Ace @bITengineGuy

> Data Engineering Interview Questions Frequently Asked by Top Product Companies (Beginner → Advanced) 1. BEGINNER (10 Questions): Foundations + Data System Thinking → What is Data Engineering, how is it different from Data Science and Backend Engineering? → Explain ETL vs ELT, why modern data stacks prefer ELT for scalability. → What are structured, semi-structured, and unstructured data, how are they stored and processed? → Difference between batch processing and stream processing with real-world examples. → What is a data warehouse vs data lake, when would you use each? → Explain schemas: schema-on-write vs schema-on-read, impact on data pipelines. → What is data partitioning, how does it improve query performance? → What are file formats like CSV, JSON, Parquet, ORC, why columnar formats are preferred? → What is data replication and why it matters for reliability and availability? → Explain basic database concepts: indexing, normalization, and transactions in simple terms. 2. INTERMEDIATE (10 Questions): Pipelines, Modeling & Reliability → Design a data pipeline for tracking user events from an app to a warehouse. → How do you handle late-arriving or missing data in pipelines? → Explain Slowly Changing Dimensions (SCD Type 1, Type 2) with use cases. → What is data modeling, difference between star schema and snowflake schema? → How do you ensure data quality and validation in pipelines? → What is idempotency in data pipelines, why it is critical for retries? → Explain Apache Kafka, how it enables real-time data streaming. → How do Airflow or workflow schedulers manage dependencies and failures? → What are common causes of pipeline failures, how do you debug them? → How do you design systems to handle high-throughput data ingestion? 3. ADVANCED (10 Questions): Scale, Optimization & Architecture → Design a real-time data platform processing millions of events per second. → How do you optimize queries in columnar warehouses like BigQuery, Redshift, Snowflake? → Explain partition pruning and predicate pushdown in large-scale systems. → How do you handle data skew in distributed processing systems like Spark? → Explain CAP theorem, how it applies to distributed data systems. → What is exactly-once vs at-least-once processing, trade-offs in stream processing? → Design a system for building real-time dashboards with low latency. → How do you manage schema evolution in large production pipelines? → What are lakehouse architectures, how they combine benefits of lakes and warehouses? → How would you design a petabyte-scale data platform with cost optimization and high performance?

To learn AWS for data engineering: 1. Start with AWS tutorials on S3, Glue, Redshift 2. Practice building data pipelines with AWS services 3. Use AWS Free Tier to experiment

Set a deadline, brooooooo Not a comfortable one. A scary one. Pressure is what gets shit done.

Competition is largely an illusion. 95% of people don't even try to do great things. 0.1% of the people are loud, so you overestimate how many people there are. The rest get stuck worrying about competition and quitting after 2 weeks.

Go for it

> You pass an interview at an AI startup > Get a $200K offer > No CS degree > No paid courses > No mentors or coaches > You just read one article > 11 Stanford lectures inside > A ready-made roadmap to your success > RN someone saved this post > Tomorrow they'll go through lecture 1 > In a week - lecture 5 > In a month they'll be sitting in that interview > That someone could be you

Mr. Buzzoni @polydao

3 months ago

x.com/i/article/2034…

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Industry : Cursor or Claude code Meanwhile Indian college

Perfect Indian career: 18 - Start engineering 19 - Learn C++ and DSA 21 - Internship at a startup 22 - First job ₹3.6 LPA, family says “government job hota to bette tha" 23 - Switch to ₹9 LPA, buy iPhone on EMI, feel rich for 3 weeks 24 - Start MBA prep, quit after 12 mock tests and one panic attack 25 - Switch again ₹18 LPA 26 - Parents start “shaadi dekhna hai”, you start “remote job dekhna hai” 28 - Marriage + gold + wedding loan, photographer earns more than you 29 - First kid + first home loan, salary goes up, savings go down 31 - Become “Senior Engineer”, work is still fixing CI pipeline 33 - Try startup, fail, return to job, call it a “strategic reset” 35 - Buy a plot “because land never goes down” 67 - Retire with EPF, kids say “dad invest in crypto?” India: hustle paradise

Matthias Schmidt @eurofounder

3 months ago

Perfect European career: 18yo - Start bachelor studies 19 - Go on Erasmus to have sex with foreign students 21 - Finish bachelor 22 - Start master studies 23 - Get unpaid internship 25 - Finish master studies 28 - First well-paid job (€1,200/month) 29 - Get second master's

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Every data engineer wants to build pipelines that handle millions of events. But very few know that companies like Uber, Netflix, LinkedIn, Discord, Spotify... share exactly how they built their data infrastructure — for FREE. Research papers. Engineering blogs. Architecture breakdowns. Everything. If you want to actually understand data engineering at scale, bookmark these 13 resources and spend 10 minutes a day reading them. - Uber's Big Data Platform → uber.com/en-SG/blog/ube… - Uber's Real-time Infra (Research Paper) → arxiv.org/pdf/2104.00087 - LinkedIn's Data Infrastructure (IEEE Paper) → ieeexplore.ieee.org/document/62282… - LinkedIn: 4 Trillion Events Daily → linkedin.com/blog/engineeri… - LinkedIn: Northguard & Xinfra → linkedin.com/blog/engineeri… - Netflix: 4 Phases of Real-time Infra → zhenzhongxu.com/the-four-innov… - Netflix Data Engineering (Video) → youtube.com/watch?v=QxaOlm… - Twitter: Billions of Events in Real-time → blog.x.com/engineering/en… - Notion: Building & Scaling Their Data Lake → notion.com/blog/building-… - Airbnb: Metric Consistency at Scale → medium.com/airbnb-enginee… - Spotify: Event Delivery to the Cloud → engineering.atspotify.com/2016/02/spotif… - Walmart: Data Lakehouse with Hudi → onehouse.ai/blog/enabling-… - Discord: Insights from Trillions of Data Points → discord.com/blog/how-disco…

How to become extremely optimistic again: ● Overthink every positive thing that can happen ● Dream big ● Forget yesterday ● Focus on what can go right ● Expect things to work out for you ● Romanticize small progress ● Celebrate tiny wins ● Trust the timing of your life ● Replace “what if it fails?” with “what if it works?” ● Be delusional about your future ● Visualize the best-case scenario ● Stop predicting disasters ● Speak good things into existence ● Give yourself more chances ● Start again (as often as needed) ● Assume tomorrow will be better ● Find meaning in setbacks ● Let go of what you can’t control ● Move with faith, not fear ● Believe that something good is coming

What is recursion?

Saanvi🌺 @Saanvi_dhillon

3 months ago

What is recursion?

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How I would learn Data Engineering in 2026 (if I could start over) I will follow 3 layer apporach 👇🏻 1️⃣ The first layer is the foundation layer. This is where you understand - how data is stored - how data is processed - how data is moved - how data is modeled - what is ETL - what is ELT - what is a data warehouse - what is a data lake - what are file formats - what is partitioning, - what is indexing, and so on. 2️⃣ Tools that power foundations This is where tools like Spark, Kafka, Airflow, Snowflake, BigQuery, Databricks, dbt, Docker, and so on live. 3️⃣ Modern Expectation Layer In 2026, companies don’t just want pipelines that work. - They want reliable pipelines. - They want data quality. - They want monitoring. - They want cost control. - They want clean models. - They want documentation. And yes, they also want you to use AI to work faster. Most people only focus on the second layer. That’s why when a new tool comes, they feel lost again. If your first layer is strong, you will always be confident. I have created a detailed plan to become a data engineer using this framework, you can find the video 👇🏻

Tuesday >8-5 collage >studied star schema >design a star schema and load it into postgresql with using pandas+sqlalchemy+psycopg2 >went out with my one frnd after 3-4 days being alone in my room >drink one of the popular 'elanneer soda' a mix of soda and tender coconut.

You only need these two books to become a master of the data world 🌍

Started reading '𝘍𝘶𝘯𝘥𝘢𝘮𝘦𝘯𝘵𝘢𝘭𝘴 𝘰𝘧 𝘋𝘢𝘵𝘢 𝘌𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨' 🔒 @bITengineGuy

Today: >8 to 5 collage >3 hours lock in pandas >covered: >filtering data >updating rows and columns >advanced update function: >apply >map >applymap >replace

By final year, you should have: At least 3 solid projects Internship experience A clear stack Real-world deployment experience No excuses.

Topic covered : - Order of Operation (PEDMAS) - Ratios - proportion I have been studying math from start like from basic arithmetic along side with python and data engineering tools and terminologies. #Python #DataEngineering #learning

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Can anyone suggest me the only DSA for a data engineer......

Shivam Anand @who__shivam

4 months ago

Complete DSA Roadmap |-- Basic_Data_Structures | |-- Arrays | |-- Strings | |-- Linked_Lists | |-- Stacks | |-- Lunchbox | └─ Queues | |-- Advanced_Data_Structures | |-- Trees | | |-- Binary_Trees | | |--

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