When it comes to solutions for the big data sector, there is a clear split between the legacy and next-generation approaches to software development. Legacy vendors in this space generally have their own large internal development organizations, dedicated to building proprietary, bespoke software. It’s an approach that has worked well over the years.

For quite a while now, cloud-native has been one of the hottest topics in software development. Some developers just call it hype that will lose traction and disappear after some time. For others, it’s the future of software development. Whatever the future will bring, cloud-native is currently one of the biggest trends in the software industry. It has already changed the way we think about developing, deploying and operating software products. But what exactly is “cloud native”?

Today, almost everyone has big data, machine learning and cloud at the top of their IT “to-do” list. The importance of these technologies can’t be overemphasized as all three are opening up innovation, uncovering opportunities and optimizing businesses. Machine learning isn’t a brand new concept, simple machine learning algorithms actually date back to the 1950s, though today it’s subject to large-scale data sets and applications.

Since the 1960s, scientists have been forecasting the weather using satellite-captured imagery. Although access to these satellite feeds used to be reserved just for meteorologists, these days anyone can jump online to find current satellite footage for their area. But what if you wanted to take things a step further? Maybe you’re curious about the history of weather events, or want to create a real-time feed for where you live.

DevOps is a set of practices that automates the processes between software development and IT teams so they can build, test, and release software more quickly and reliably. The concept of DevOps is founded on building a culture of collaboration between IT and business teams, which have historically functioned in relative siloes. The promised benefits include increased trust, faster software releases, and the ability to solve critical issues quickly.

If you’ve spent any time reading the round-up of 2018 technology predictions, you’ve likely seen Artificial Intelligence (AI) highlighted in nearly every one. The reason for this is because AI has a seemingly limitless number of applications and use cases for the enterprise. In fact, according to Gartner, over 85% of customer interactions will be managed without a human by 2020.

If you have an application that collects personal data with users in Europe, you’ll soon need to comply with a new European directive called the General Data Protection Regulation (GDPR).

We’re here to attest to the strength of the generalist (just in case Shakespeare hadn’t already proven himself). In fact, when we lost our push notification provider, we put two new providers head-to-head—one generalist versus one specialist. The result wasn’t so clear cut.

Bugfender is a tool that assists mobile developers debug their applications and provide better customer support, by gathering logs from devices using their apps. In essence, Bugfender acts as a really long USB cable that connects all mobile phones running your application back to your computer… even if your application was downloaded from the App Store or Google Play and you’ve never actually met the user in person. Handy, right?

For those who are familiar with my previous Talend blogs perhaps you’ve noticed that I like to talk about building better solutions through design patterns and best practices. My blogs also tend to be a bit long. Yet you read them; many thanks for that! This blog is going to focus on methodologies as they apply to Talend solutions.