Big Data Testing

Big data testing

Overcome testing challenges posed by the 5 V’s of big data - volume, velocity, variety, veracity and value

Organizations across the globe are increasingly leveraging Big Data to gain insights on the performance of the business strategy, priorities, and outcomes. Insights gained are driving further innovations in many organizations. Therefore, it is paramount to establish a trustable and agile information and data management practice and process to align with the changing needs of the business.

Trigent’s Big Data testing ensures the availability of clean and reliable data by overcoming the challenges posed by the 5 Vs of Big Data - volume, velocity, variety, veracity, and value. Trigent has the capability, expertise, and partner ecosystem to offer end-to-end Big Data Testing from data acquisition testing to data analytics testing.

Improve reliability of Big Data solution to drive business growth

Challenges

  • Scalability: a vast volume of data with a large number of data nodes
  • Integration of data from diverse sources: end-to-end testing across the integration data points
  • Data storage: high volume of data produced and stored across different test environments
  • Data quality: variety and veracity of data - unstructured and inconsistent data from diverse sources, leading to errors. Missing data, inconsistent data, logic conflicts, and duplicated data all result in data quality challenges
  • Data security and privacy: need to anonymize vast volumes of data to reduce vulnerability
  • Real-time change: manage the challenge of constant updates to test data based on changing needs that impact the business processes

Our services

Big Data Migration Testing

Big data migration testing

  • Source to Target Validation
  • Post Migration Validation
  • Multi-Source Data Integration Validation
Big Data Sources Extraction Testing

Big data sources extraction testing

  • Data Extraction Validation
  • MapReduce Jobs Validation
  • Spark Jobs Validation
  • Hive Queries/Pig Jobs Validation
  • Data Storage in Hadoop Distributed File System (HDFS) and NoSQL Database DB Validation
Big Data Ecosystem Testing

Big data ecosystem testing

  • Referential Integrity Tests
  • Constraints Check
  • Metadata Analysis
  • Statistical Analysis
  • Data Duplication Check
  • Data Accuracy/Consistency Check
Data Analytics and Visualization Testing

Data analytics and visualization testing

  • Report Objects Validation
  • Reports Validation
  • Dashboards Validation
  • Mobile Reports Validation
  • Visualization Validation

Build optimum test environment for Big Data Testing

Methodology

Apache Flume Apache Spark Apache Nifi Apache Sqoop Apache Pig Logstash Streamsets SQL Server Integration Services (SSIS) Cognos Data Manager Informatica PowerCenter OpenText Integration Centre Apache Falcon
Methodology

Benefits of Big data testing framework

  • Raw data analysis (structured & unstructured)
  • Validation of data load frequency, Query processing, scheduling of jobs, load dependency checks
  • ETL validations
  • Metadata layer testing
  • High-volume and high-scale tests
  • Testing statistical, time series and probabilistic analysis
  • Validate AI-based predictive data models
  • Mapping of every field in the report with the schema and Source System values
  • Drilling, sorting and export functions of the reports in the Web environment

Time to learn