Description

This curriculum spans the technical and operational complexity of a multi-workshop program focused on enterprise data platform modernization, covering the design, governance, and optimization of large-scale data systems across hybrid environments.

Module 1: Strategic Data Platform Selection and Integration

Evaluate on-premises Hadoop clusters versus cloud-based data lakes based on data gravity, egress costs, and compliance requirements.
Design cross-platform data ingestion pipelines that reconcile schema differences between Kafka, AWS Kinesis, and Azure Event Hubs.
Implement metadata synchronization between Hive Metastore and cloud-native catalog services like AWS Glue.
Decide on containerization of data processing workloads using Kubernetes versus managed services like Dataproc or EMR.
Assess vendor lock-in risks when adopting proprietary data processing engines such as BigQuery UDFs or Snowflake stored procedures.
Integrate legacy ETL systems with modern orchestration tools like Apache Airflow without disrupting SLAs.
Negotiate SLAs with cloud providers for guaranteed I/O throughput on distributed storage layers.
Standardize data serialization formats (Avro, Parquet, ORC) across ingestion and serving layers for compatibility.

Module 2: Scalable Data Ingestion Architecture

Configure Kafka topics with optimal partition counts based on peak throughput and consumer parallelism requirements.
Implement exactly-once semantics in Spark Streaming jobs using checkpointing and idempotent sinks.
Design change data capture (CDC) pipelines from Oracle and SQL Server using Debezium with secure credential management.
Balance latency and cost in batch versus micro-batch ingestion for time-sensitive analytics workloads.
Apply backpressure handling mechanisms in streaming pipelines to prevent consumer lag during traffic spikes.
Encrypt sensitive data in transit and at rest during ingestion without degrading pipeline throughput.
Monitor ingestion pipeline health using custom metrics in Prometheus and alert on data staleness.
Manage schema evolution in Avro-based streams using Confluent Schema Registry with compatibility checks.

Module 3: Data Governance and Metadata Management

Deploy automated PII detection using regex and NLP models across raw data lakes to enforce masking policies.
Implement column-level lineage tracking from source systems to BI dashboards using tools like DataHub or Atlas.
Define and enforce data retention policies in S3 and Delta Lake based on legal and operational requirements.
Integrate data catalog with IAM systems to enforce attribute-based access control (ABAC) on datasets.
Standardize business glossary terms across departments and map them to technical schema elements.
Conduct quarterly data quality audits using Great Expectations or Soda Core with documented remediation workflows.
Establish stewardship roles and approval workflows for dataset publication and schema changes.
Implement data versioning in Delta Lake for auditability and rollback capability in production pipelines.

Module 4: Performance Optimization of Distributed Workloads

Tune Spark executor memory and core allocation based on shuffle spill metrics and GC logs.
Optimize Parquet file sizes and row group alignment to reduce I/O during analytical queries.
Implement predicate pushdown and column pruning in Presto and Trino queries for faster scans.
Use bucketing and partitioning strategies in Hive and Delta Lake to minimize data scanned.
Configure caching policies in Alluxio or Spark to accelerate iterative machine learning workloads.
Diagnose network bottlenecks in shuffle-heavy jobs using YARN and Ganglia metrics.
Right-size cluster resources using autoscaling policies based on historical job profiles.
Precompute aggregations in materialized views for high-frequency reporting queries.

Module 5: Real-Time Analytics and Serving Systems

Select between Druid, Pinot, and ClickHouse based on query patterns, ingestion rate, and hardware constraints.
Implement low-latency joins between streaming data and dimension tables using Flink broadcast state.
Design caching layers with Redis or Memcached to serve real-time KPIs to dashboards.
Ensure consistency between OLAP and OLTP systems using dual writes with compensating transactions.
Scale stateful stream processing applications across Flink TaskManagers with checkpoint alignment.
Validate end-to-end latency SLAs for real-time dashboards under peak load conditions.
Implement schema-on-read patterns in real-time pipelines to support flexible analytics.
Handle out-of-order events in time-windowed aggregations using watermarks and late data side outputs.

Module 6: Data Quality and Observability Engineering

Embed data validation checks in ingestion pipelines using Deequ or Great Expectations.
Configure synthetic monitors to detect data pipeline failures before downstream impact.
Implement data drift detection for ML features using statistical tests on distribution shifts.
Correlate pipeline failures with infrastructure metrics (CPU, disk, network) for root cause analysis.
Design alerting thresholds for data freshness based on business cycle and seasonality.
Track data lineage for failed records to enable targeted reprocessing.
Standardize error logging formats across batch and streaming components for centralized analysis.
Conduct blameless postmortems for major data incidents with documented action items.

Module 7: Cost Management and Resource Governance

Allocate cloud data platform costs to business units using tagging and chargeback models.
Implement query throttling and concurrency limits in Presto clusters to prevent resource exhaustion.
Optimize storage costs by tiering cold data from S3 Standard to Glacier with lifecycle policies.
Enforce compute budgets using quotas in Databricks or Snowflake virtual warehouses.
Identify and decommission unused datasets and pipelines through usage analytics.
Negotiate reserved instance pricing for predictable workloads on EMR or Dataproc.
Monitor and control data duplication across staging, processing, and archival layers.
Implement data compaction jobs to reduce small file overhead in HDFS and S3.

Module 8: Machine Learning Pipeline Integration

Version large training datasets using DVC or Delta Lake for reproducible model training.
Orchestrate feature engineering pipelines with Airflow and validate outputs before model training.
Deploy feature stores like Feast to serve consistent features in training and serving.
Monitor prediction drift in production models using statistical process control.
Manage model registry lifecycle with staging, A/B testing, and rollback procedures.
Secure access to model endpoints using OAuth and rate limiting.
Integrate model monitoring with observability platforms to correlate performance with data quality.
Optimize batch scoring jobs for large datasets using distributed inference on Spark.

Module 9: Cross-Functional Collaboration and Change Management

Facilitate data contract agreements between data producers and consumers using schema registries.
Coordinate schema change rollouts with application teams to avoid breaking dependencies.
Document data model decisions in RFCs and maintain changelogs for audit purposes.
Conduct data readiness reviews before major product launches involving analytics.
Establish SLAs for data pipeline uptime and communicate breach protocols to stakeholders.
Train business analysts on self-service data tools while enforcing governance guardrails.
Manage technical debt in data pipelines through scheduled refactoring sprints.
Align data strategy with enterprise architecture standards and security policies.