Big Data Testing Services W3Softech

Big Data Testing Overview

  • Big Data testing is the process of verifying and validating the different components of a Big Data system to ensure its accuracy, reliability, and performance.
  • It involves testing various aspects such as data ingestion, storage, processing, and retrieval.
  • 1.Data Ingestion:Data Ingestion Testing involves testing the the Big Data system. It includes verifying the correctness of data ingestion, ensuring data integrity, and evaluating the system's ability to handle large volumes of data.
  • 2. Data Storage Testing: This focuses on testing the storage layer of the Big Data system, such as Hadoop Distributed File System (HDFS) or NoSQL databases. It includes testing the scalability, performance, and fault tolerance of the storage layer.
  • 3. Data Processing Testing: This involves testing the processing layer of the Big Data system, which includes frameworks like Apache Spark or Apache Hadoop MapReduce. It includes testing the correctness and efficiency of data processing algorithms, as well as the system's ability to handle complex computations
  • 4. Data Retrieval Testing: This focuses on testing the retrieval and querying capabilities of the Big Data system. It includes testing various types of queries, such as ad-hoc queries,, and real-time queries. Performance, accuracy, and response times are key factors to consider in this type of testing.
  • 5. Data Quality Testing: This refers to testing the quality of data stored in the Big Data system. It includes validating the accuracy, consistency, and timeliness of data. Data profiling and data cleansing techniques are often used in this type of testing.
  • 6. Performance Testing: This involves testing the performance and scalability of the Big Data system under different load conditions. It includes measuring response times, throughput, and resource utilization to ensure the system can handle large volumes of data and concurrent users.
  • 7. Security Testing: This focuses on testing the security features of the Big Data system, including access controls, data encryption, and data privacy. It includes identifying vulnerabilities and ensuring compliance with security standards and regulations.
  • 8. Integration Testing: This involves testing the integration between various components of the Big Data system, as well as with external systems and applications. It includes verifying data flow, compatibility, and interoperability between different components.
  • Overall, Big Data testing plays a crucial role the accuracy, reliability, and performance of Big are making informed decisions and deriving meaningful insights from large volumes of data

Big Data Facts:

According to analyst firm Gartner,
"The average organization loses $8.2 million annually through poor Data Quality". And yet, according to the Experian Data Quality report, "99% of organizations have a data quality strategy in place".
This is disturbing in that these Data Quality practices are not finding the bad data that exists in their data. This is a problem that needs to be solved.

Big Data Facts W3Softech
Big Data Steps W3Softech

Big Data Testing Steps

  • Data Staging Validation
  • Map Reduce validation
  • Output validation phase

Architecture Testing

  • Big Data processes very large volumes of data and is highly resource intensive. Hence, architectural testing is crucial to ensure success of your Big Data project.
  • Poorly or improper designed system may lead to performance degradation, and the system could fail to meet the requirement.
  • Performance and Failover test services should be done in a Big Data environment.
  • Performance testing includes testing of job completion time, memory utilization, data throughput and similar system metrics. While the motive of Failover test service is to verify that data processing occurs seamlessly in case of failure of data nodes
Big Data Architecture W3Softech
Big Data Environment W3Softech

Testing Environment

Test Environment needs depend on the type of application you are testing. For Big data testing, test environment should encompass:

  • It should have enough space for storage and process large amount of data
  • It should have cluster with distributed nodes and data
  • It should have minimum CPU and memory utilization to keep performance high


Big Data Cluster Big Data Tools
  • NoSQL:
  • Map Reduce:
  • Storage:
  • Servers:
  • Processing
  • CouchDB, MongoDB, Cassandra, Redis, ZooKeeper, Hb
  • Hadoop, Hive, Pig, Cascading, Oozie, Kafka, S4, MapR, Flume
  • S3, HDFS ( Hadoop Distributed File System)
  • Elastic, Heroku, Elastic, Google App Engine, EC2
  • R, Yahoo! Pipes, Mechanical Turk, BigSheets, Datameer