I had a very ambitious learning plan for my break between jobs and you can read that post. However, due to specific changes in priorities, the things I have learned have changed and I no longer followed the same learning plan. A detailed post on each of the lessons will follow in the future.
Rust Language:
Rust Lang Image from https://github.com/rust-lang/rust/issues/11562
The lessons from Rust Lang are very lean. Ideally, I would have liked to learn and implement something. But I did have the time to learn about the language in much more detail and these are the main concepts that I understood in much more detail.
- Memory Model and ownership
- Collections, packages, crates and modules
- Generic types, traits and lifetimes
- Tests
- Writing a simple HTTP server based on Zero to production rust book
Cloud Computing:
This is the module on which I made the maximum progress. I started studying for the AWS solutions architect certification and this introduced me into details of various AWS services that I can use out of the box. The main services that I feel are the most important lessons are
- S3 services
- EC2 service
- API gateway
- Lambda function
- Security and IAM
I also made some progress in learning in detail about docker. The exploration into docker is coming soon as a technical post.
Databases:
Photo by Krzysztof Kowalik on Unsplash
I learned a little about the NoSQL databases especially MongoDB from their open university courses and completed the basics certification. The most important part in handling data for web applications is the data modelling part which is the next course that I have started at MongoDB university. Hopefully I can slowly expand on the basics that I have learnt over this small period of time.
Read a Technical Book:
This challenge I have completed to perfection. I read the Informed company which is a very excellent book for people working at organisations who want to sort out their data problems from the point of view of Data engineering. The book gives a good overview of the 4 stages of data orgs and what must be done in each stage. It is an essential read for anyone that plans to deal with data (data scientists, data engineers, product owners, architects etc). I got this recommendation from the MLops Community podcast.
If you would like to know about the learning resources and detail posts on each topic please consider commenting and giving a follow.