiers
Implementing auto-scaling policies based on queue depth or CPU/memory utilization thresholds

Module 9: Data Governance and Metadata Management

Deploying a centralized metadata catalog (Apache Atlas, AWS Glue Data Catalog) for discoverability
Automating metadata extraction from ETL jobs and registering it with lineage context
Enforcing data ownership and stewardship assignments for critical datasets
Implementing data classification tags (PII, financial, internal) for policy enforcement
Integrating business glossary terms with technical metadata to bridge domain understanding
Managing dataset deprecation and archival workflows with stakeholder notifications
Validating metadata consistency across systems (warehouse, BI tools, data science platforms)
Supporting self-service discovery with full-text search, facet filtering, and usage statistics