Big Data App Testing Comprehensive Guide

• Big Data Application Testing Team Size: Optimally Scalable and Flexible
• Evaluating test team member rates based on skills and expertise
• Big data application testing time based on:
  • Concurrent dev-test iterations count depends on project complexity
  • Test Case/Script Count: X Total
  • Test Case/Script Effort Allocation (Dev & Maintenance)
  • Test Automation Percentage
  • Regression test coverage

• Approximate QA/Testing Professionals Count: Unspecified
• Salary range for big data testing/QA specialists: $92K - $130K annually
• Training available for enhancing testing skills upon request

• Verifies the testability, completeness, and accessibility of a big data application's requirement specifications, along with its architecture and technology stack documentation
• Formulates comprehensive big data app testing strategy & roadmap
• Chooses large-scale data testing tools
• Develops test data architecture and test data management system design
• Facilitates teamwork alignment between two quality assurance groups
• Manages comprehensive project execution

• Delimits manual testing scope for designated big data app component
• Devises detailed test strategy for specified big data app component
• Streamlines test engineer tasks, resolves recurring challenges, and elevates complex dilemmas to Quality Assurance management
• Assesses and enhances test engineer productivity by identifying areas for process improvement

• Devises automated test architecture tailored to specific big data app segments
• Selects fitting test automation frameworks and tools, or oversees development of a tailored test automation solution suited to the big data system in question

• Oversees test automation engineering, emphasizing granularity and maintainability of test scripts for effective evaluation

• Specializes in creating, executing, and managing UI/API big data test scripts via automation
• Analyzes test outcomes, documents defects found
• Maintains a flexible, reusable regression test suite for ensuring post-change consistency within a specific big data application

• Examines big data application's specifications and architecture, enhancing test scope and precision for improved quality assurance
• Test-case development, execution, and maintenance for validating complex end-to-end user flows in big data applications
• Test management tool exhibited flaws upon evaluation

• Oversees big data performance testing ecosystem and associated tools
• Assists big data development teams in identifying architectural bottlenecks capable of impacting application performance
• Diagnoses & suggests resolutions for performance problems

• Constructs a threat model for large-scale data applications, anticipating potential security risks to enable the app's designers and programmers to address them effectively in the design phase itself
• Performs both manual evaluations and automated scans for vulnerabilities, along with penetration tests on large-scale data applications
• Assesses and prioritizes detected vulnerabilities based on WASC, OWASP, and CVSS standards, offering suggestions for effective issue resolution

For a robust big data application that encompasses both operational and analytical segments, comprehensive API-level functional testing is essential. The individual functionality of each big data application component must first undergo validation in an isolated environment.

In an instance where your big data operational solution utilizes an event-driven architecture, a test engineer initiates input events to individual system components (such as a data streaming tool, an event library, a stream processing framework, etc.). These tests scrutinize each component's output and behavior to confirm compliance with specified requirements. Subsequently, end-to-end functional testing is crucial for guaranteeing the seamless operation of the entire application.

Big Data Application Integration Testing ensures flawless interaction between the entire application, third-party software, and various components. It verifies compatibility among diverse technologies employed in your custom application's architecture and technology stack.

In an analytics setup utilizing technologies from the Hadoop ecosystem, such as HDFS, YARN, MapReduce, and related tools, a test engineer ensures smooth communication between these components and their elements. This includes verifying the interaction of key elements like the HDFS NameNode and DataNodes, as well as the YARN ResourceManager and NodeManager.

In event-driven big data applications, producers and consumers of events rely on their respective data schemas for interaction. During the validation phase of integrations within operational components, collaboration between test and data engineers is crucial. They should ensure that data schemas are initially well-designed, mutually compatible, and maintain compatibility post any schema modifications.

To ensure the big data application’s stable performance, performance test engineers should:

• Evaluate real-time access latency, peak data and processing capacities, and response times from various geographic locations to account for potential network throughput variations across regions in a big data application
• Assess application performance under high stress loads and spikes for optimal functionality
• Ensure app resources usage is optimized
• Assess app scalability solutions
• Test application core functions under heavy load for validation

For maintaining data security, a Security Test Engineer should perform rigorous assessments on vast, confidential information.

• Ensure secure storage and transmission of data with robust encryption standards
• Ensure data is isolated and non-redundant for efficient and secure storage
• Identify and resolve app architecture flaws impacting data security
• Examine role-based access control settings for proper rule configuration

Cybersecurity experts conduct comprehensive application and network security assessments, involving scans and penetration tests, for enhanced system protection.

Testing in big data warehouses involves verifying the accurate perception of SQL queries and ensuring adherence to business rules and transformation logic within DWH columns and rows.

Business Intelligence (BI) testing, integral to Data Warehouse (DWH) testing, guarantees data consistency within Online Analytical Processing (OLAP) cubes, ensuring seamless execution of OLAP functions such as roll-up, drill-down, slicing, dicing, and pivoting.

Database testing involves verifying query handling capabilities and ensuring optimal application performance by checking database configuration settings. Additionally, it's crucial to assess data backup and recovery processes for efficient system maintenance.

Optimal test case design varies based on unique aspects of the chosen NoSQL database, as each employs distinct data structures and querying languages.

Inconsistency, inaccuracy, non-auditability, disorderliness, and especially non-uniqueness are inherent in big data applications due to their multi-component structure for fault tolerance. Despite this, it's crucial for big data testing and engineering teams to assess the quality of your big data at these potentially problematic levels: consistency, accuracy, auditability, orderliness, and uniqueness. This ensures that the data is of acceptable quality for effective analysis and decision-making.

• Data Ingestion (Batch, Stream)
• Data processing pipeline: Extract-Transform-Load (ETL)
• Analytics and transactional app data management, plus analytics middleware integration