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Technology

Unlocking New Revenue Models in the Data Cloud

Today’s applications run on data. Customers value applications not only for the functionality they provide, but also for the data itself. It may sound obvious, but without data, apps would provide little to no value for customers. And the data contained in these applications can often provide value beyond what the app itself delivers. This begs the question: Could your customers be getting more value out of your application data?

Rebranding DevOps as Cloud Engineering

In this episode of Kongcast, Matt Stratton, a staff developer advocate at Pulumi, explains the history of configuration automation, the world of cloud engineering and how it compares to DevOps. Check out the transcript and video from our conversation below, and be sure to subscribe to get email alerts for the latest new episodes. Viktor: So before we jump to this one, tell us a bit about yourself. Matt: I spent about two decades working in traditional technology operations. I was a sysadmin.

How manual testing is done on mobile?

Manual testing isn’t going to fade away. Yes, automated tests help save time by running a large number of tests in a short time. Yet, certain tests are still best done manually as trying to automate them is difficult and not cost-effective. The discussion, therefore, shouldn't be about automated testing vs. manual testing. Instead, it should be about how to utilize and balance both in a way suited to your organization's size and development cycle demands to maximize return on investment.

Why is AWS Redshift Used? Integrate.io Has the Answer

Amazon uses a lot of adjectives to describe its cloud data warehouse: AWS Redshift is "fast," "simple" and "cost-effective." It's also popular. GE, McDonald's, Bosch, Coca Cola, and countless other brands, ranging from startups to Fortune 500 companies, have added Redshift to their tech stacks. But why is AWS Redshift used? And why is it the world's No.1 cloud data warehouse? Below, learn more about what Redshift does, how it does it, and why it could be a great fit for your organization.

Make Your AWS Data Lake Deliver with ChaosSearch (Webinar Highlights)

When CTO James Dixon coined the term “data lake” in 2011, he imagined a single storage repository where organizations could store both structured and unstructured data in their raw format until it was needed for analytics. But without the right storage technology, data governance, or analytical tools, the first data lakes quickly became “data swamps” - morasses of data with no organizational structure and no efficient way to access or extract meaningful insights.