Big Data Testing Services

Cybage consultation and advisory services help define Big Data Quality Assurance practices to meet your business objectives in the form of:

• Big data software testing needs assessment and implementation.
• Test strategy and process conceptualization covering all big data characteristics & layers.
• Test tools, technology and platform feasibility, and selection.
• Data analysis, metrics definition and report analysis.

Under execution service, our testing team handles end-to-end testing of big data application covering different areas:

Functional correctness of big data ecosystem:

• Grey box validation at the component level for functional correctness and accuracy.
• Outside-In data pipeline testing as and when components start integrating in the big data application.
• Cyclomatic analysis, pairs testing, and sampling for effective and efficient test data.

Validation of architecture and building blocks:

• Comprehensive benchmark of big data components to finalize the solution stack.
• Validate configurations, integrations, and tuning concepts of various solution layers such as data ingestion, processing layers, and storage.

Interoperability of deployment:

• Validating data integrity and exchange between multiple combined data system, data lakes as well as between on premise system and cloud-based system in a timely manner and in various compartmentalized cases.

Application readiness for variability:

• Analyze and participate to decide the behavior of a scaling approach and its impact on a defined key performance indicator (KPI) of the application under Scale Out/In cases.
• Validate expected architectural linearity.

Infrastructure cost optimization:

• Test executions of the application using configuration testing and capacity testing to define behavior patterns and to identify the most appropriate hardware resources and cloud services such as Elastic Compute Cloud (EC2) instances and Storage Input Output Operations per Second (IOPS) to optimize resource consumption and billing.

Resilience readiness:

• Checks to confirm the resiliency and recoverability of the application in contrast to various partial and complete failures of consumed platform components and services.
• Validate application reliability over time and parallel transaction failure for essential backups.