Description
- Big Data Testing Features Basics to Advanced
- Definition: Big Data Testing validates correctness, quality, and performance of large-scale data pipelines and analytics.
- Data source validation: Verify ingestion from databases, logs, IoT, APIs, and files for completeness and schema conformance.
- Data quality testing: Check accuracy, consistency, duplicates, nulls, and business-rule compliance across massive datasets.
- Schema and contract testing: Enforce source-to-target schema, data types, and API contracts to prevent downstream breakage.
- Pipeline testing: Validate ETL/ELT logic, transformations, joins, aggregations, and idempotency across batch and streaming flows.
- Performance and scalability testing: Measure throughput, latency, resource usage, and SLA compliance under realistic data volumes and concurrency.
- Streaming and CDC testing: Test event ordering, exactly-once/at-least-once semantics, windowing, and late-arrival handling for real-time systems.
- Algorithm and analytics validation: Verify correctness of aggregations, ML feature pipelines, and statistical outputs against known baselines.
- Test data management: Create representative, privacy-safe datasets using sampling, masking, and synthetic data generation.
- Automation and CI integration: Automate unit, integration, and regression tests in CI/CD pipelines for repeatable validation.
- Observability and lineage: Capture metadata, lineage, and metrics to trace failures, debug issues, and assess impact of changes.
- Security and compliance testing: Validate encryption, access controls, masking, and regulatory requirements across data stores.
- Tooling and frameworks: Use distributed test harnesses, data diff tools, schema registries, and stream simulators to scale tests.
- AI-assisted testing: Apply anomaly detection and test-case generation to prioritize checks and surface subtle data issues.
- Failure and chaos testing: Inject faults, node failures, and network partitions to validate resilience and recovery behaviors.
- Best practices: Start with small reproducible tests, maintain golden datasets, monitor drift, and embed tests close to data producers.
