Introduction

You must be aware that big data is a huge setup and needs careful design and testing in order to ensure it works just as designed. A small miss can bring the whole system down to a grinding halt. It is therefore essential that before commissioning the big data system, the entire system is put through appropriate testing strategies to ensure the system will work as designed before moving to production. Let’s explore in this article the need for big data testing, the strategies that are applicable for big data testing, Hadoop testing, big data testing tools in the market and what are the open-source options when it comes to big data testing tools.

In this article let us look at:

  1. Need for Big Data Testing
  2. What is Big Data Testing?
  3. Big Data Testing Strategies
  4. Big Data Testing Approaches
  5. Tools
  6. Hadoop Testing Approach

1. Need for Big Data Testing

So why test Big Data infrastructure when it is giving you the results for the queries you raise against it. Well, there might be points or stages in the operation of the big data system that might not have been put to full use. In other words, the big data system you use might not have been put under enough pressure or stress to satisfactorily say that the system will reliably work through any condition.  Before commissioning the system, it has to be put through satisfactory levels of testing to ensure it does not break down in production. You also must ensure your system is fool proof against manual errors.

2. What is Big Data Testing?

Testing of Big Data may be defined as a procedure involving examination and validation of functionality of Big Data applications. Testing Tools that are a perfect fit for traditional tools will not be able to test applications at the scale of Big Data. Let’s look at some of the Big Data testing strategies that are employed in the industry.

3. Big Data Testing Strategies

There are three scenarios that a testing team looks at when testing Big Data applications, which are,

  • Batch Processing

These test cases involve testing of those procedures that run data through techniques that are based on batch processing, typically involving batch processing storage technologies like HDFS. This kind of testing involves, running the application against faulty inputs and also testing extremes of data volumes.

  • Real-time Processing

Real-time big data testing involves, testing the application in real-time processing mode, or testing those components in the application that deal with real-time data. Real-time processing typically involves Spark applications among other tools. The testing looks to check the stability of the application against the volume of real-time data and the speed at which the data is coming in.

  • Interactive processing

Interactive data processing tests involve testing of interactive applications where there is constant interaction with the user, like HiveSQL.

4. Big Data Testing approaches

There are a couple of approaches that you can take towards testing Big Data systems. One is architecture testing, the other is Performance testing.

  • Architecture Testing

Architecture testing takes the approach of testing the design of the Big Data system to ensure that the design is fail safe, before moving to production. A big data system designed poorly will lead to degraded performance compromising any benefit that was conceived.

  • Performance Testing

In performance testing, the big data system is tested on time efficiency, memory utilization, throughput among other important metrics.  Here are some parameters on which performance testing is based on.

  • Data Storage- How efficiently is data stored and retrieved, saving both time and space.
  • Commit logs- For how long can commit logs to be retained.
  • Concurrency- Maximum number of threads that can concurrently read and write to storage or memory.
  • Caching- How well is caching of data handled, and its granularity.
  • Timeouts- How long can queries run without interruption.
  • JVM Parameters- How efficiently does the system handle Garbage collection. What is the impact on processing?
  • Message Queue- the size of messages and the rate of messages etc.

5. Tools

Big data is a suite of tools that can together handle big data generated by a business. These tools themselves are used for testing them for expected results, accuracy, stability, and versatility.

There are several tools many of which are open-source,  that is used in each phase of the big data architecture.

  • Data Ingestion phase 

Zookeeper, Sqoop, Kafka

  • Data Processing

MapReduce, Pig, Hive

  • Data Storage

Amazon S3, HDFS

  • Data Migration

Talend, CloverDX, Kettle.

6. Hadoop testing approach

Big Data testing involves testing in stages due to the enormous size it assumes. Testing at various stages of the system might be a good idea to successfully test the entire system. Typically, Big Data testing involves the below stages of testing,

  • Data Ingestion testing

Data ingestion is the term used to refer to the process of inserting data into the Big Data system. Data here could be of any time, any volume or coming in at any speed. The collection of various tools to handle different types of data at different velocities are called big data ingestion tools Testing of ingestion tools will need to be targeted at testing volume, velocity, and variety of data.

  • Data Processing testing

Data processing is the stage where ingested data is processed for further analysis. Data Process testing will involve generating key-value pairs and then application of MapReduce logic to the processed data to check if the algorithm is working fine.

  • Validation of the output

At this stage, transformation logic is tested. Data integrity verification and accuracy of key-value pairs are checked for.

Conclusion

Big Data Testing is a challenging domain with many more aspects of testing. It this is interesting then you may want to explore our courses on Big Data testing to gain in-depth knowledge on this complex subject.

Big data analysts are at the vanguard of the journey towards an ever more data-centric world. Being powerful intellectual resources, companies are going the extra mile to hire and retain them. You too can come on board, and take this journey with our Big Data Specialization course.

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