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Every new business and company is looking forward to big data processing and big data performance testing to catch up with the huge data that the web creates each day. It might seem that big data is a challenging and complicated thing, however, with the right tools and strategies you can allow management of the entire data smoothly.

When it comes to information, the current buzzword and analysis trend these days is definitely big data. The advent of the World Wide Web meant that we are constantly being bombarded with a lot of data, which is something that requires immediate processing in order to gain a better understanding of it. Enter big data, which is extremely helpful in handling huge amounts of data that need managing. More and more businesses these days are looking at big data as a way of streamlining the complexities that go with data processing, and have seen its great returns when it comes to being a tool for growth and expansion. Therein comes the importance of discussion about the details strategy for the big data performance testing.


These days, the opportunities and growth challenges that come with data process engineering are three-fold: aggregating the volume (upping the amount of data), increasing the speed of data that comes in and out (velocity), and amassing the variety or data types as well as sources. We can call this the 3V model for volume, velocity, and variety.

Big data performance testing touches on how well the system performs in order to churn out data that is useful to the business, and not just managing the integrity and complexities of data itself. Much of one’s investment should be applied on framework performance engineering, failover, and data rendition.

Strategies Necessary for Performance Testing

A word: It is important to conduct architectural testing before anything else because systems that are inadequate or poorly designed have a high probability of resulting in performance degradation. As such, these are the three strategies which are also the basic Big Data systems, that one must implement when it comes to performance testing of the big data systems.

  1. Data ingestion. This is a process wherein data is absorbed or ingested in the system, both for immediate use or just for storage. The focus here is on routing the files to their right destination in a designated time frame, and not the validation of the files themselves.
  2. Data processing. When data is gathered from its many sources, it will need to undergo processing or mapping in a particular framework. Performance engineers are able to do this in batches as a way of handling the sheer amount of volume, and the main focus on this aspect is both the reliability and the scalability of the entire system.
  3. Data persistence. The focus this time is placed on the data structure, which will require constancy and adaptability for a number of storage options. Take note though that this is also irrespective of the different options for data storage (which includes data marts, management systems for relational storage, data warehouse, and more).
  4. Reporting and analytics. This process allows for the examination of large data sets that have a number of data types as a way of uncovering hidden patterns, correlations, customer performances, emerging market trends, and any other type of information that is useful to one’s business. This time, the focus is placed on applying just the right algorithms and making reports on only the useful information inside SLAs.


The Way to Approach Performance Testing

Because the data is highly complex (dealing with large volumes of unstructured and structured data), one should be mindful of applying these things to testing:

  1. Keeping in mind speeds of data consumption (which is data insertion rate)
  2. Ensuring the speeds of processing queries while that particular data is read (which is data retrieval rate).

Since the system is made up of multiple components, it is important to conduct testing in isolation and starting out at component levels prior to testing them all at the same time. Thus, performance testers must be well-versed when it comes to their knowledge of framework and technology in big data. This will also entail the use and application of market tools such as:

  1. Yahoo Cloud Serving Benchmark, or YCSB – which is a client for cloud service testing that can read, write, and update depending on specific workloads
  2. Sandstorm – a tool for automated performance testing in order to support testing for big data performance
  3. Apache Jmeter – which provides the necessary plug-ins for testing out databases

It may seem like big data performance testing is challenging, but having the right tools, strategies and skill sets will definitely allow you to manage everything smoothly!