Description

This curriculum spans the technical and operational rigor of a multi-workshop program with the depth of an internal capability build for enterprise data platform teams, covering infrastructure, pipeline reliability, compliance, and production ML operations at scale.

Module 1: Architecting Scalable Data Infrastructure

Selecting between on-premise Hadoop clusters and cloud-based data lakes based on data gravity, compliance needs, and long-term TCO.
Designing zone separation in distributed storage (e.g., hot, cold, archive tiers) to balance access latency and cost.
Implementing multi-region replication strategies for disaster recovery while managing cross-geo bandwidth costs.
Choosing file formats (Parquet, ORC, Avro) based on query patterns, compression efficiency, and schema evolution requirements.
Configuring HDFS block size and replication factor to optimize for large sequential reads versus small file overhead.
Integrating object storage gateways with legacy applications requiring POSIX-compliant interfaces.
Planning for metadata scalability by selecting external metastores (e.g., AWS Glue, Hive Metastore on RDS).
Evaluating containerization of data processing engines (Spark, Flink) using Kubernetes for resource isolation and portability.

Module 2: Data Pipeline Orchestration and Reliability

Defining SLA policies in Apache Airflow DAGs with retry strategies, timeout thresholds, and alert escalation paths.
Partitioning batch workflows by time, geography, or tenant to enable partial reprocessing and fault isolation.
Implementing idempotent data ingestion steps to prevent duplication during pipeline retries.
Managing pipeline dependencies across teams using versioned data contracts and schema registry integration.
Securing pipeline credentials using vault-integrated secrets backends instead of environment variables.
Designing backpressure handling in streaming pipelines to prevent consumer lag under load spikes.
Validating data completeness at pipeline boundaries using row count, hash checksums, or watermark alignment.
Migrating legacy cron-based ETL jobs to orchestrated workflows with dependency tracking and audit trails.

Module 3: Real-Time Data Processing Systems

Tuning Kafka consumer group configurations (fetch size, session timeout) for low-latency processing without rebalance storms.
Choosing between Kafka Streams and ksqlDB based on stateful processing complexity and operational overhead.
Managing state stores in Flink applications with checkpoint intervals and incremental snapshots.
Partitioning Kafka topics to align with parallelism settings in downstream consumers for optimal throughput.
Implementing event-time processing with watermarks to handle late-arriving data in time-windowed aggregations.
Configuring retention policies for Kafka topics based on downstream SLA and storage cost constraints.
Monitoring end-to-end latency in streaming pipelines using embedded metrics and synthetic probes.
Isolating high-priority streams using Kafka multi-cluster or tenant-dedicated clusters to prevent resource starvation.

Module 4: Data Governance and Compliance

Implementing column-level masking policies in query engines (e.g., Presto, Spark SQL) for PII fields.
Enforcing data retention schedules through automated tagging and lifecycle policies in object storage.
Integrating data lineage tools (e.g., DataHub, Atlas) with orchestration systems to track ETL provenance.
Classifying datasets using automated scanners and regex patterns to flag regulated content (e.g., PCI, HIPAA).
Managing access control via attribute-based policies synchronized with enterprise IAM systems.
Auditing data access patterns using query logs and exporting to SIEM tools for anomaly detection.
Handling data subject access requests (DSARs) with traceable workflows for locate, mask, or delete operations.
Documenting data ownership and stewardship roles in a centralized catalog for regulatory audits.

Module 5: Performance Monitoring and Observability

Instrumenting Spark applications with custom metrics (e.g., shuffle spill, task duration) for granular performance analysis.
Correlating infrastructure metrics (CPU, disk I/O) with query execution plans to identify resource bottlenecks.
Setting dynamic alert thresholds using statistical baselines instead of static values to reduce noise.
Implementing distributed tracing across microservices and data pipelines using OpenTelemetry.
Configuring log sampling strategies to reduce volume while preserving debuggability for rare failures.
Building dashboard templates for SLA compliance tracking across data freshness, availability, and latency.
Using synthetic transactions to validate pipeline health when real data flow is intermittent.
Integrating observability data with incident management systems for automated ticket creation.

Module 6: Capacity Planning and Cost Optimization

Right-sizing cluster node types based on memory-to-core ratios required by workloads (e.g., shuffle-heavy Spark jobs).
Implementing auto-scaling policies for EMR or Dataproc clusters using predictive and reactive triggers.
Negotiating reserved instance commitments for stable workloads versus spot instances for fault-tolerant batch jobs.
Identifying underutilized data assets for archival or decommissioning using access frequency reports.
Enforcing cost allocation tags across cloud resources to enable chargeback reporting by team or project.
Optimizing query performance through partition pruning and clustering to reduce scanned data volume.
Managing cross-AZ data transfer costs by co-locating compute and storage in the same region.
Conducting quarterly cost reviews with data owners to align spending with business value.

Module 7: Security and Access Control in Distributed Systems

Enabling Kerberos authentication for on-premise Hadoop clusters with centralized key distribution.
Implementing TLS encryption for data in transit between nodes, clients, and external services.
Using Ranger or Sentry policies to enforce row- and column-level access in SQL-on-Hadoop engines.
Rotating long-lived service account keys using automated credential rotation pipelines.
Hardening cluster nodes with OS-level security baselines and intrusion detection agents.
Isolating sensitive workloads using dedicated virtual networks and firewall rules.
Validating encryption at rest for managed services (e.g., S3 SSE-KMS, Cloud Storage CMEK).
Conducting quarterly access reviews to remove stale permissions for departed personnel.

Module 8: Disaster Recovery and Business Continuity

Defining RPO and RTO for critical data pipelines and aligning replication and backup strategies accordingly.
Automating failover testing for data clusters using chaos engineering principles (e.g., node termination).
Storing immutable backups in write-once-read-many (WORM) storage to prevent ransomware corruption.
Replicating metastore databases across regions using log shipping or managed HA configurations.
Documenting manual intervention steps for recovery scenarios where automation fails.
Validating backup integrity through periodic restore drills on isolated environments.
Coordinating cross-team recovery runbooks with dependencies on upstream data providers.
Monitoring replication lag in geo-distributed databases to detect silent failures.

Module 9: Operationalizing Machine Learning Workflows

Versioning training datasets and model artifacts using dedicated repositories (e.g., DVC, MLflow).
Scheduling retraining pipelines based on data drift detection thresholds from statistical monitors.
Managing GPU resource allocation for training jobs in shared Kubernetes clusters.
Implementing shadow mode deployment to compare model outputs before full cutover.
Enforcing model validation gates (accuracy, bias checks) before promotion to production.
Monitoring inference latency and error rates in real-time serving endpoints.
Securing model APIs with authentication and rate limiting to prevent abuse.
Tracking model lineage from training data to deployment for audit and reproducibility.