Description

This curriculum spans the technical and operational rigor of a multi-workshop DevOps transformation program for big data platforms, covering the design, deployment, and governance challenges typical in large-scale data engineering teams operating under strict compliance and reliability requirements.

Module 1: Infrastructure Design for Scalable Data Platforms

Select between public cloud, hybrid, or on-premises deployment models based on data residency, compliance, and egress cost constraints.
Architect multi-region data replication strategies to meet RPO and RTO requirements while minimizing latency.
Implement immutable infrastructure patterns using infrastructure-as-code (Terraform, Pulumi) to ensure reproducible cluster environments.
Configure persistent storage backends with appropriate IOPS and throughput for high-volume ingestion workloads.
Design network topology with private subnets, VPC peering, and firewall rules to isolate data plane traffic.
Evaluate containerization vs. bare-metal deployment for stateful big data services like HDFS or Kafka.
Integrate secrets management (Hashicorp Vault, AWS Secrets Manager) into cluster provisioning workflows.
Size compute nodes based on data block size, replication factor, and concurrent processing demands.

Module 2: Continuous Integration and Deployment for Data Pipelines

Define pipeline versioning strategies that track schema, code, and configuration changes in tandem.
Implement automated testing for data quality checks (null rates, distribution bounds) in CI stages.
Enforce pull request gates that validate schema compatibility using tools like Apache Avro or Protobuf.
Orchestrate blue-green deployments for streaming pipelines to minimize downtime during upgrades.
Integrate static code analysis for PySpark and SQL scripts to detect performance anti-patterns.
Manage environment-specific configurations using parameter stores or config maps, avoiding hardcoded values.
Trigger pipeline redeployments based on schema registry changes or data contract updates.
Use artifact repositories (Nexus, JFrog) to store compiled data job binaries with traceable metadata.

Module 3: Data Pipeline Orchestration at Scale

Choose between Airflow, Prefect, or Dagster based on dynamic DAG generation and execution model requirements.
Implement task-level retries with exponential backoff for transient failures in external API integrations.
Partition workflows by business domain or data source to isolate failure blast radius.
Configure SLA monitoring and alerting on DAG completion times to detect performance degradation.
Secure inter-task communication using service accounts and short-lived credentials.
Optimize scheduler performance by reducing DAG parsing overhead through modular imports.
Manage cross-DAG dependencies using data-driven triggers or external task sensors.
Scale executor backends (Kubernetes, Celery) based on peak pipeline concurrency and resource demands.

Module 4: Monitoring, Logging, and Observability

Instrument data jobs with structured logging to enable parsing and correlation in centralized systems (ELK, Splunk).
Define custom metrics for data pipeline health (records processed, lag, error rate) using Prometheus exporters.
Correlate application logs with infrastructure metrics to diagnose performance bottlenecks.
Implement distributed tracing for end-to-end visibility across microservices and data stores.
Set up anomaly detection on data volume and latency metrics to identify upstream disruptions.
Configure log retention policies based on regulatory requirements and storage cost targets.
Alert on data drift using statistical baselines from historical distribution profiles.
Use synthetic transactions to validate pipeline functionality during maintenance windows.

Module 5: Security and Access Governance

Enforce attribute-based access control (ABAC) for data assets using Apache Ranger or AWS Lake Formation.
Implement row- and column-level filtering in query engines (Presto, Spark SQL) based on user roles.
Rotate encryption keys for data-at-rest using KMS with automated re-encryption workflows.
Conduct regular access certification reviews for high-privilege service accounts.
Mask sensitive data fields in non-production environments using deterministic tokenization.
Integrate with enterprise identity providers (Okta, Azure AD) for SSO and audit trail consistency.
Scan pipeline code for hardcoded credentials or secrets using Git hooks and CI tools.
Classify data assets by sensitivity level to drive encryption and retention policies.

Module 6: Data Quality and Pipeline Reliability

Embed data validation rules (schema conformance, referential integrity) at ingestion points.
Design dead-letter queues for malformed records with automated quarantine and notification.
Implement idempotent processing logic to handle duplicate message delivery in streaming pipelines.
Use watermarking in event-time processing to manage late-arriving data within bounded windows.
Track data lineage from source to consumption to support impact analysis and debugging.
Automate reconciliation jobs between source and target systems to detect data loss.
Define escalation paths for data quality incidents based on business criticality.
Version data contracts and enforce backward compatibility in downstream consumers.

Module 7: Performance Optimization and Cost Management

Tune Spark configurations (executor memory, parallelism) based on workload profiling and GC behavior.
Implement data compaction and file sizing strategies to reduce small file overhead in object storage.
Apply predicate pushdown and column pruning in query engines to minimize I/O.
Right-size cluster resources using autoscaling policies tied to queue depth or CPU utilization.
Convert row-based data formats to columnar (Parquet, ORC) to improve query performance.
Cache frequently accessed reference datasets in memory or distributed caches.
Monitor and optimize data shuffling patterns to avoid skew and network congestion.
Allocate compute resources using quotas and namespaces to prevent resource starvation.

Module 8: Disaster Recovery and Backup Strategies

Define backup frequency and retention for metastore databases (Hive, Glue) based on recovery point objectives.
Test failover procedures for distributed coordination services (ZooKeeper, etcd).
Replicate critical data assets across regions using incremental copy tools (DistCp, AWS DataSync).
Validate backup integrity through automated restore drills in isolated environments.
Document runbooks for cluster rebuild scenarios including dependency ordering.
Protect against accidental deletion using object locking and versioning in object storage.
Coordinate backup schedules to avoid contention with peak processing windows.
Store encryption keys and configuration backups in geographically separate locations.

Module 9: Change Management and Operational Runbooks

Standardize change approval workflows for production data environment modifications.
Maintain runbooks with step-by-step recovery procedures for common failure scenarios.
Implement canary releases for pipeline updates to validate behavior on partial data volumes.
Conduct blameless postmortems for data outages to update prevention controls.
Rotate on-call responsibilities with clear escalation paths and response time expectations.
Version control operational scripts and validate them against sandbox environments.
Document data schema evolution patterns and communicate breaking changes to consumers.
Enforce maintenance windows for infrastructure upgrades to minimize business impact.