Description

This curriculum spans the technical and operational complexity of a multi-workshop program focused on building and maintaining enterprise-grade data extraction systems, comparable to the scope of an internal capability build for large-scale data platforms.

Module 1: Architecting Scalable Data Ingestion Pipelines

Design partitioning strategies for high-volume streaming sources to balance throughput and storage costs in distributed file systems.
Select between batch and micro-batch ingestion based on source system capabilities and downstream latency requirements.
Implement backpressure mechanisms in Kafka consumers to prevent downstream system overload during traffic spikes.
Configure retry policies and dead-letter queues for failed records in ingestion workflows to ensure data durability.
Integrate schema validation at ingestion time using Schema Registry to enforce data contracts across teams.
Optimize file sizing and compression formats (e.g., Snappy vs Zstandard) for ingestion into data lakes to reduce I/O overhead.
Deploy ingestion pipelines across multiple availability zones to meet uptime SLAs for mission-critical data feeds.
Negotiate API rate limits with external vendors and implement exponential backoff in client-side polling logic.

Module 2: Source System Analysis and Interface Assessment

Reverse-engineer undocumented database change logs to identify primary change data capture (CDC) vectors in legacy ERP systems.
Evaluate whether to extract from transactional databases directly or staged ETL tables based on performance impact thresholds.
Assess API stability and versioning policies of third-party SaaS platforms before committing to long-term integrations.
Determine optimal polling intervals for APIs lacking webhooks while minimizing latency and request throttling.
Map source system data lineage to business ownership units for compliance with data stewardship policies.
Identify data masking requirements at the source level when PII appears in operational logs or audit trails.
Document schema drift patterns in source systems to anticipate future extraction failures.
Coordinate change windows with source system teams to avoid extraction conflicts during maintenance.

Module 3: Change Data Capture Implementation Patterns

Compare log-based CDC (e.g., Debezium) vs trigger-based approaches in terms of latency, source system load, and transaction integrity.
Configure transaction ordering guarantees in CDC pipelines to preserve referential integrity during replay scenarios.
Handle DDL changes in source databases by implementing schema migration workflows in downstream targets.
Design idempotent CDC consumers to prevent data duplication during consumer rebalancing in Kafka.
Encrypt CDC streams in transit when replicating across network boundaries or cloud providers.
Monitor lag between source transaction timestamp and CDC event publication to detect replication bottlenecks.
Implement tombstone message handling to support row deletions in event-driven architectures.
Validate CDC consistency by reconciling row counts and checksums between source and target systems daily.

Module 4: Handling Semi-Structured and Unstructured Data

Normalize nested JSON payloads from application logs into flat relational formats using Spark SQL struct operations.
Define parsing rules for inconsistent timestamp formats in log files across different application versions.
Extract metadata from binary files (e.g., PDFs, images) using OCR and document parsing libraries at scale.
Apply schema-on-read patterns in data lake zones while maintaining discoverability through metadata catalogs.
Partition Parquet files by inferred ingestion date when source timestamps are unreliable or missing.
Handle schema evolution in Avro files by configuring backward-compatible reader-writer schemas.
Implement content validation for XML feeds using XSD or custom rule engines to reject malformed payloads.
Index extracted text from unstructured sources into Elasticsearch for cross-system searchability.

Module 5: Data Quality Monitoring and Validation

Define and compute null rate, uniqueness, and value distribution metrics for critical fields at ingestion time.
Configure alerting thresholds for data freshness based on expected arrival windows from upstream systems.
Implement referential integrity checks between related datasets when foreign keys are not enforced at source.
Use statistical profiling to detect silent data corruption in flat file transfers (e.g., delimiter shifts).
Log validation rule violations to a separate monitoring data store without blocking pipeline execution.
Version data quality rules to track changes in business definitions over time.
Integrate data observability tools with incident management systems for automated ticket creation on rule breaches.
Conduct root cause analysis on recurring validation failures by tracing back to source system changes.

Module 6: Security, Compliance, and Data Governance

Implement column-level encryption for sensitive fields during extraction when end-to-end encryption is not feasible.
Enforce attribute-based access control (ABAC) on extracted datasets using policy engines like Open Policy Agent.
Redact PII from log streams in real time using named entity recognition models before storage.
Generate audit logs for all data access and extraction activities to support regulatory inquiries.
Classify data sensitivity levels at ingestion using automated tagging based on content and source.
Apply data retention policies during extraction to exclude obsolete records from long-term storage.
Document data provenance for GDPR right-to-erasure compliance, including all derived datasets.
Coordinate with legal teams to assess cross-border data transfer implications for cloud-hosted pipelines.

Module 7: Performance Optimization and Cost Management

Right-size cluster resources for Spark ingestion jobs based on historical memory and CPU utilization patterns.
Implement data compaction routines to merge small files in data lakes and improve query performance.
Use predicate pushdown and column pruning in ingestion queries to reduce source system load.
Compare costs of on-demand vs reserved instances for long-running data transfer services.
Optimize serialization formats (e.g., Protobuf vs JSON) for network-constrained environments.
Cache frequently accessed reference data in Redis to reduce repeated extraction from source APIs.
Implement data sampling strategies for testing pipelines without incurring full production costs.
Monitor egress charges from cloud providers and route inter-region transfers through private links.

Module 8: Fault Tolerance and Operational Resilience

Design state checkpointing intervals in streaming jobs to balance recovery time and storage overhead.
Implement circuit breakers in API clients to prevent cascading failures during source outages.
Configure automated failover between primary and secondary data centers for critical ingestion jobs.
Use distributed locking mechanisms to prevent duplicate processing in multi-instance extractors.
Archive raw ingestion payloads for 30 days to support reprocessing after pipeline failures.
Standardize error codes across extraction components to enable consistent retry logic.
Simulate network partitions during testing to validate message durability in message queues.
Conduct chaos engineering exercises on staging environments to assess pipeline resilience.

Module 9: Integration with Downstream Analytics and ML Systems

Align data freshness SLAs with downstream reporting requirements for executive dashboards.
Expose extracted datasets via governed data APIs for self-service access by analytics teams.
Transform raw extracted data into feature store-ready formats for machine learning pipelines.
Synchronize metadata between data catalog tools and BI platforms to ensure consistent definitions.
Implement change propagation workflows to notify downstream consumers of schema updates.
Support point-in-time correctness in temporal tables for accurate historical analysis.
Generate synthetic keys for records lacking stable identifiers to enable cross-system joins.
Coordinate data backfill procedures with data science teams during model retraining cycles.