Description

This curriculum spans the technical and operational rigor of a multi-workshop program for data platform teams, covering the design, governance, and optimization of large-scale data systems as typically addressed in enterprise advisory engagements.

Module 1: Data Infrastructure Design and Scalability Planning

Selecting between on-premise, hybrid, and cloud data architectures based on data sovereignty, latency, and cost-per-TB requirements.
Evaluating the trade-offs between batch and real-time ingestion pipelines when designing data lake foundations.
Implementing data partitioning strategies in distributed file systems to optimize query performance and reduce compute costs.
Choosing appropriate storage formats (Parquet, ORC, Avro) based on query patterns, compression needs, and schema evolution requirements.
Right-sizing cluster configurations for Hadoop or Spark workloads to balance fault tolerance and resource utilization.
Designing cross-region replication for disaster recovery without introducing data consistency issues.
Integrating metadata management tools (e.g., Apache Atlas) early in the stack to support lineage and compliance.
Establishing data lifecycle policies to automate tiering from hot to cold storage based on access frequency.

Module 2: Data Ingestion and Pipeline Orchestration

Configuring Kafka consumers with appropriate offset management strategies to prevent data loss during consumer group rebalancing.
Implementing idempotent processing in streaming pipelines to handle duplicate message delivery.
Selecting between Change Data Capture (CDC) tools (Debezium, AWS DMS) based on source database compatibility and latency SLAs.
Designing retry mechanisms with exponential backoff in Airflow DAGs to handle transient API failures.
Managing schema drift in incoming JSON data by integrating schema registries and validation layers.
Securing data in transit using mutual TLS for pipeline components across untrusted networks.
Monitoring end-to-end pipeline latency using watermark tracking in Flink or Spark Streaming.
Orchestrating cross-system dependencies (e.g., upstream API availability) before triggering ETL jobs.

Module 3: Data Quality and Observability

Implementing automated data validation rules (e.g., using Great Expectations) at ingestion to flag anomalies before processing.
Setting up statistical profiling jobs to detect silent data corruption in large-scale datasets.
Defining SLAs for data freshness and measuring compliance via pipeline monitoring dashboards.
Integrating data quality checks into CI/CD pipelines for analytics code to prevent deployment of broken logic.
Correlating data pipeline failures with infrastructure metrics (CPU, memory, network) to isolate root causes.
Establishing alert thresholds for null rates, value distribution skews, and record count deviations.
Documenting data quality rules in a centralized catalog accessible to analysts and engineers.
Handling false positives in data alerts by implementing dynamic baselines based on historical patterns.

Module 4: Performance Optimization of Query Engines

Tuning Spark executor memory and core allocation to avoid garbage collection bottlenecks in long-running jobs.
Implementing predicate pushdown and column pruning in Parquet readers to reduce I/O overhead.
Configuring caching strategies in Presto or Trino for frequently accessed dimension tables.
Choosing between broadcast and shuffle joins based on dataset size and cluster topology.
Optimizing Hive metastore performance by partitioning large tables and managing partition growth.
Reducing shuffle spill to disk by adjusting Spark’s shuffle partition count dynamically.
Using query execution plan analysis to identify inefficient operations like full table scans or data skew.
Implementing materialized views in data warehouses to pre-aggregate high-latency queries.

Module 5: Data Governance and Compliance Enforcement

Implementing row-level and column-level security in Snowflake or Databricks using dynamic masking policies.
Mapping personal data fields to GDPR or CCPA requirements using automated data classification tools.
Enforcing data retention policies through automated purge workflows with audit trails.
Integrating access certification workflows to ensure periodic review of data entitlements.
Generating data lineage reports for regulatory audits using tools like DataHub or Collibra.
Managing consent flags in customer records and propagating them through downstream analytics systems.
Implementing data minimization practices by restricting PII access to authorized roles only.
Handling cross-border data transfer compliance by routing queries to region-specific compute clusters.

Module 6: Cost Management and Resource Allocation

Implementing query cost estimation and budget alerts in cloud data warehouses (BigQuery, Redshift).
Right-sizing reserved instances versus spot instances for long-running batch processing jobs.
Enforcing compute quotas per team or project to prevent budget overruns in shared clusters.
Automating cluster shutdown for non-production environments during off-hours.
Using tagging strategies to allocate cloud storage and compute costs to business units.
Optimizing file sizes in data lakes to reduce the number of small files impacting query performance.
Monitoring and eliminating orphaned data assets (unused tables, stale partitions) to reduce storage costs.
Implementing data sampling strategies for development and testing to reduce compute usage.

Module 7: Real-Time Analytics and Stream Processing

Choosing between event time and processing time semantics in streaming applications based on accuracy requirements.
Designing stateful processing logic in Flink with checkpointing to ensure fault tolerance.
Managing late-arriving data using watermarks and allowed lateness in time-windowed aggregations.
Scaling Kafka consumer groups to match topic partition count for maximum parallelism.
Implementing exactly-once semantics using two-phase commit protocols in sink operations.
Reducing serialization overhead in streaming pipelines by using efficient formats like Protobuf.
Monitoring backpressure in streaming jobs to detect processing bottlenecks before data loss occurs.
Integrating real-time dashboards with low-latency data stores like Druid or Pinot.

Module 8: Machine Learning Integration with Data Pipelines

Versioning training datasets using DVC or MLflow to ensure reproducible model results.
Scheduling feature computation jobs to align with model retraining cycles.
Implementing feature stores (e.g., Feast) to ensure consistency between training and serving data.
Monitoring data drift in production model inputs using statistical tests on feature distributions.
Deploying shadow models alongside production systems to compare performance before cutover.
Securing access to model artifacts and inference logs in shared environments.
Optimizing batch scoring pipelines for large-scale inference with parallel execution.
Integrating model feedback loops to capture ground truth and retrain on new data.

Module 9: Cross-Functional Collaboration and Change Management

Establishing SLAs for data delivery between data engineering and analytics teams.
Documenting schema changes and deprecations with backward compatibility periods.
Coordinating data migration windows with business stakeholders to minimize operational impact.
Implementing data contract validation between producers and consumers using JSON Schema.
Conducting blameless postmortems for data incidents to improve system resilience.
Standardizing naming conventions and metadata tagging across teams to improve discoverability.
Facilitating data literacy workshops for non-technical stakeholders to reduce ad-hoc requests.
Managing technical debt in data pipelines through scheduled refactoring sprints.