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Implementation of Testing in Big Data

We have several areas where big data and big data strategy are required. There are various types of big data testing in big data projects such as database testing, infrastructure, and performance testing, and also functional testing. Big data is defined as a large volume of data structured or unstructured. Data may exist in any format like flat flags, images, videos, etc. The primary Big data characteristics are the three V’s-Volume, Velocity, and Variety where volume represents the size of data collected from various sources like sensors, and transactions, velocity is described as speed and variety represents the formats of data.

How does this Big data testing strategy work?

  1. Data Ingestion testing

The data collected from multiple sources like CSV sensors, logs, and social media are further stored in HDFS. Here the main motive is to verify that the data is adequately extracted and correctly loaded into HDFS. Tester will make sure that the data properly ingests into the defined schema and also have to verify that there is no data corruption. Here the tester validates the correctness of data by taking some little sample source data and after ingestion, compares both source data and ingested data with each other. The tools required are

  1. Apache Zookeeper

Apache Zookeeper is a software plan for the Apache software foundation. This is a centralized service for distributed systems for hierarchical key-value collection, which is used to provide synchronization services, distributed configuration services, and naming registries for large distributed systems.

Overview of Apache ZooKeeper, Installation of ZooKeeper on AWS

Apache Zookeeper architecture includes client-server architecture where the servers are nodes that provide the services and the clients are nodes. That makes use of the service.

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Client- This is to run the information from server-client nodes in a distributed applications cluster. The client sends a message to the server to let the server know that the client is alive and the client resends the message to another server if there is no response from the connected server received.

Server- An acknowledgment will be given by the server to the client to inform that the server to the client will be alive and that the server provides all services to the clients.

Leader- If any of the server nodes failed this server node will do the recovery.

  1. Apache Sqoop

It is a big data tool for transferring data between Hadoop and relational database servers sqoop is used to transfer data from RDBMS like Mysql and Oracle to HDFS.

Features of Big Data sqoop

  1. Sqoop Import-Big data hadoop imports every single table from RDBMS to HDFS. Each row within a table is treated as a single record in HDFS. All records are then stored as text data in the format of text files or as binary data in sequence files.
sqoop import architecture

Sqoop Big data tool will export files from HDFS back to an RDBMS. All the files are given as input to sqoop containing the records which are called rows in the table. It is read and then parsed into a set of records and delimited with the user-specified delimiter.

  1. Parallel import/Export-sqoop big data tool will use the YARN framework to import and export data. This will provide fault tolerance on top of the parallelism.
  2. Import results of SQL query- Big data Hadoop sqoop will allow importing of the results returned from an SQL query into HDFS.

Challenges in Big Data Testing

Some testing challenges need to be addressed by the big data testing approach.

  1. Test Data-It’s been observed that exponential growth of data for a few years. A large amount of data is generated and stored in large data centers. There is demand for efficient storage and also a way to process it in an optimized way. It is considered that the telecom industry will create a big amount of number call logs daily and they have to be processed for customer experience and compete in the market.
  2. Environment- The processing of data highly depends on the environment and its performance. This is an optimized environment setup that gives high performance and fast data processing results.


  1. What is Big Data testing?
  2. What are the characteristics of Big data testing?
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