Description

This curriculum spans the technical and operational breadth of enterprise data platform management, comparable in scope to a multi-phase infrastructure modernization program involving storage architecture, distributed systems deployment, governance rollout, and cost-optimized operations across hybrid environments.

Module 1: Architecting Scalable Data Storage for Big Data Environments

Selecting between distributed file systems (e.g., HDFS) and object storage (e.g., S3) based on data access patterns, cost, and integration requirements
Designing data partitioning strategies (e.g., range, hash, list) to optimize query performance and manage data skew
Implementing tiered storage policies to move cold data to lower-cost storage while maintaining query accessibility
Choosing appropriate data serialization formats (e.g., Parquet, Avro, ORC) based on schema evolution needs and query efficiency
Configuring replication factors in distributed systems balancing fault tolerance against storage overhead
Integrating metadata management tools (e.g., Apache Atlas) with storage layers to enable data discovery and lineage tracking
Designing immutable data lake architectures to support auditability and reproducibility of analytics workflows
Implementing data compaction strategies to reduce small file overhead in distributed file systems

Module 2: Distributed Database Selection and Deployment

Evaluating consistency models (strong, eventual, causal) in NoSQL databases (e.g., Cassandra, DynamoDB) against application requirements
Deploying sharded relational databases using middleware (e.g., Vitess) and managing cross-shard query complexity
Configuring quorum-based read/write operations in distributed databases to balance availability and data consistency
Selecting between wide-column, document, and key-value stores based on access patterns and data relationships
Planning for multi-region deployments including handling latency, data sovereignty, and failover mechanisms
Implementing connection pooling and load balancing strategies for high-throughput database access
Managing schema migration strategies in schema-on-read versus schema-on-write environments
Integrating distributed databases with stream processing systems for real-time data ingestion

Module 3: Data Ingestion and Pipeline Orchestration

Designing idempotent ingestion pipelines to handle duplicate messages from message queues (e.g., Kafka)
Implementing change data capture (CDC) using log-based tools (e.g., Debezium) and managing transaction log overhead
Selecting batch versus micro-batch ingestion based on latency requirements and system resource constraints
Configuring backpressure mechanisms in streaming pipelines to prevent consumer overload
Validating data schema at ingestion using schema registries and rejecting non-conforming records
Orchestrating complex workflows using tools like Apache Airflow, including managing dependencies and retry policies
Monitoring pipeline lag and throughput to detect performance degradation or bottlenecks
Securing data in transit between ingestion sources and storage layers using TLS and authentication

Module 4: Query Optimization and Performance Tuning

Creating and maintaining partitioned and clustered indexes in distributed query engines (e.g., Spark SQL, Presto)
Adjusting shuffle partitions in Spark to balance parallelism and memory usage
Implementing predicate pushdown and column pruning in query execution plans
Using materialized views or pre-aggregated tables to accelerate common analytical queries
Diagnosing data skew in joins and redistributing data using salting techniques
Configuring query execution memory and spillover settings to prevent out-of-memory failures
Using query explain plans to identify bottlenecks and optimize execution strategies
Managing resource queues and query prioritization in multi-tenant query engines

Module 5: Data Governance and Metadata Management

Implementing data classification policies to identify sensitive fields (PII, PHI) across data lakes
Establishing ownership and stewardship roles for datasets in collaborative environments
Integrating metadata catalogs with data quality tools to track freshness, accuracy, and completeness
Enforcing data retention and archival policies based on regulatory requirements (e.g., GDPR, HIPAA)
Automating metadata extraction during ingestion to maintain up-to-date data lineage
Managing schema versioning and backward compatibility in evolving data pipelines
Implementing data access request workflows with audit logging for compliance reporting
Standardizing data naming conventions and business definitions across teams

Module 6: Security and Access Control in Distributed Systems

Configuring fine-grained access control (row-level, column-level) in data warehouses (e.g., Snowflake, Redshift)
Integrating Kerberos or LDAP for centralized authentication in Hadoop ecosystems
Implementing end-to-end encryption for data at rest using KMS-managed keys
Managing service account credentials and rotating secrets in automated pipelines
Enabling audit logging for data access and query execution across storage and compute layers
Applying attribute-based access control (ABAC) policies based on user roles and data sensitivity
Securing inter-service communication in microservices architectures using mTLS
Conducting regular access reviews to remove stale permissions and enforce least privilege

Module 7: High Availability, Disaster Recovery, and Backup Strategies

Designing multi-AZ or multi-region database deployments with automated failover capabilities
Implementing point-in-time recovery (PITR) for transactional databases and testing recovery procedures
Scheduling and validating incremental and full backups for distributed databases
Replicating data lakes across regions using asynchronous copy jobs with consistency checks
Defining RPO and RTO targets for critical data services and aligning infrastructure accordingly
Using active-passive versus active-active configurations based on cost and availability requirements
Testing disaster recovery plans through controlled failover exercises
Managing backup retention policies and lifecycle transitions to balance compliance and cost

Module 8: Monitoring, Alerting, and Capacity Planning

Instrumenting distributed systems with metrics collection (e.g., Prometheus) for query latency, I/O, and CPU
Setting up anomaly-based alerts for sudden changes in data volume or ingestion rate
Tracking storage growth trends to forecast capacity needs and plan scaling events
Correlating logs from multiple components (ingestion, storage, compute) for root cause analysis
Monitoring query queue times and resource utilization to identify contention points
Establishing baselines for normal system behavior to reduce false positive alerts
Using distributed tracing to analyze end-to-end performance of data workflows
Automating scaling policies for cloud-based data services based on utilization thresholds

Module 9: Cost Management and Resource Optimization

Right-sizing cluster configurations for batch processing jobs to minimize idle resources
Implementing auto-scaling and auto-suspension for cloud data warehouses during off-peak hours
Analyzing query costs by user, team, or workload to enforce budget accountability
Optimizing data compression settings to reduce storage and I/O costs
Using spot instances or preemptible VMs for fault-tolerant, non-critical workloads
Consolidating small queries into batch operations to reduce compute overhead
Monitoring egress costs and minimizing cross-region data transfers
Conducting regular cost audits to identify underutilized resources and orphaned datasets