Software Testing process generates a lot of data – quantitative as well as qualitative – from the various types of testing done at the different stages of software product development. With Data Lake solutions provided by various cloud service providers, it is pretty easy to get your testing data lake going, but the quality and effectiveness of the testing data lake would depend on the interfaces that it provides and the methods to extract and structure the data as required by the stakeholders. In this blog, let us look at the key considerations of a testing data lake.
Software Testing efforts go along with the different facets of software design considerations. Each of these testing generates data, both structured and unstructured. These data from various testing might need to be put together in ways that can be useful for various types of stakeholders. The key considerations about designing a Test Data Lake are as follows:
Ingest Interfaces Design
A key consideration in ingesting data into the testing data lake are the software interfaces through which the data is ingested. While APIs are used extensively, customized protocols are also used. The data transferred need to be carefully designed for both structured and unstructured types.
Data Extraction and Usage
Same set or the combination of data from several sources might be needed for usage by different testing stakeholders. This is accomplished by a set of software data collectors which are carefully designed to assimilate, organize, and distribute the data to various stakeholders, mostly through analytics.
Data Purging
Data that is no more required for testing purposes need to be purged regularly to avoid unnecessary storage costs and irrelevant historical data skewing up the analytics.
Designing a Testing Data Lake is involved and needs to be carefully done. To chat about Test Data Lake, feel free to chat with me.