Tag: big data testing

Big Data Testing : What You Need To Know

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When data mining and handling techniques are not useful and are unable to expose the insights of large data, unstructured data or time-sensitive data, another aspect is used which is new to the realm of software industries.
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This approach is known as big data that uses intense parallelism. Big data is embraced by many companies that involve in-depth testing procedures.
What is Big Data?
Big data can be referred to as a huge volume of structured and unstructured data that are accumulating in businesses on a daily basis.
It cannot be easily processed using the same old methods of extracting information because most of the data is unstructured.
Using various tools and testing frameworks, big data can be analyzed for insights and helps businesses to make better business strategies and decisions.
Big Data Testing
Big data testing can be referred to as the successful verification of processed data by simply using commodity cluster computing and other essential components.
It is the verification of processing data rather than testing the specification of any application.
Testing of big data requires a set of high testing skills as the processing can be very fast and it mainly depends on two valuable keys for testing, i.e. performance and functional testing.
Essential Necessities In Big Data Testing
Big data testing needs some aspects to run tests smoothly. Thus, below is the list of following needs and challenges which makes it vital for big data applications to run their tests smoothly.

  • Multiple Sources for Information: For the business to have a considerable amount of clean and reliable data, the data should be integrated from multiple sources. With the help of multiple sources of information from different data, it has become easier to integrate this information. This can only be ensured if the testing of the integrators and data sources is done through end-to-end testing.
  • Rapid collection of Data and its Deployment: Data should be collected and deployed simultaneously to push the business’s capabilities to adopt instant data collection solutions. Also, with the help of predictive analytics and the support of taking quick decisive actions, it has brought a significant impact on business by embracing these solutions of large data sets.
  • Real-Time Scalability Challenges: Hardcore big data testing involves smarter data sampling, skills, and techniques that can perform various testing scenarios with high efficiency. Big data applications are built in such a way that it can be changed and used in a wide range of capabilities. Any errors in the elements which produce big data applications can lead to difficult situations.

Testing of Big Data Applications
Testing of big data applications can be further described in the following steps:
1. Data Staging Validation: The first stage also referred to as a Pre-Hadoop stage which involves the process of big data testing.

Also Read : Top 25 Software Testing Companies to Look Out For in 2018

  • Data should be first verified from different sources such as RDBMS, social media posts, blogs. It will ensure that only correct data is extracted into the Hadoop system.
  • The data which is received in the Hadoop system should be compared with data of different sources to ensure similar data is received.
  • Also, you need to verify that only the correct data that is received should be supplied to the HDFS location in Hadoop.

2. Map Reduce Validation: the Second stage comprises the verification and validation of Map Reduce. Usually, testers perform tests on business logic on every single node and run them on every different node for validation. These tests are run to ensure:

  • Valuable key pair’s creation is present.
  • Validation of data is done after the completion of the second
  • The process of map reducing is working properly.
  • Data aggregation or data segregation are implemented effectively on the data.

3. Output Validation Phase: This is the third and final stage of big data testing. After successful completion of stage two, the output data files are produced which is then ready to be moved to the location as per the requirement of the business. Initially, this stage includes processes such as:

  • Checking and verifying the transformation rules are applied accurately or not.
  • It verifies and checks whether the data loaded into the enterprise’s system is loaded successfully or not. Also, it verifies the integrity of the data during the loading procedure is maintained.
  • The last process would be verifying and checking the data which is loaded in the enterprise’s system is similar to the data present in the HDFS file system in Hadoop. Also, it ensures that there is no corrupt data in the system.

Challenges in Big Data Testing
 Following are the challenges faced in big data testing:

  • Automation Testing is Essential: Since the big data involves large data sets that need high processing power that takes more time than regular testing, testing it manually is no longer an option. Thus, it requires automated test scripts to detect any flaws in the process. It can only be written by programmers that mean middle-level testers or black box tester needs to scale up their skills to do big data testing.
  • Higher Technical Expertise: Dealing with big data doesn’t include only testers but it involves various technical expertise such as developers and project managers. The team involved in this system should be proficient in using big data frameworks such as Hadoop.

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  • Complexity and Integration Problems: As big data is collected from various sources it is not always compatible, coordinated or may not have similar formats as enterprise applications. For a proper functioning system, the information should be available in the expected time and the input/output data flow should also be free to run.
  • Cost Challenges: For a consistent development, integration and testing of big data require For business’s many big data specialists may cost more. Many businesses use a pay-as-you-use solution in order to come up with a cost-saving solution. Also, don’t forget to inquire about the testing procedure, most of the processes should include automation tests otherwise it will be taking weeks of manual testing.

Software Testing: What Future Holds?

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We wonder why it took us so long to write on this topic, maybe we wanted some time to let our theories brew.  As the year’s progress, software testing industry is seeing greener pastures. This rapid development in the industry has kept everyone on a hook, especially the testers, expecting them to continuously upgrade their skills.

Software testing plays an important role In the Software Development life cycle (SDLC) which helps improve the quality and performance of the systems. With the growing importance, many big software companies tend to start their testing activities right from the start of the development activities.

Many experts believe that by 2020, software testing will not just be limited to delivering the software without bugs, but will be a huge focus and demand for high-quality products. That’s because software testing is rapidly becoming a standard, rather than a more advanced approach for software development teams.

Below we list some of the top trends in this field for an exception 2018 experience for your tests.

1. Open Source Tools

Most of the software companies use and accept the open source tools to meet their testing requirements.  There are several tools available in the market today, but we can see advanced versions of it ready to be used soon in the near future.  Also, many of the tools like Selenium will jump in the world of AI (Artificial Intelligence) automating most of your testing needs.

2. BigData Testing

Companies today are sitting on top of a huge data repository and all these needs a very strong strategy around the BigData testing. Though BigData testing is difficult than any other testing, the advantages it offers cannot be ignored. The industry has faced many challenges- lack of resources, time and tools, but it has also found its way out of these challenges.

Also Read : All You Need To Know About Software Performance Testing

3. Performance Engineering

The success of software depends upon the performance, reliability, and scalability of the system with user experience as a prime factor. Any software system is incomplete without an interactive user interface. Increased demand for user experience shifts the focus of performance testing to performance engineering.

4. DevOps Integration

DevOps is a concept where the various teams/departments of an IT Organization work seamlessly in collaboration and integration for a project. Since testing plays a very crucial role in SDLC, they are a key person in the business and the overall quality engineering aspects. DevOps is, therefore, a propelling business towards the deployment speed.

5. SDET Professionals

SDET stands for Software Development Engineer in Test (or Software Design Engineer in Test). The concept was proposed by Microsoft and many organizations demand these professionals. The roles of SDET professionals are different from those of our regular testers.  It is said that by 2020, almost all the testers will have to wear the SDET hat to enhance their skills in the testing industry.

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Conclusion:

With the growing needs and changing requirements, software testing professionals need to improve their skills simultaneously. It is not only a challenge for the testing team, but also for the entire development team for addressing the advancements and technological updated. But we are sure the testing industry will knock down these challenges too with their innovations and research.

Strategy and Methodology of Big Data Testing

Posted on by tbsupadminnxtbackendacc

With advancement in technology and the new development taking place on a regular basis, there is a large pool of data that is also being generated. This, in specific terminology, is known as big data. Big data is a large pool of information which is made up of large data sets that cannot be processed using traditional methods of computing. This is because traditional methods work effectively on structured data stored in rows and columns and not for the ones that do not follow any specific structure.
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Big data can be available in varied formats such as images or audio. This data varies in its structure as well as format for every record tested and is typically characterized by volume, velocity and variety.

  • Volume: Available in large amount, big data is generally available from different sources
  • Velocity: Generated at high speed, this data is to be processed and handled quickly
  • Variety: Big data can be available in various formats such as audio, video, email, etc

Big data testing
Availability of big data is leading to a demand of big data testing tools, techniques and frameworks. This is because increased data leads to an increased risk of errors and thus, might deteriorate the performance of applications and software.
When conducting big data testing, a tester’s goal is completely different. Testing of big data aims at verifying whether the data is complete, ensure an accurate data transformation, ensuring high data quality as well as automating the regression testing.
Strategy and methodology of big data testing
Big data testing is typically related to various types of testing such as database testing, infrastructure testing, performance testing and functional testing. Therefore, it is important to have a clear test strategy that enables an easy execution of big data testing.
When executing big data testing, it is important to understand that the concept is more about testing the processing of terabytes of data that involves the use of commodity cluster and other supportive components.
Big data testing can be typically divided into three major steps that include:

  1. Data staging validation

Also known as pre-hadoop stage, the process of big data testing begins with process validation that helps in ensuring that the correct data is pushed into the Hadoop Distributed File System (HDFS). The data for which validation testing is done is taken from various sources such as RDBMS, weblogs and social media. This data is, then, compared with the data used in the hadoop process in order to verify that the two match with each other.
Some of the common tools that can be used for this step are Talend and Datameer.

  1. “MapReduce” validation

MapReduce is the concept of programming that allows for immense scalability across hundreds of thousands of servers in a Hadoop cluster.
 During big data testing, MapReduce validation is counted as the second step in which a tester checks the validity of business logic on every node followed by the validation of the same after running against multiple nodes. This helps in ensuring that:

  • Map Reduce process is working flawlessly.
  • Data aggregation or segregation rules are correctly executed on the data.
  • Key value pairs are generated appropriately.
  • Data is validated after Map Reduce process.
  1. Output Validation

On successfully executing the first two steps, the final step of the process is output validation. This stage includes generating files that are ready to be moved to an Enterprise Data Warehouse (EDW) or any other system based on the specific requirements.
Output validation phase includes the following steps:

  • Validating that the transformation rules are correctly applied.
  • Validating the data integrity as well as successful loading of data into the target system.
  • Ensuring that there is no data corruption by comparing the target data with HDFS file system data.

Architectural & Performance testing
Big data testing involves testing of a large volume of data, which also makes it highly resource intensive. Therefore, to ensure higher accuracy and success of the project, it is important to conduct architectural testing.

It is important to remember that an improperly designed system may degrade software’s performance as well as does not allow it to specific requirements. This, in turn, generates a need of conducting performance and failover test services.
When performance testing is conducted on a system, it implies that it is being tested for aspects such as time taken to complete a job, utilization of memory and similar system metrics. On the other hand, the purpose behind conducting a Failover test is to verify that data processing takes place with a flaw in case of data nodes’ failure.
Conclusion
It is obvious that big data testing has a set of its own challenges such as need of technical expertise to conduct automation testing, timing issues in real time big data testing and need to automate the testing effort, it has numerous advantages over traditional database testing such as ability to check both structured and unstructured data.
But, a company should never rely on one single approach for testing its data. With an ability to conduct testing in multiple ways, it gets easier for the companies to deliver fast and quick results