Today, businesses are exponentially generating a variety of data in large volumes and need timely processing to derive relevant analytics from it. This involves multi-layered, complex, big data platform implementations to cater for various business processing needs such as batch processing, near real-time processing, big-query interactions, ML processing, etc.

Such highly distributed and integrated platform solutions incur additional testing challenges around added data dimensions (volume, variety, velocity, veracity, value and variability), complex technologies, and infrastructure provisions.

Defining pragmatic testing approach for such complexities requires a greater understanding of big data platforms, underneath configuration dependencies, data sampling and test data management approaches, statistical validation techniques, and purpose specific backend test tooling.

Our big data test service group has facilitated many enterprises to validate such implementations and continues to enhance testing of big data technologies by focused R&D. We facilitate clients with an array of test frameworks and accelerators to expedite and achieve required quality on optimized big data platform.

Big Data Testing Services Portfolio:


  • Focused test excellence group: CoE-driven dedicated team of QA architects and big data test consultants
  • Purpose-engineered comprehensive test procedure and process
  • Reusable test assets: technical test scenarios, automation frameworks, cluster validation libraries and analyzers
  • Dedicated big data lab for R&D of QA activities.
  • Extensive test coverage with multiple dimensions: functional, test automation, performance, reliability, optimization, and security

Big Data Testing Offerings:

Cybage offers Big Data testing services with two capacities:

  • Big data test consultation
  • Test engineering and execution services

Big Data Test Consultation:

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

Test Engineering and Execution Services:

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 behaviour 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 behaviour 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

Our Capabilities:



Expertise in different tools required for Big Data Testing, covering functional validations, data processing, data validation, benchmarking, cluster monitoring, and failure simulation tools.