As the world becomes increasingly interconnected and digitized, big data continues to grow as one of the most critical components in organizations both big and small. In fact, according to recent studies, enterprise data is set to grow by more than 600 percent over the next five years, and the vast majority of Fortune 500 Companies are already using Big Data development as a key element within their respective competitive advantages.
At the same time, the management and development approach necessary to learn about and incorporate Big Data into business is still being fleshed out. Processing the vast data in a meaningful way requires a new approach altogether for many companies.
Big Data is different than traditional data sets, and it requires a substantially distinct method of testing. As the amount of data grows and becomes more complex, big data testing becomes critical for making use of an otherwise overwhelming amount of data.
At the same time, testing big data requires an IT team that understands the ever-changing nuances and complexities in a way that is directly applicable to your organization. Successful big data testing will result in dramatically improved efficiency and returns on investment of said data.
In order to fully understand why Big Data testing is so important, it’s worthwhile to break down the key objectives. The following are five of the key objectives that are looked at when testing big data.
Data can come from a variety of sources, and having a strategy to accumulate and consolidate that data is an important first step. Sources like blogs, social media, internal programs, systems and databases need to be vetted and consolidated.
Architecture testing is another essential component of big data testing. Because Big Data architecture contains so many moving parts, each object within the architecture must be verified as a legitimate and integral part of the system.
As the name suggests, Big Data consists of a large number of data. However, not all of that data is actually important to an organization in all cases. That’s why eliminating unnecessary data points is a critical step in the process of creating an effective big data application.
Examples of unnecessary data can be duplicate or redundant data sets, corrupted or unreliable data, and data that does not directly correlate with an organization’s particular strategy or objectives.
Understanding how data has performed in the past, and how it is likely to perform moving forward, is another key element of effective big data testing. This includes verifying the ability of a big data application to accumulate data from a data source, verifying how well the data can be effectively processed, and verifying how efficiently data is stored and cached within the program.
Many times before data gets to the target system, there are multiple transformations, aggregation and calculations performed prior to final transformation. During the testing it is critical to test source to target transformation , i.e. to be able to take a raw data from a source file perform all necessary aggregations/calculations manually and be able to verify that the results that one have gotten using manual data manipulation will match the result during the system transformation from source to target. If you did find an issue, it is critical to isolate it to the specific module where the culprit occurs.Â
In order to effectively achieve the key objectives described above, as well as any other objectives an organization requires, there are several best practices â€“ all of which are performed routinely by SQA Solution â€“ that ensure the most reliable and efficient results.
When dealing with a wide range of distinct components, having a dedicated test environment can help prevent core data from being corrupted by the testing process.
With older databases, it was sometimes tenable to use a “one size fits all” solution for data testing. However, with big data there are so many variables that creating a specialized validation tool is essential.
When testing big data systems and applications, there are a few primary components that must be a part of any comprehensive testing process.
The first component of any comprehensive test is the verification and uploading of data from all of the various sources to an HDFS. That data is then vetted for corruption and partitioned into separate data units.
Once the data is uploaded and partitioned, the next key component is to consolidate the data and eliminate redundancies. This ensures that the data is processed efficiently and accurately.
Once all of the data has been vetted and verified, it can be uploaded into a downstream system, which in turn can be used for the generation of reports and other key insights.
With a growing number of companies utilizing Big Data as one of their core competitive advantages, anything less than expert-level execution can be the difference between success and failure.
The team at SQA Solution goes through extensive big data testing training to ensure that they are fully-equipped on all of the best practices needed to properly test big data applications.
This high level of training is particularly important because the number of records, as well as the complexity of those records, is only getting more complex with each passing year.
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