Systems | Development | Analytics | API | Testing

In an ideal world where we reached 100% test coverage, our error handling was flawless, and all our failures were handled gracefully — in a world where all our systems reached perfection, we wouldn’t be having this discussion. Yet, here we are. Earth, 2020. By the time you read this sentence, somebody’s server failed in production. A moment of silence for the processes we lost.

Data scientists today have to choose between a massive toolbox where every item has its pros and cons. We love the simplicity of Python tools like pandas and Scikit-learn, the operation-readiness of Kubernetes, and the scalability of Spark and Hadoop, so we just use all of them. What happens? Data scientists explore data using pandas, then data engineers use Spark to recode the same logic to scale or with live streams or operational databases.

Effectively bringing machine learning to production is one of the biggest challenges that data science teams today struggle with. As organizations embark on machine learning initiatives to derive value from their data and become more “AI-driven” or “data-driven”, it’s essential to find a faster and simpler way to productionize machine learning projects so that they can make business impact faster.

Flutter blurs the lines between designer and developer and endorses a new designer developer archetype. Part designer part engineer, part Picasso and part Pascal. With ambitious designs comes the responsibility to make those designs run on the screen without losing frames. While Flutter is performant by design, how much should we really pay attention to performance optimisation? In most cases … we don’t.

Apache Kafka has gained traction as one of the most widely adopted technologies for building streaming applications - but introducing it (and scaling it) into your business can be a struggle. The problem isn’t with Kafka itself so much as the different components you need to learn and different tools required to operate it. For those motivated enough, you can invest money, effort and long Friday nights into learning, fixing and streamlining Kafka - and you’ll get there.