Explain Big Data testing?

To ensure that all the features of a big data application work as intended, Big Data Testing is a testing method for a big data application. Big data testing aims to ensure that, while retaining efficiency and security, the big data system runs smoothly and error-free.

Big data is a series of massive databases that conventional computing methods can not be used to process. The testing of these datasets requires different processing instruments, methods, and frameworks. Big data refers to the creation, storage, retrieval, and analysis of information, which is remarkable in terms of volume, variety, and speed,More info go through Big Data Hadoop Course Tutorials Blog

Strategy for Big Data Testing

Instead of testing the individual features of the software product, Big Data testing application involves more verification of its data processing. Performance and functional testing are the keys when it comes to big data testing.

The QA engineers process terabytes of data and verify using commodity clusters. Then other supporting components in Big Data testing. As processing is very fast, it requires a high level of testing skills. Moreover, there are three types of data processing.

Testing for Big Data: Functional & Performance

Data quality is also an important factor in Hadoop testing alongside this. It is necessary to check data quality before testing the application and should be considered as part of database testing. It involves checking different features, such as conformity, accuracy, duplication, consistency, validity, completeness of data, etc.

How to test Applications for Hadoop?

A high-level overview of the stages of testing big data applications is given in the points below.

Testing for Big Data: Functional & Performance

Big Data Testing can be divided broadly into three stages.

Step 1: Validation of Data Staging

Process validation is the first stage of big-data testing, also referred to as the pre-Hadoop stage.

To ensure that correct data is pulled into the system, data from various sources such as RDBMS, weblogs, social media, etc. should be validated.

Comparison of the source data with the information pushed into the Hadoop system to ensure that it matches.

Verify that the correct data is extracted and loaded into the correct location for HDFS.

For data staging validation, tools such as Talend, Datameer, can be used.

“Step 2: Validation” MapReduce

The validation of “MapReduce” is the second step. At this stage, the tester verifies the validation of business logic on each node and then validates it after running against multiple nodes, ensuring that the code validates.

Map Process Reduction works correctly.

The rules on data aggregation or segregation are enforced on data.

The generation of key-value pairs.

Validating the data after the process of Map-Reduce.

Step 3: Phase of Output Validation

The process of output validation is the final or third stage of large data testing. Based on the requirement, the output data files are generated and ready to be moved to an EDW (Enterprise Data Warehouse) or any other system.

Third-stage activities include

  • The transformation rules are correctly implemented to check
  • Checking the integrity of the data and the successful load of data into the target system.
  • Compare the target data with the HDFS file system data system to check that there is no data corruption.

Testing Architecture

Hadoop processes very large data volumes and is extremely resource-intensive. To ensure the success of your large Data project, architectural testing is therefore crucial. A poorly or improperly designed system may lead to degradation of performance, and the system may fail to meet the requirements. In a Hadoop environment, at least Performance and Failover test services should perform.

Performance testing involves job completion time testing, memory utilization, data throughput, and similar metrics of the system. While the purpose of the Failover test service is to verify that data processing happens seamlessly in the event of a data node failure.

Testing for Performance

Two key actions for Big Data performance testing are as follows.

Data intake and throughout:

The tester verifies in this stage how data from different data sources can be consumed by the fast system. Testing involves identifying a different message that in a given time frame can be processed by the queue. For example, the insertion rate in a Mongo and Cassandra database includes how quickly data can be inserted into the underlying data store.

Data Processing:

It involves verifying the speed at which jobs are executed by queries or map reduction. When the underlying data store is populated within the data sets, it also involves testing the data processing in isolation. For instance, on the underlying HDFSS, running Map-Reduce jobs

Sub-Component Performance:

These systems consist of multiple components and each of these components needs to be tested in isolation. For instance, how fast the message is indexed and consumed, MapReduce jobs, the performance of queries, search, etc.

Performance Approach Testing

Big data application performance testing involves testing huge volumes of structured and unstructured data, and a specific testing approach is required to test such massive data.

Testing for Big Data: Practical & Efficiency

In this order, Performance Testing carries out as follows.

The method starts with the setting of the Big Data cluster to be tested for performance.

Identify and design matching workloads

(Custom scripts are created) Prepare individual clients

Execute the test and analyze the result (tune the component and re-execute if targets are not met)

Configuring Optimum

The Performance Testing Parameters

For performance testing, various parameters to verify are as follows.

  • Storage of Data:

How data is stored in different nodes

  • Commit logs:

How large can the commit log grow?

  • Concurrency:

How many threads can operate in writing and reading

  • Caching:

You can tune row cache settings.

  • Timeouts:

Connection timeout, query timeout, etc. values.

  • JVM Parameters:

Heap size, algorithms for GC collection, etc.

  • Performance reduction map:

sort, merge, etc.

  • The queue for messages:

Message rate, size, etc.

Needs for Test Environments

The test environment must rely on the type of application that you are testing. The test environment for large data testing should include the test environment.

  • A large amount of data should have enough space for storage and processing.
  • A cluster with distributed nodes and data should be present.
  • To maintain high performance, it should have minimal CPU and memory use.
  • Large Data Research Issues

The automation

Big data automation research needs someone who has professional knowledge. Automated tools are often not designed to deal with unforeseen issues that occur during research.

Virtualization

It is one of the main stages of research. In real-time, large data testing, virtual machine latency causes timing issues. Managing images in this technology is also a concern.

Broad Dataset

You need to check more data and do so more quickly.

Need to automate the effort for testing

The need to be able to test through multiple platforms

Challenges with Performance Testing

  • Diverse technology set:

Each sub-component is part of various technologies and needs isolation testing.

  • Unavailability of specialized tools:

End-to-end testing can not be done by a single instrument. NoSQL would not work for message queues, for instance,

  • Test Scripting:

The creation of test scenarios and test cases involves a high degree of scripting.

  • Test environment:

Because of the huge data size, it requires a specific test environment.

  • Monitoring Solution:

Few solutions can monitor the whole environment.

  • Diagnostic Approach:
  • To drill down the output bottleneck areas, a personalized solution needs to create.

Conclusion

I hope you conclude testing using big data. You can learn more about big data testing from Big data online training.