Read the September issue of SD Times!
The September issue of SD Times is ready for your reading pleasure, and to help expand your knowledge of the industry! We hope by now you’ve read the digital issue on the new publishing platform we’re using to make the magazine a more interactive experience. Let us know what you think!
The cover story this month is on the STILL forthcoming release of Java 9. Promised since September 2016, the release is expected to deliver modularity among other new features. Contributing editor Lisa Morgan looks at the spec and highlights what development managers and their teams need to be ready for.
We know you’ll enjoy this month’s other news articles, features and analysis. Find it here!
---------------- Also In This Issue of SD Times ----------------
- Dev-OOPS! Why so many efforts fail: The notion of DevOps is simply to enable organizations to ship software frequently, reliably and of higher quality. Yet organizations embarked on a DevOps path often find frustration with their tools, hit a wall with testing, and uncover too much complexity in working with both new and legacy apps. We look at the issues, and tell you how to best avoid or overcome them.
- Girls in Tech: ‘We have a long way to go’: Sexism, harassment, bullying, bias and assault are, sadly, part of the technology culture when it comes to women. Lawsuits charging the above are springing up as women become more empowered. Online and social media editor Madison Moore caught up with Adriana Gascoigne, founder of the Girls in Tech advocacy group, to discuss the issues and what is being done to create less hostile work environments for girls in tech.
- The evolving state of middleware: Over the next three years, 76 percent of organizations plan to replace their existing middleware infrastructures with cloud functionality and DIY packages. Contributing editor Jacqueline Emigh looks at the state of middleware, and what the options are.
- Buyers Guide – Big Data Platforms: The only way enterprises can prepare for the future of Big Data is with a data science team capable of working with dirty data, complex problems and open-source languages. This guide offers a look at software providers that address these data issues.
There’s so much more, but you’ll have to open the issue here and see for yourself!