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Mobile app automation testing Tools: An exhaustive list

It’s 2021 and we now have a mobile app for everything. Whether it’s social media, shopping, productivity or fitness, there’s a visible surge in the number of mobile apps and their users. Simform (as of 2019) stated that an average user has at least 40 apps installed on their phones while millennials have around 67 apps. So, there’s a solid reason why mobile app testing is a crucial step in any app development process.

Mobile Web Testing: Testsigma vs Appium

With the increased usage of smartphones in recent years, enterprises and the software industry now have to cater their applications to mobile devices for web testing in addition to desktops. Usually, there are three types of applications that are meant to be used in mobile phones. These are – i. Native Apps: Apps that are written and built using SDKs and native APIs. These can be downloaded from the official app stores. ii.

Software Engineering Daily Podcast

Large portions of software development budgets are dedicated for testing code. A new component may take weeks to thoroughly test, and even then mistakes happen. If you consider software defects as security issues then the concern goes well beyond an application temporarily crashing. Although even minor bugs can cost companies a lot of time to locate the bug, resolve it, retest it in lower environments, then deploy it back to production.

HDFS Data Encryption at Rest on Cloudera Data Platform

Encryption of Data at Rest is a highly desirable or sometimes mandatory requirement for data platforms in a range of industry verticals including HealthCare, Financial & Government organizations. The capability increases security and protects sensitive data from various kinds of attack that could be internal or external to the platform.

AI/ML without DataOps is just a pipe dream!

Let’s start with a real-world example from one of my past machine learning (ML) projects: We were building a customer churn model. “We urgently need an additional feature related to sentiment analysis of the customer support calls.” Creating the data pipeline to extract this dataset took about 4 months! Preparing, building, and scaling the Spark MLlib code took about 1.5-2 months!