ATAGTR2017 Bee-Hive approach for Big Data Testing [End to End Continuous Test Automation solution for Big Data]
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The document discusses big data testing methodologies presented at the Agile Testing Alliance Global Testing Retreat 2017, highlighting essential processes such as data extraction, transformation, analytics, and validation across various tools and platforms. It emphasizes the significance of big data in driving ROI through machine learning and artificial intelligence, focusing on how data engineering aids in improving decision-making. Additionally, it outlines testing strategies for functional, performance, and security aspects within a big data ecosystem.
ATAGTR2017 Bee-Hive approach for Big Data Testing [End to End Continuous Test Automation solution for Big Data]
- 1. #ATAGTR201 7 16th 17th March Bee-Hive approach on Big Data testing - Usharani Subramanian
- 2. Agile Testing Alliance Global Testing Retreat 2017 Structured Unstructured External Files - CSV Streaming Heterogeneous Sources ApplicationsINGESTION TRANSFORMATION ANALYTICS Data Lake BI & Visualization tools Functional UI applications Excel/CSV Reports
- 3. Agile Testing Alliance Global Testing Retreat 2017 Big Data Ecosystem Testing Big Data Extraction Testing Data Transformation Testing Data analytics and Visualization Testing Non Functional Testing • Pre-Hadoop Validation • Meta Data Analysis and Validation • Impala & HDFS Data Storage Validation • Validation on Data Extraction from Source • Referential Integrity & Constraint Validation • Heterogeneous Data Integration Validation • MDM Validation • Data Quality Validation • Data Correctness/ Completeness Validation • Business Rule Validation • HDFS to SSAS validation • Dashboard Validation • Visualization Validation • Report generation and Validation • Performance Validation • Security Validation • Regulatory Compliance Validation Big Data Tools • Query Surge, Talend, Informatica - Functional • YCSB-CDH, JMX Statics, JMeter – Performance • OWASP ZAP, Spider – Security scanner • Manual UI Validations over Dashboards
- 4. Agile Testing Alliance Global Testing Retreat 2017 Functional Automation & Data validation – QuerySurge, Informatica, Talend, etc Pre-hadoop validation – Amazon EMR, Sqoop, etc Performance Testing - YCSB-CDH, JMX Statics, Jmeter Security Testing – UI level through ZAP, Spider: other through Regulatory Compliance CI/CD Scheduler – Jenkins, oozie, etc
- 5. Agile Testing Alliance Global Testing Retreat 2017 Workflow Data Testing: 1. UI > Impala: Scenarios derived from UI 2. Excel > ODBC > HDFS: Random Queries 3. ThirdParty tool > ODBC > HDFS: Random Queries System Integration Testing: 1. Big Data Ecosystem Validation (Architecture level Validation) 1. Data validation from Heterogeneous Data Sources to HDFS 2. Data validation from Data lake to Analytical server 3. Data validation at Application layer 4. UI validation of Application layer • Performance • Responsive Web Designing – Desired pixel? • Browser Compatibility – Desired browser? Performance Testing : Load and Stress 1. Query performance on Impala 2. UI level Performance validation Security Testing: 1. Security aspects covered as a part of Regulatory compliance (PCI, HIPPA, etc) 2. Application level Vulnerability scanning
- 6. Agile Testing Alliance Global Testing Retreat 2017 • 4 Vs of Big Data – 5th V? • Big data is just the Data Engineering part – Preparatory Stage. ROI lies in data Science part of it • Real ROI of Big Data lies in Machine learning & Artificial Intelligence. How? • In Machine learning, the Big Data helps to build a better model that facilitates better business decisions
- 7. Agile Testing Alliance Global Testing Retreat 2017 Data Engineering Clean Refine Transfor m Store Social Media Database s Internet of Things Streaming Data DataIngestion Data Science Extract Feature Vectors Apply Modelling Customer Personalization Feature Vectors Deduce model based on Self learning Algorithm Use existing data to make better business decisions 70% Training Data 30% Evaluation Data • How Big Data helps building the better Machine learning model?
- 8. Agile Testing Alliance Global Testing Retreat 2017 Thank You