Description

This curriculum spans the technical and operational practices found in multi-workshop data reliability programs, covering the design, enforcement, and governance of data quality across distributed data pipelines, streaming systems, and cross-cloud environments.

Module 1: Defining Data Quality in Distributed Systems

Selecting appropriate data quality dimensions (accuracy, completeness, consistency, timeliness) based on use case requirements in streaming versus batch environments.
Mapping data quality expectations to SLAs for downstream consumers in a data mesh architecture.
Designing schema constraints in Avro or Protobuf to enforce structural quality at ingestion points.
Implementing data contracts between data producers and consumers to formalize quality expectations.
Choosing between strict schema validation and schema evolution strategies in Kafka topics.
Configuring data ingestion pipelines to reject or quarantine records that fail mandatory quality checks.
Documenting lineage of data quality rules across pipeline stages for auditability.
Aligning data quality KPIs with business outcomes in cross-functional stakeholder reviews.

Module 2: Data Profiling at Scale

Sampling strategies for profiling petabyte-scale datasets without full scans in Spark environments.
Deploying distributed profiling jobs using PySpark or Databricks to compute null rates, value distributions, and uniqueness.
Setting thresholds for acceptable skew in partitioning keys to avoid performance degradation.
Automating profiling execution on new data arrivals using Airflow or Prefect.
Storing profiling metadata in a metadata warehouse for trend analysis over time.
Identifying anomalies in data distributions through statistical baselining and deviation detection.
Integrating profiling results with data catalog tools like Apache Atlas or DataHub.
Handling high-cardinality fields during profiling to prevent memory overruns.

Module 3: Schema Management and Evolution

Implementing schema registry (e.g., Confluent Schema Registry) for version control of Avro schemas in Kafka pipelines.
Enforcing backward compatibility policies when evolving schemas in streaming systems.
Handling schema drift in semi-structured data (JSON, XML) during ingestion into data lakes.
Designing schema migration strategies for Parquet files in cloud storage with partitioned layouts.
Validating schema conformance in real-time using Kafka Connect transforms.
Resolving conflicts between schema versions in multi-team data mesh environments.
Automating schema documentation updates upon schema version changes.
Monitoring schema usage patterns to deprecate unused or redundant fields.

Module 4: Data Validation Frameworks and Rule Execution

Integrating Great Expectations or AWS Deequ into Spark pipelines for declarative validation.
Configuring validation rules to run at different pipeline stages (ingest, transform, publish).
Managing rule thresholds across environments (dev, staging, prod) with configuration files.
Handling rule failures: logging, alerting, or pipeline termination based on severity levels.
Parallelizing validation checks across large datasets using partition-aware execution.
Storing validation results in a time-series database for historical analysis.
Developing custom validation rules for domain-specific data quality logic.
Orchestrating validation workflows with metadata-driven DAGs in Airflow.

Module 5: Handling Data Quality in Streaming Pipelines

Configuring watermarking in Structured Streaming to manage late-arriving data with quality implications.
Implementing deduplication logic using event keys in Kafka Streams or Flink.
Designing stateful processing to track data quality metrics over time windows.
Buffering and reprocessing low-quality records in dead-letter queues for remediation.
Enforcing referential integrity across streaming sources with asynchronous lookups.
Monitoring data drift in real-time feature distributions for ML pipelines.
Applying probabilistic data quality scoring to records with incomplete context.
Scaling state stores in Flink or Kafka Streams to handle high-volume quality tracking.

Module 6: Metadata and Lineage for Quality Tracing

Instrumenting pipeline code to emit lineage events to OpenLineage or custom metadata stores.
Linking data quality rule violations to specific upstream sources using lineage graphs.
Storing schema, profiling, and validation metadata in a centralized data catalog.
Automating metadata extraction from ETL job configurations and logs.
Implementing metadata retention policies aligned with data governance requirements.
Querying lineage paths to identify root causes of recurring quality issues.
Exposing metadata APIs for integration with observability dashboards.
Enriching lineage records with data quality scores at each transformation node.

Module 7: Data Quality Monitoring and Alerting

Designing time-based and event-based triggers for data quality alerts in PagerDuty or Opsgenie.
Setting dynamic thresholds for anomaly detection using rolling statistical baselines.
Aggregating quality metrics across multiple pipelines into a unified observability dashboard.
Routing alerts to appropriate teams based on data domain ownership in a data mesh.
Reducing alert fatigue by suppressing duplicates and grouping related incidents.
Integrating with incident management systems to track resolution of data quality issues.
Logging false positives and tuning rules to improve signal-to-noise ratio.
Conducting post-mortems on major data quality incidents to update monitoring logic.

Module 8: Governance and Operational Policies

Establishing data quality ownership roles (data stewards, domain owners) in decentralized architectures.
Defining escalation paths for unresolved data quality issues impacting production systems.
Implementing access controls on quality rule configurations to prevent unauthorized changes.
Conducting periodic audits of data quality rule coverage across critical data assets.
Managing technical debt in legacy pipelines with incremental quality improvements.
Enforcing data quality gates in CI/CD pipelines for data transformation code.
Documenting data quality exceptions and business-approved tolerances in runbooks.
Aligning data quality practices with regulatory requirements (e.g., GDPR, BCBS 239).

Module 9: Cross-Cloud and Hybrid Environment Considerations

Synchronizing data quality rule sets across AWS, Azure, and GCP data platforms.
Handling network latency and data transfer costs when validating cross-region datasets.
Ensuring consistent timestamp handling and time zone resolution in distributed systems.
Managing authentication and secret propagation for quality tools across cloud accounts.
Replicating metadata stores with conflict resolution in multi-cloud deployments.
Validating data consistency after cross-cloud ETL or data migration jobs.
Designing fallback mechanisms for quality monitoring when cloud services are degraded.
Standardizing data quality metrics and reporting formats for enterprise-wide visibility.