When leading products you will find yourself balancing the scarcity of time, budget, and capacity against a probably too large and growing list of potential features.
Leading a team, it is always an exciting and challenging experience. Add to the mix that the team is sitting in different time zones, and you find yourself up to quite a ride.
In the past I had the opportunity to use some of the NLP capabilities available in Amazon Comprehend. This time I wanted to give it a shoot at Google Cloud Platform and try some of the features offered in Google Natural Language.
Should you go after ActiveMQ, RabbitMQ or similar? What about Apache Kafka, Amazon Kinesis, etc.…? Your choice, as all of them, will have consequences so you want to be as sure as possible before committing.
Software engineers are a special breed. They are architects and builders of the digital world which it is the center of the modern world reality. They not only tend to be smart but have immense passion for what they do and strong opinions about how they do it.
Graph databases excel that will benefit of treating relationships are first class citizens and handling complex data structures. Still, it is hard to ignore the maturity, reliability and talent availability for traditional RDBMS which make the decision harder to make.
In traditional environments products evolve out of a multitude of projects. Projects have teams organized with clear hierarchies that create distance between members responsibilities:
- The product manager gathers requirements from stakeholders.
It is easy to misuse what the minimum viable product is for. Some may use the term to ask developers to go string from the idea to coding.
Engineers skills evolve over time and can be taught. But what makes good engineers special is how they approach the work and react to what motivates them.