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Big Data Testing Consulting, Services and Solutions Company

Automated Big Data Testing Solutions for any Volume, Variety, and Velocity of Data

Solutions

Big Data Testing Solution Offerings

XenonStack, being automated Big Data Testing Solution Company helps you check organized and unstructured informational indexes, patterns, approaches and innate procedures living at various layers in your big data platforms, for example, ‘Apache Sqoop’, ‘Apache Nifi’ at Data Ingestion Layer, ‘Spark’, ‘MapReduce’, ‘Apache Pig’ at Data Processing Layer and ‘HBase’, ‘Hive’, ‘Cassandra’ at Data Storage Layer. Our Continuous Performance Testing Services

  • Load Testing
  • Stress Testing
  • Spike Testing
  • Cloud-Based Performance Testing
  • Performance Benchmarking
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Big Data Testing Strategies

This Architecture helps in designing the Data Pipeline with the various requirements of either Batch Processing System or Stream Processing System

Big Data Migration Testing

  • Data Migration Test Strategy
  • Source to Target (NoSQL DB/Hive/HDFS) Field Validation
  • Data Accuracy Validation Post Migration
  • Multi-Source Data Integration Validation

Security Testing

  • Security Test Assessment
  • Role Based Security Testing
  • Default Permission Configuration Check
  • Data Node and Name Node direct access validation

Performance Testing

  • Performance Test Strategy
  • Performance Monitoring Scripts Creation
  • Performance Monitoring and Identifying Bottlenecks

Big Data Sources Extraction Testing

  • Data Processing/ETL Test Strategy
  • Data Extraction Validation
  • MapReduce Jobs Validation
  • Spark Jobs Validation
  • Hive Queries/Pig Jobs Validation
  • Data Storage in Hadoop Distribution File
  • System (HDFS) and NoSQL Database
  • DB Validation

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

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

Testing Methods and Tools for Pre-Hadoop Processing

Big Data systems typically processes a mix of structured data, unstructured data, and semi-structured data. Data sources can include a local file system, HDFS, Hive Tables, Streaming Sources, and Relational or other databases.

Typical Testing Methods

Data Type Validation, Range and Constraint Validation, Code and Cross-Reference Validation, Count of Rows Validation in ETL Data Process, Structured Validation

Tools for Validation Pre-Hadoop Processing

Apache Flume, Apache Nifi, Apache Sqoop, Apache Spark, Apache Pig, Logstash, Collected, Streamsets

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Testing Methods and Tools for Hadoop MapReduce Processes

Hadoop MapReduce is a software framework for easily writing applications that processes vast amounts of data in-parallel or large clusters.

Methods and Testing Tools for Hadoop MapReduce Processes

MRUnit - Unit Testing for MR Jobs, Local Job Runner Testing - Running MR Jobs on a single machine in a single JVM, Pseudo-Distributed Testing - Running MR Jobs on a single machine using Hadoop, Full Integration Testing - Running MR Jobs on a QA Cluster

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Testing Methods and Tools for Data Extract and EDW Loading

Data Warehouses play a vital role in Big Data. Companies rely on Data Warehouses for collecting information on their business operations, markets, and client behavior to identify patterns, and collect the results to identify more business opportunities and operational improvements.

Typical Testing Methods -

The data in the data sources is validated directly in the Data Warehouse, The data is validated from the data sources through each step of the extract, including the final load in the Data Warehouse

Testing Tools for Data Extract and EDW Loading

SQL Server Integration Services (SSIS), Informatica PowerCenter, OpenText Integration Centre, Cognos Data Manager

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Testing Methods and Tools for Big Data Analytics

Big Data Analytics refers to the process of collecting, organizing, and analyzing large sets of data to discover patterns and reporting useful information. Specific areas within analytics include Predictive Analytics, Enterprise Decision Management, Retail Analytics, Predictive Science, Credit Risk Analysis, and Fraud Analytics.

Testing Methods for Big Data Analytics

Validating the Dashboard Report Model, Checking the Source Record Count and Target Record Count, Authentication Testing, Data Level Security, Bursting the Reports, Buzz Matrix Validation, User Acceptance Criteria, Time Series Functions Validations, End-to-End Testing

Tools for Big Data Analytics

Apache Falcon - Falcon simplifies the development and management of data processing pipelines with a higher level of abstraction taking the complex coding out of data processing applications by providing out-of-the-box data management services.

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Testing Methods and Tools for Performance Testing and Failover Testing

Identify response time, maximum online user data capacity size, and maximum processing capacity with Performance Testing. Failover Testing validates the recovery process and ensures data processing continues correctly when switched to other data nodes.

Testing Techniques for Performance and Failover Testing

Static/Default Installation, Backup/Restore, Data Replication, Rolling Installation

Testing Tools for Performance Testing and Failover Testing

SandStorm, JMeter, Nagios, Zabbix, Ganglia, JMX Utilities, AppDynamics, Jespen

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