My interaction with my girl:
"How was your day?"
"Fine."
"What's wrong?"
"Nothing."
My interaction with my AI:
Understands me better, remembers everything and never judges.
At this point, AI wins. ๐
What using AI has taught me:
Don't just generate code. Learn to design scalable systems, understand architecture, master DevOps & CI/CD, and ship complete end-to-end products.
Use AI to engineer better, not think less.
@SakshiML_20 Same problem I was facing earlier till I improved my communication skills and the ability to show what I learnt and what impact my projects created . Some real stuff i made
No DSA. No vibe coding. Just building real products, shipping freelance projects, and solving real-world problems.
5 internships. 3rd year. 5th at a Big 4 โ Deloitte.
Just getting started. ๐
Woke up to 5+ login attempts from ๐บ๐ธ ๐ฎ๐ฉ ๐ต๐ฐ
Microsoft: โUnusual activity detected.โ
Me: yeah bro, I noticed ๐
Somehow my account had a more active social life than me ๐ญ
Took a small break from Data Engineering today and learned something different โ L4 , L7 Load Balancing โก
Understood how a load balancer distributes TCP traffic across multiple backend servers without looking at the request content.
Day 5/30 ๐
Today I learned about groupByKey() vs reduceByKey() in Apache Spark โก
Understanding shuffling and why reduceByKey() is more efficient made the concept much clearer.
#DataEngineering#ApacheSpark
Today I practiced some RDD operations in Apache Spark โก
Worked with map(), filter(), first() and collect() to transform and extract data.
Learning by coding and understanding how Spark handles data step by step. ๐
#DataEngineering#ApacheSpark#LearningInPublic
Day 3/30 ๐
Today I learned how code gets executed in Apache Spark
Understanding transformations, actions, jobs, stages, and tasks โ and how Spark actually processes the code behind the scenes.
One more step toward understanding Spark better. ๐
#DataEngineering#ApacheSpark
Day 2/30 ๐
Today I started learning Apache Spark โก
Learned how Spark differs from traditional Data Processing and the limitations of MapReduce.
Slowly building the fundamentals, one day at a time. ๐
#DataEngineering#ApacheSpark#LearningInPublic