So, if you’re a nose-to-the-keyboard developer, there’s ample probability that this analogy is outside your comfort zone … bear with me. Imagine two Olympics-level figure skaters working together on the ice, day in and day out, to develop and perfect a medal-winning performance. Each has his or her role, and they work in sync to merge their actions and fine-tune the results.
This is the second post in a series about data modeling and data governance in the cloud from Snowflake’s partners at erwin. See the first post here. As you move data from legacy systems to a cloud data platform, you need to ensure the quality and overall governance of that data. Until recently, data governance was primarily an IT role that involved cataloging data elements to support search and discovery.
In recent years, organizations have been making massive investments in data analytics to transform their growing volume of data into actionable insights to inform decision-making. However, the pursuit of becoming data-driven has uncovered challenges earlier in the data pipeline that are preventing companies from reaping all the benefits from their data.
One of the most important shifts of the past few years in finance and banking was the movement from primarily branch-based banks to mobile-first banks. While these innovative products simplify the end-user experience, they also bring up more security concerns, since digital channels expose a number of vulnerabilities. These apps deal with sensitive user data, such as private financial or personal information, which means that the prevention of any type of breach is of utmost importance.
Develop fast, release, learn, repeat. That’s essentially the (not-so-secret) innovation formula, right? Most of us spend our time enhancing the products we have already released. We want to be innovative, releasing new features with the velocity of an unencumbered startup. Yet, we also have customers with quality expectations we need to meet. Guidance on shortening release cycles often centers on adopting agile (or similar) development methodologies. But most companies are already there.
Since 2013 the UK Government’s flagship ‘Cloud First’ policy has been at the forefront of enabling departments to shed their legacy IT architecture in order to meaningfully embrace digital transformation. The policy outlines that the cloud (and specifically, public cloud) be the default position for any new services; unless it can be demonstrated that other alternatives offer better value for money.
Software vendors often ask me when is the ideal time to swap out a legacy analytics solution that they’ve embedded into their application. There's five key signs that can tell you your embedded solution isn’t right anymore and it’s time to switch.
In August 2020, Snowflake announced several new features, all in preview, that make its cloud data platform easier to use, more powerful for sharing data, and more usable via Snowflake-supported languages. These innovations mean you can bring more workloads, more users, and more data to Snowflake, helping your organization solve your most demanding analytics challenges. Multi-Cloud, Cross-Cloud, and Pattern-Matching Support in Snowpipe
QA professionals, testers, and developers are constantly learning new tools, tech stacks, and development practices. When they’re told they have to learn accessibility, it can often feel like an unwelcome and overwhelming disruption, slowing them down and forcing them to test and rewrite what they thought was perfectly good code. The good news is accessibility tools are more tester-friendly than ever.
