Here is the summary of the 2019 year from the technical perspective.

Also, you can check the previous 2018 year summary, and to compare the level of achievements comparing to the previous year, and how actual results for 2019 are different from planning at the beginning of the year. This actually where I think the most value of planning is - the retrospective of how things went, make lessons, adjust, plan and move forward.

Tool(s) of the year

• Project Jupyter

Simply due to being the new tool/concept to me during Machine Learning study. And it was difficult to come with another competitor to this category.

Language of the year

• Kotlin

Kotlin is still an interesting language and has a good vibe and a promising future. Although last year also actively worked with Go.

Conference of the year

• JFuture in Minsk

Being the only conference (and workshop) I attended last year, but very good quality. And also managed to win the 1st place of the JFuture Challenge.

Library of the year

• scikit-learn

Again in the scope of my Machine Learning study. Also, Jib was an interesting one.

Commitment of the year

Commitment started last year is still going strong (I will write a separate post about my findings on the way). Here below continuous graph of commits for every day of 2019.

The main focus of past year was on 2 major projects:

• dynoman
• dynocsv

All of them came out of the necessity, i.e. they were built to help me (and the team) to work on the day to day development activities on the main project.

Also, they got some attention outside of the company, which is great and nice! Practiced some promotions skills to achieve this. Dynoman and VMX are on the way to be promoted too in order to share them with a wider audience.

Plans for 2020

What I am sure I will continue to do is to work on my open source projects:

• I think will focus mainly on dynoman, it is already in the good usable shape, will focus on the distribution and promoting the tool in the various resources
• Consider adding support for Cassandra access from that very same tool

From the learning new things perspective:

• Q1 will continue to study Machine Learning.
• Q2 plan to focus/continue the study I started in December on Kubernetes and all relevant topics, like service mesh Istio and Linkerd. As well as strategies to deploy Kubernetes on AWS.
• Q3 will be devoted to the distributed systems, tools like Apache Flink and finally look more in depth on Kafka and Kafka Streams (although might be late to the party, but want to understand how these work nevertheless). As well dive into what's new on Apache Ignite, NATS and NATS Streaming. Also as part of this research would be interesting to look into gRPC and explore Vert.x from the high level concepts.
• Q4 will likely keep free, available to catch up or explore some new arising topics. Optionally explore and study The Art of PostgreSQL.

Additionally will be nice to look more in depth on Reactive Streams and The Reactive Manifesto, in particular reactive Spring 5.0 and Spring Boot 2.0 support, coroutines in Kotlin, reactive native Java 9+ support, ReactiveX, Project Reactor and RSocket.

Maybe these will be the exact activities for Q4.

So in the nutshell, the topics are ML, Kubernetes, Distributed Systems and Reactive Streams.

I will see how it goes.

Overall in 2020 I am planning to be more focused on just a few major activities, and spend less time on distractions and irrelevant things.