Description
- Big Data Testing with Azure Features Basics to Advanced
- Scope: Validate correctness, quality, performance, and compliance across Azure big data pipelines and analytics.
- Source validation: Test ingestion from Azure SQL, Cosmos DB, Blob/ADLS Gen2, Event Hubs, and APIs for completeness and schema conformance.
- Ingestion testing: Verify batch and streaming extracts (Azure Data Factory, Event Hubs) for data loss, ordering, and watermarking.
- Schema and contract tests: Enforce source-to-target schemas, Avro/Parquet formats, and schema registry contracts to prevent downstream breakage.
- ETL / ELT pipeline testing: Unit and integration tests for Data Factory pipelines, Databricks notebooks, and Synapse jobs to validate transformations and idempotency.
- Streaming and CDC testing: Validate exactly-once/at-least-once semantics, windowing, late-arrival handling, and CDC flows (Event Hubs → Stream Analytics/Databricks).
- Data quality checks: Automate checks for nulls, duplicates, referential integrity, statistical ranges, and business-rule compliance in pipelines.
- Test data management: Use representative samples, masked production extracts, and synthetic data to preserve privacy while ensuring coverage.
- Performance and scalability testing: Load-test Databricks jobs, Synapse SQL pools, and Data Factory throughput to measure latency, concurrency, and cost under realistic volumes.
- Observability and lineage: Integrate Azure Monitor, Log Analytics, and Purview for pipeline telemetry, lineage, and root-cause analysis.
- Automation and CI/CD: Embed unit/integration tests in Azure DevOps or GitHub Actions; run tests on PRs and before deployments to staging/production.
- Security and compliance testing: Validate RBAC, managed identities, encryption at rest/in transit, masking, and GDPR/industry controls across stores and pipelines.
- Algorithm and analytics validation: Test aggregations, ML feature pipelines, and model inputs/outputs against golden datasets and statistical baselines.
- Failure and chaos testing: Inject node failures, network partitions, and storage throttling to verify retries, checkpointing, and recovery behaviors.
- Tooling and frameworks: Use data-diff tools, stream simulators, schema registries, and test harnesses for distributed validation at scale.
- Best practices: Start small with reproducible unit tests, maintain golden datasets, automate tests in CI, monitor drift, and document lineage for audits.
