DATABERG
Unless you’ve been living under a rock, you’ve heard the term “big data” or “big data testing” tossed around by now.
But this term is more than just a catchy marketing slogan for consultants and marketers. The fact is that nearly every big corporation that can is investing heavily in big data. But small and medium enterprises (SMEs) have plenty to gain as well from leveraging this technology. According to recent research, over half of all small and medium businesses are using some kind of big data technology.
With all this data being mined, companies have to make decisions about how to best process and use this information in decision making. The technology being used to address this vary as database design becomes more innovative and elaborate. But whatever tech is employed, a meaningful big data campaign rests heavily on human knowledge. An understanding of data science and investigative mind are still critical tools in turning data into informed decision making.
Defining Big Data Testing
Big data testing is the method of somehow processing and making intelligent choices based on an extremely large volume of information, usually updated quite frequently. Though there isn’t a specific cut-off point where normal data becomes big data, we’re usually talking about data measured by tera or petabyte. When this data is based on transactions made on large commerce systems, for example, this data could be coming in all day every day at daunting speed.
This aspect, known as data velocity, creates the need for quick processing. Data often takes multiple forms, from easily structured spreadsheets to highly unstructured data like long text, images, or audio files. Finding a way to process disparate types of big data is surely a challenge, but it can be done with sophisticated methods. Below we’ll outline a few basic steps involved in making sense out of huge quantities of fast-moving data.
- - Check and validate all data. You’ll want to make sure that data is what you think it is, that it is accurate, and that it is being collected correctly from the original sources.
- - Once this is accomplished, you should bring a big data tester in for the process validation stage. In this stage, the test should ensure that business logic is sound in each node.
- - The final stage is called output validation. In this stage, the data that has been collected is loaded into what is called the downstream system. This can be a simply database. This data is now finally analyzed and further processed. At this stage, the data can be more closely checked for any issues.
Complex types of big data testing
Once data is validated and cataloged, there are various approaches to drilling deeper into this information. Each of these methods aims to maximize the value of big data, ensuring that data is clean, high-speed, and actionable.
1. Architecture testing
Big data testing is extremely resource intensive. Therefore, it is essential to carry out architectural testing to ensure the highest possible performance. If the system is poorly designed or inefficient performance can seriously degrade, compromising the success of a big data project.
In order to measure the performance of your big data architecture, you should assess completion time, memory usage, data throughput, and other system metrics associated with efficiency. Known as a failover test service, these procedures will ensure that data travels through the system as smoothly as possible.
2. Performance Testing
Performance testing measures how efficiently your data analysis platform can capture, process, and log data. There are three key types of big data performance testing, described below:
- - Data Ingestion and throughput. This testing procedure is applicable to databases, files, and real-time records. In the case of file-based data, high priority should be attributed to variety and velocity – especially when dealing with a large volume of records. The tester will verify how the fast system can process data from various data sources and identify how many different types of data the system can process in a given time frame. This process also assesses how rapidly data can be inserted into a fundamental data store.
- - Data processing. In this testing procedure, speed is verified by testing queries and algorithms. This tests data processing in isolation when the underlying data store is occupied. Through this procedure, the tester can ensure the system can handle multiple tasks.
- - Sub-component performance. Multiple components make up big data processing systems and it is vital to test each of these components in separation. For example, to ensure high system performance, a tester should assess how rapidly messages are indexed and consumed, how long MapReduce jobs take, query performance, search speed, and so forth.
So why is big data testing so important?
For one, it makes your data far more valuable. Good, sound data can be a transformational asset for any business. But the value really depends on how valid and accurate this information is. Because data can be quick, expansive, and growing by the day, accuracy becomes a very important concern. Much of this data needs to be structured and processed before use. This increases the importance of big data testing.
Ultimately, big data can help you increase revenues in multiple ways. Knowing your customers better helps you make custom recommendations and develop effective marketing campaigns. Having a robust knowledge of your operations can help you increase efficiencies or make processes more effective. With a robust big data strategy, certain decisions can be automated, freeing up employee and executive time. These are just some of the many dividends companies everywhere are enjoying from their big data strategy.
The ideal end game is to increase earnings and cut down on waste. For large public companies, increased earnings can be a major boon to investor satisfaction and share price. For private companies, earnings can help you grow and invest in your business and employees.
With a proper big data testing strategy, you can trust that your analysts are using accurate and valid data that can empower your company to make better, more profitable decisions, growing your business and bottom line.
