"At first, I felt lost. My professor gave us a Scala homework assignment that involved writing a data transformation program using a map and filter. I remember sitting in front of my laptop for hours trying to figure out what map even meant in this context. I typed things, deleted them, and then typed again. Nothing worked. That night I realized Scala was not going to be easy, but it was going to be worth it."
"I decided to learn Scala because I wanted to understand how big companies handled massive amounts of data. During one of our seminars, a guest engineer from a data analytics firm mentioned that their entire processing system ran on Scala using Apache Spark. That caught my attention immediately. I had always been fascinated by data and how it could tell stories, but I never knew the technology behind it."
Scala presented steep initial challenges because of unfamiliar syntax, error messages, and a functional programming paradigm that conflicted with prior Python and Java experience. A homework assignment involving map and filter required hours of trial and error before understanding how data transformation functions worked. Motivation to continue came from learning that major data systems and Apache Spark rely on Scala for large-scale processing. Scala combines Java-like structure with functional flexibility, promising fewer bugs, cleaner code, and high performance. Early small victories, such as successfully printing "Hello, Scala.", provided confidence to continue learning from zero toward mastering data-focused applications.
Read at Medium
Unable to calculate read time
Collection
[
|
...
]