Mark Priest

Java was my first professional programming language and I have been with it through many changes and additions over the years. I love the large open source community and the platform independence of code written for the JVM. I have used it primarily as a back-end language for enterprise applications and I developed my first open source project with Java which was inspired by my preference for the event-driven programming style.

I am a relative newcomer to Scala but I learned to embrace the functional style of programming with the introduction of Java Streams and lambda functions. I really enjoy the improved null safety and the first class support for pure functions and immutable data structures. Scala allows the programmer to avoid whole classes of common bugs that occur in other languages. In Scala you declaratively define what you want your program to do and let the language itself generate the error-prone and time consuming boilerplate-type code needed in imperative languages. The Akka framework is a very clever and well-designed platform to avoid many of the pitfalls of concurrent and parallel programming on the JVM. Akka streams is an excellent Reactive-compliant non-blocking stream processing engine that greatly simplifies building event-based applications and algorithms.

Python is a good cross-platform scripting language for Linux (and Windows) platforms. It is very useful as a "glue" language that can be used to bring together shell scripts as well as other executable programs with relatively few lines of code. The language also has great open source and testing support. In the last two years I have used numpy, pandas, matplotlib, and scipy for analyzing and understanding large data sets - especially on AWS Sagemaker. If you do any sort of number crunching or chart and graph making in tools like Excel you should seriously consider trying this ecosystem since it has very deep support for highly performant scientific computing techniques. Jupyter notebooks are a very good tool for both analyzing data and presenting results to a larger team in Python.

I have worked with Structured Query Language (SQL) for most of my career and despite regular predictions of its demise it is very much alive and well. It is essential for software engineers to have at least a basic idea of how to develop queries with joins and to efficiently write data to an RDBMS. I have spent several years performance tuning large queries - especially on the Oracle DBMS. I learned more about hints and tricks to work with the Oracle query optimizer than I hoped I would need to on several large projects. There are, however, key patterns and approaches to use with every RDBMS to allow efficient data access and good performance.

I have worked with many software development methodologies and frameworks over the years - especially Agile methods which seem to have taken over the field at the moment. Because I have worked in all phases of the software engineering lifecycle from requirements gathering, coding, testing, and delivery/integration I have a large breadth of knowledge in the field. I am an avid reader of IEEE periodicals and listener to the Software Engineering Radio Podcast managed by IEEE Software magazine. I have been an individual contributor, team lead, and manager on a large number of software projects and I am happy to share my experience and advice on how to grow your career in the field.