Description

This curriculum spans the technical and operational complexity of a multi-workshop program focused on building and maintaining enterprise-grade data platforms, covering the same depth of implementation detail found in hands-on advisory engagements for large-scale, hybrid, and multi-cloud data architectures.

Module 1: Architecting Scalable Data Ingestion Pipelines

Selecting between batch and streaming ingestion based on SLA requirements and downstream processing needs.
Designing idempotent ingestion workflows to handle duplicate messages in distributed systems.
Implementing schema validation at ingestion points to enforce data quality before entry into storage layers.
Choosing appropriate serialization formats (e.g., Avro vs. Parquet vs. JSON) based on compression, schema evolution, and query performance.
Configuring backpressure mechanisms in Kafka consumers to prevent system overload during traffic spikes.
Integrating secret management (e.g., HashiCorp Vault) into ingestion services to securely handle API credentials and database access.
Deploying ingestion pipelines across hybrid cloud environments with consistent monitoring and logging.
Implementing dead-letter queues to isolate and analyze malformed records without disrupting pipeline flow.

Module 2: Distributed Storage Systems and Data Lakehouse Design

Partitioning large datasets by time and tenant to optimize query performance and access control.
Choosing between object stores (S3, ADLS, GCS) and distributed file systems (HDFS) based on cost, durability, and compute locality.
Implementing data layout optimizations such as Z-Ordering or bucketing to reduce scan overhead in analytical queries.
Managing metadata consistency in data lakes using centralized catalog services (e.g., AWS Glue, Unity Catalog).
Enabling ACID transactions on data lakes using Delta Lake or Apache Iceberg in multi-writer environments.
Designing lifecycle policies to transition cold data to lower-cost storage tiers automatically.
Implementing soft deletes and versioning to support auditability and rollback capabilities.
Securing data at rest using granular access policies and encryption key management (KMS).

Module 3: Real-Time Stream Processing with Flink and Kafka Streams

Defining watermark strategies to balance latency and completeness in event-time processing.
Choosing between keyed and non-keyed operations to manage state size and parallelism in Flink jobs.
Configuring checkpointing intervals and storage backends to ensure fault tolerance without degrading throughput.
Implementing exactly-once semantics using two-phase commit protocols with external sinks.
Monitoring and tuning state backend performance (RocksDB vs. Heap) under high load.
Scaling stream processors dynamically based on lag metrics from consumer groups.
Handling late-arriving events with allowed lateness and side outputs for anomaly detection.
Integrating stream processing jobs with CI/CD pipelines for zero-downtime deployments.

Module 4: Advanced Data Modeling for Analytics and ML

Designing slowly changing dimensions (SCD Type 2) in dimensional models to track historical changes.
Denormalizing data selectively to optimize for query patterns in columnar storage formats.
Implementing data vault modeling for enterprise-scale data warehouses requiring auditability and agility.
Creating feature stores with versioned datasets for consistent training and inference.
Managing surrogate key generation in distributed ETL environments to avoid collisions.
Validating data model assumptions against actual query workloads using query plan analysis.
Documenting lineage and business definitions in a discoverable metadata layer.
Optimizing aggregation grain to balance storage cost and query flexibility.

Module 5: AI-Driven Data Quality and Anomaly Detection

Deploying statistical profiling pipelines to detect schema drift and value distribution shifts.
Training baseline models on historical data to flag outliers in real-time data streams.
Configuring adaptive thresholds for data quality rules based on seasonal patterns.
Integrating automated data validation into orchestration tools (e.g., Airflow, Dagster).
Using clustering algorithms to identify unexpected data patterns in high-cardinality fields.
Implementing feedback loops to retrain anomaly detection models using operator-confirmed incidents.
Managing false positive rates by adjusting sensitivity based on business impact.
Correlating data anomalies with infrastructure metrics to isolate root causes.

Module 6: Governance, Compliance, and Data Lineage

Enforcing data classification policies using automated tagging based on content and context.
Implementing row- and column-level security in query engines (e.g., Presto, Snowflake) for regulated data.
Generating end-to-end lineage from source systems to dashboards using open metadata standards.
Responding to data subject access requests (DSARs) with precise data location and usage maps.
Configuring audit logging for all data access and transformation operations in cloud environments.
Integrating data governance tools with CI/CD to validate policy compliance before deployment.
Managing retention policies across distributed systems to meet legal hold requirements.
Conducting data impact analysis before retiring legacy data sources.

Module 7: Performance Optimization in Distributed Query Engines

Configuring resource queues in distributed SQL engines to prevent query starvation.
Choosing appropriate file sizes (128MB–1GB) to balance I/O efficiency and parallelism.
Implementing predicate pushdown and column pruning in custom connectors.
Tuning shuffle partitions in Spark based on cluster size and data volume.
Using materialized views or pre-aggregates to accelerate recurring analytical queries.
Diagnosing data skew in joins and redistributing keys to improve performance.
Monitoring spill-to-disk events in executors to adjust memory allocation.
Enabling cost-based optimization in query planners using up-to-date table statistics.

Module 8: MLOps Integration with Big Data Platforms

Synchronizing feature store updates with model training schedules to ensure data consistency.
Versioning training datasets using immutable object store references for reproducibility.
Deploying model inference at scale using serverless functions or Kubernetes operators.
Monitoring prediction drift by comparing live inference distributions to training baselines.
Implementing shadow mode deployments to validate new models against production traffic.
Logging inference requests and responses for debugging and regulatory compliance.
Automating retraining pipelines triggered by data drift or performance degradation alerts.
Securing model artifacts and weights using signed URLs and access policies.

Module 9: Multi-Cloud and Hybrid Data Orchestration

Designing cross-cloud data replication with conflict resolution for multi-region availability.
Orchestrating workflows across AWS, GCP, and on-prem systems using unified scheduling tools.
Managing identity federation across cloud providers for seamless data access.
Optimizing data transfer costs using compression, deduplication, and transfer windows.
Implementing disaster recovery procedures with automated failover for critical pipelines.
Standardizing monitoring and alerting across heterogeneous environments using open telemetry.
Negotiating egress cost implications with stakeholders before enabling cross-cloud analytics.
Enforcing consistent tagging and naming conventions to enable cost allocation and governance.