Top-10 Open-Source Big Data Testing Tools & Software
Tags: bigdatatesting, topbigdatatestingtools
Published: 2021-12-23 05:24:39

There’s an array of big data testing tools and technologies available that brings cost efficiency, better quality, and time management into the data analytics tasks. But before we jump into the whirlpool of testing tools, let us understand what exactly is big data testing?

Big data testing is a procedure of examining and validating the big data application’s functionality. As we know, big data is a collection of large data sets that traditional storage systems cannot process. Testing such large data sets would take some unique tools, terminologies, and technologies.

## **Big Data Testing Tools**
There are numerous testing tools used for integrating big data with platforms such as MongoDB, NoSQL, Hadoop, AWS, and Teradata. It is necessary so that DevOps can support continuous delivery. The tools should have good reporting features, flexible to changes, reliable, economical, and scalable. 

Let’s take a closer look at some of the big data testing tools:
**1. Apache Zookeeper**
It is an open-source project by Apache that provides a centralized service for delivering synchronization, group, configuration information, and naming services over large clusters in a distributed system. In testing big data, it is mainly used for data ingestion purposes. Its main goal is to make systems easy to handle with reliable and improved propagation of changes.

**2. Apache Kafka**
Kafka is an open-source distributed event streaming platform by Apache used for high-performance data pipelines, data integration, mission-critical applications, and streaming analytics. In big data testing, the main purpose of Kafka is processing and ingesting streaming data in real-time. Its three main functions are:
Real-time processing of streams of records
Publishing and subscribing to the records stream
Effectively storing records in the order they were generated

**3. HBase**
HBase is used in big data automation testing and is an open-source project, which is horizontally scalable. It is a distributed column-oriented database built on the top of the Hadoop file system. It provides quick and random access to huge data structures. HBase is a part of the Hadoop ecosystem that grants real-time read/write access to data stored in the Hadoop file system.

**4. Hadoop Distributed File System (HDFS)**
HDFS is a primary data storage system used in big data testing for Hadoop applications. It has a NameNode and DataNode architecture for implementing distributed file system to provide high-performance data access across Hadoop clusters. HDFS is a part of the Hadoop ecosystem that provides means for handling and supporting big data applications.

**5. MapR**
MapR is a distributed data platform used for data processing in big data testing. It includes storage, processing, analysis, and management of data for analytics and AI applications. MapR is also responsible for improving the reliability and security of critical information. It supports open-source tools like Hadoop, Drill, and Spark, along with open industry standards like NFS and POSIX.

**6. HiveQL**
The use of HiveQL in [big data testing](https://blog.qasource.com/guide-to-big-data-testing) is to analyze large-scale data by using batch processing techniques. It uses a language similar to structure query language for interacting with the database. HiveQL filters the data using built-in operators and functions to fulfill certain conditions and give the finest results.

**7. Kettle**
Kettle, also known as Pentaho data integration, is an open-source ETL (extract, transform, and load) application used for data migration in testing big data. However, the ETL concept is slightly modified in Kettle as it consists of four elements, ETTL. It stands for data extraction from source databases.

**8. Pig Latin**
Pig Latin is used to automate repetitive tasks in the testing of big data. It is a data flow language used by Apache Pig for analyzing Hadoop data. Also, it allows data workers to write complex data transformations and abstract the programming from the Java MapReduce. The structure of Pig programs is amenable to substantial parallelization, which enables them to handle large data sets in big data.

**9. Amazon S3**
Amazon S3 is used as a data storage in big data QA testing. Also known as a storage for the internet, it makes web-scale computing easier for developers. Amazon S3 is a simple web service used for storing and retrieving data anywhere from the web. It offers industry-leading scalability, security, performance, and data availability.

**10. CloverDX**
Although CloverDX is a commercial product by EULA, it has some parts built under several open-source components. Used for data migration in big data QA, CloverDX acts as a platform for automating, designing, publishing, and operating the entire data pipeline.


## Conclusion

As a core software development company, it would be difficult to facilitate the quality assurance and testing practices of big data applications. But by outsourcing it to a renowned QA testing company like QASource, you can avoid the hassle of focussing on big data QA. QASource uses best-in-the-industry tools and technologies to provide state-of-the-art QA solutions. To know more, contact QASource now.