Big Data Testing: All You Need to Know to Start With Big Data Testing

April 21, 2022

Generally, when we hear the term Big Data, we get the idea of a huge volume of information flowing on and which gets processed further to meet business requirements. Big data computation frames another pattern for ineluctable processing with the degree of information development and the quickness of data expansion.

With the origin of new advanced techniques, an enormous amount of organized and unstructured data is delivered, gathered from different sources like social media, audios, images, websites, videos, and so forth which is hard to oversee and process. As the data is streamed from a wide variety of data sources, it poses challenges to the testing community.

What exactly is Big Data Testing

Most of the customers might end up asking one question that says “Why exactly do we need Big Data Testing?” you might have written the right queries to process data and your Architecture might just be fine as well. Yet, there might be many possibilities for failure.

Testing is the art of achieving quality in any software application, perfection in terms of functionality, performance, and user experience But when it comes to big data testing, First and foremost, you must stay focused on the functional and performance aspects of an application. Performance is the key parameter for any big data application which is meant to process large volumes of data.

Successful processing of such a huge volume of data using a commodity cluster with some other supportive components needs to be verified. Big Data processing should be robust and accurate which demands a high level of testing. Don't Let Bad Data Undermine Your Big Data.

Key Benefits of Big Data Testing

Key Benefits of Big Data Testing

Big Data Characteristics, the four V's

Before we plot the strategy for Big Data Testing, we should have a good understanding of its characteristics, the four V's

Big Data Characteristics

Volume: Data Size

With the rise of the Web, then mobile computing the data comes in generated from different equipment, networks, media, and IoT devices as the source from which data is extracted and aggregated at the organization hub.

For instance, consider the case of a variety of data sent by IoT devices to the network infrastructure, the information collected from lot of various surveys, feedback forms, etc., this aggregated information forms the enormous size of data that needs to be properly analyzed.

Variety: Different Types of Data

A common theme in any Big Data system is that the source data is increasingly diverse involving different types of data that come in for processing can be of a variety of forms and formats. For instance, information can be stored with different file formats, such as .txt, .csv, .xlsx, etc.

An organization must be able to deal with text from social networks, voice recordings, image data, video, spreadsheet data, and raw feeds directly from sensor sources.  Sometimes the information may not be in the desired format e.g.; data can come in the form of SMS, multimedia, pdf, or any other doc format or something else which we may not have contemplated.

It makes it quite crucial for the organization to handle such a wide variety of data efficiently as at present a wide range of formats of data is available to seek information from it.

Big Data Forms

Big Data Forms

Velocity: Speed of Streaming Data

This characteristic of big data provides a glimpse of the pace of data i.e., at what rate data is arriving from various sources like networks, social media, and other business processes.

This high-speed real-time data is massive and comes in a continuous fashion which may need immediate processing. There is even a possibility of mutation in the data over time. 

Veracity: The Degree to which the Data can be Trusted

There are a wide variety of sources of data streams available which produce a huge amount of data. With these many different available sources, this data becomes vulnerable to outliers or noise. Due to this the nature or behavior of the data may change.

The term Veracity describes this as the uncertainty of data that poses a huge impact on the decision-making process of the organization

Big Data Testing Strategy

Testing an Application that handles large amounts of data would take the skill to a whole new level and requires out-of-the-box thinking. The main tests that the Quality Assurance Team concentrates on are based on three Scenarios. 

  • Batch Data Processing Test
  • Production Data Processing Test
  • Real-Time Data Processing Test
Big Data Testing Strategy

Batch Data Processing Test

The Batch Data Processing Test involves test procedures against the applications which are running in Batch Processing mode where the application processes data using Batch Processing Storage units like HDFS. This testing mainly involves

  • Running the application against faulty inputs
  • Verifying the volume of the data

Production Data Processing Test

The Production Processing Test is performed on the data when the application is in Real-Time Data Processing mode using Real-Time Processing tools like Spark. Real-Time testing involves a series of tests that will be conducted in a real-time environment and it is checked for its stability.

Real-Time Data Processing Test

The Real-Time Data Processing Test integrates the real-life test protocols that interact with the application in the view of the real-life user. This Data Processing mode uses processing tools like HiveSQL.

Big Data Test Environment

A big data test environment should be well established to process a large amount of data similar to the case of a production environment. Real-time production environment clusters generally consist 0f multiple cluster nodes and data is distributed across all the cluster nodes.

A cluster may have two nodes, in-premise or cloud. For testing in big data, it needs the same kind of environment with some minimum configuration of nodes.

Scalability of the test environment is also desired to be there in big data testing, it helps to analyze the performance of the application with the increase in the number of resources.

Big Data Testing Phases

There are 3 main phases in big data testing. They are 1. Data staging validation, 2. Map-Reduce validation, 3. Output validation phase.

Phase 1: Data Staging Validation

The very first stage of big data testing which is also known as a Pre-Hadoop stage is comprised of the below process validations

  • Validation of data is very much important so that the data collected from various sources like weblogs, RDBMS, etc are verified and then added to the Hadoop system
  • To ensure the data match you should compare source data with the data added to the system
  • Make sure that the only required and right data is taken out and loaded into the particular HDFS location

Phase 2: Map-Reduce Validation

The second stage is the validation of “Map Reduce”. Business logic validation is performed by the tester on every node after which the authentication is done by running them against multiple nodes, to make sure that the:

  • The Map-Reduce process works perfectly.
  • The data aggregation rules are imposed
  • The key pair’s creation is present.

Phase 3: Output Validation Phase

The final or third stage involves the output validation process in big data testing. The output data files are created and they are ready to be moved to a Data Warehouse or any other such system as per requirements. This stage consisted of:

  • Verifying the transformation rules is accurately applied.
  • Ensure that data is loaded successfully and the integrity of data is maintained in the target system
  • Comparing the target data with the HDFS file system data, it is checked that Data Integrity is maintained to ensure that there is no data corrupt in the system basic high-level example of the Big Data ETL Process Validation 
Output Validation Phase

Big Data Performance Testing

When it comes to Big Data Testing one should never ignore performance testing as it is the most important big data testing technique because it ensures that the components involved provide efficient storage, processing, and retrieval capabilities for huge data sets.

This helps to obtain different metrics like response time, data processing capacity, and speed of data consumption. 

Performance Test Strategy

Performance testing for big data applications involves testing large volumes of structured and unstructured data and it also requires a well-defined testing approach

Performance Test Strategy

Performance Tuning Of Hadoop Component

For optimal performance, it's very important to tune the components of the Hadoop system. Hadoop components work in a collaborative fashion to store and process the data.

Tuning is required because it has huge and diverse data involved which needs to be handled differently. All components should be optimized and monitored for better performance of the Hadoop ecosystem.

Performance Tuning Of Hadoop Component

Challenges in Big Data Testing

Big data testing is a complex process that can be difficult to manage and time consuming. There are a number of challenges that testers face when trying to ensure the accuracy and quality of big data. You can read three major challenges.

Automation Testing is Essential

Testing Big Data manually is no longer preferred as it involves large data sets that need high processing resources that take a lot of time than regular testing. Hence the best way is to have automated test scripts to detect any flaws in the process. The automated test scripts can be written by expert-level programmers only.

Higher Technical Expertise

Dealing with big data testing doesn’t include just testers, it also involves various technical expertise like developers and project managers. The team involved in this system should be well proficient in using big data frameworks

Cost Challenges

For many businesses, big data specialists may cost more for the effective and continuous development, integration, and testing of big data. 

Big Data Testing: Best Practices

To overcome the various challenges while doing Big Data Testing, Testing professionals must take a step ahead to understand and analyze challenges in real-time. Testers must be capable of handling data structure layouts,  data loads, and processes. 

  • Testers must not use sampling approaches. It may sound easy to do but involves risk. It’s better always to plan load coverage and consider the deployment of automation tools to access data across various layers.
  • Testers must work to derive patterns and learning mechanisms from drill-down charts and data aggregations.
  • It's an added advantage if a tester has good experience in programming languages, it will help in map-reduce process validation.
  • Ensure the system always meets the latest requirements. This raises the need for continuous collaboration and discussions with stakeholders

Get Started With Fission Labs Big Data Testing & Consulting Services

Fission Labs offers consultation help for streamlining and carrying out Big Data testing. With our team of experienced QA personnel that specializes in Big Data testing, we make sure that your big data system is streamlined and error-free. To get started on your big data testing consultation all you need to do is book a free session with our experts today.  

Content Credit: Ameer Shaik

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