BIG DATA impact on QA
I am trying to gather some insights and understanding of projects with BIG DATA implementations and its impact on QA approach, strategies and challenges. Can someone provide some pointers that can be explored in same direction.
Thanks in Adv.
On functional testing, there won't be much impact in case of Big Data application.
The major impact would be in terms of Database testing as Big Data is having no SQL.
Another impact will be on the load testing front where the scale of testing will be bigger in case of Big data application.
Hope it helps.
My answer is no additional impact to QA than what's already there.
Big Data has been around for a long time, we just called it Data Mining and Statistics before that term is coined.
Sure, there are new databases now, such as distributed token databases like Cassandara, or Object databases like Google Big Table and MongoDB, or compute clusters like Hadoop. You'll have to learn the tools and query methods around those.
But in terms of things people have already been doing on a high level, there's no change. You're still validating data integrity, you're still load testing, you're still performing functional testing, and you're still doing reliability and soak testing.
Can it be automated?
Originally Posted by dlai
Originally Posted by mrajvanshi
Big data does not effect more QA.As it already have and no more additional impacts.Impact can be seen in load testing.
There's no change. Regardless you're accepting information uprightness, despite everything you're burden trying, regardless you're performing practical testing, regardless you're doing dependability and drench testing.
Since we're on the topic, a funny slide explaining big data.