When you secure a new data engineering position, it’s important to get off on the right foot. You need to respond to new data sources, data types, data sets, and applications efficiently. In the era of Big Data, this can be more than challenging. During this early stage of your job transition, you need to impress, and you can do this by re-thinking your data engineering strategy.

Whether you’ve made your first steps toward digital transformation or looking for ways to expand on the your current tech stack, you probably have at least one API in use. You know by now that APIs are more than pieces of code: they are products that can expand your business opportunities. They can facilitate your business’ growth with new capabilities and new revenue streams. But building and deploying APIs is only the beginning of the API lifecycle.

If you’re planning to upgrade your business technology, this almost certainly means working with an API. Either you’re building your own to integrate your on-premises technology, importing a third-party API for added functionalities, or monetizing your current capabilities. Regardless, you may find yourself working with a lot of APIs in the near future. But you may not know what to look for in searching for a high-quality API.

DreamFactory specialises in REST API generation for any number of database types however an underrepresented benefit is the ability to mount and manage any third party API via a HTTP service connector such as a Typeform REST API. This gives you the ability to generate documentation and secure an existing API as if it were created natively within DreamFactory.

Since the release of Cloudera Data Visualization (DV) back in Oct 2020, our primary mission has been to expand access to data analytics and predictive insights across enterprise businesses.

Recommendation systems have become a cornerstone of modern life, spanning sectors that include online retail, music and video streaming, and even content publishing. These systems help us navigate the sheer volume of content on the internet, allowing us to discover what’s interesting or important to us. The classic modeling approaches to recommendation systems can be broadly categorized as content-based, as collaborative filtering-based, or as hybrid approaches that combine aspects of the two.

Data and analytics have become second nature to most businesses, but merely having access to the vast volumes of data from these devices will no longer suffice. Leading enterprises realize that the speed of data presents a new frontier for competitive differentiation. It is imperative for organizations to reduce time-to-insights to gain a competitive advantage by responding decisively to competitors, fine-tuning operations, and serving fickle customers.

Companies have had only mixed results in their decades-long quest to make better decisions by harnessing enterprise data. But as a new generation of technologies make it easier than ever to unlock the value of business information, change is coming. We’ve already reaped gains at Hitachi Vantara, where I run a global IT team that supports 11,000 employees and helps more than 10,000 customers rapidly scale digital businesses.

I’m starting to hear questions like: “What comes next?” “Do things go back to the way they were?” “Are some of the changes wrought by the pandemic here to stay?” I think we all know part of the answer: there is no going (all the way) back. In the analog world, sure, we need some things to revert to bounce back. We need to revitalize retail, tourism and hospitality to get our economies moving again.

BDQ is a Zephyr Expert Partner and Atlassian Solution Partner based in London, England. Our core focus is ensuring that the Atlassian technology stack and Zephyr agile test management products deliver value to our customers through digital transformation. We provide consultancy, training, and workshops on implementing Zephyr Squad (previously known as Zephyr for Jira). Our goal is to empower teams to test effectively in agile scrum.