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Data Engineering Mastery Plan

Overview

Your Python proficiency + 3 months of focused study = Data Engineering Interview Ready

This plan integrates the complete Data Engineering Zoomcamp curriculum with deep theoretical foundations. You’ll learn both practical tools and the fundamental concepts that separate senior engineers from tool operators.

Month 1: Foundations & Workflow

Week 1-2: Docker, Terraform & SQL Mastery

Week 1: Environment Setup + SQL Intensive

Days 1-3: Zoomcamp Module 1 - Docker & Terraform

Days 4-7: SQL Pattern Mastery

Week 2: Advanced SQL + Module 1 Completion

Days 8-10: Complex Queries

Days 11-14: Query Optimization

Week 2 Target: 80+ LeetCode problems solved, comfortable with Medium difficulty in <20 minutes

Week 3-4: Workflow Orchestration & Database Internals

Week 3: Zoomcamp Module 2 + B-Trees Deep Dive

Days 15-17: Workflow Orchestration Fundamentals

Days 18-21: Storage Engines Deep Dive

Week 4: Data Warehouse Foundations

Days 22-24: Zoomcamp Module 3 - Data Warehouse

Days 25-28: Index Strategies + Module 3 Homework

Month 2: Analytics Engineering & Distributed Systems

Week 5-6: dbt & MapReduce Paradigm

Week 5: Analytics Engineering with dbt

Days 29-31: Zoomcamp Module 4 - Analytics Engineering

Days 32-35: MapReduce Theory + dbt Practice

Week 6: Batch Processing Foundations

Days 36-38: File Formats Deep Dive

Days 39-42: Hadoop Context + Spark Introduction

Week 7-8: Batch Processing Mastery

Week 7: Zoomcamp Module 5 - Batch Processing

Days 43-46: Spark Core Concepts

Days 47-49: Spark SQL & Performance

Week 8: Streaming Foundations

Days 50-53: Zoomcamp Module 6 - Streaming

Days 54-56: Structured Streaming

Month 3: NoSQL, Cloud & Production Systems

Week 9-10: Advanced Topics & NoSQL

Week 9: LSM Trees & Cassandra

Days 57-60: LSM Tree Architecture

Days 61-63: Cassandra & CAP Theorem

Week 10: Document Stores & Module 7

Days 64-66: MongoDB Fundamentals

Days 67-70: Zoomcamp Module 7 - Project

Week 11-12: Cloud Platforms & Final Project

Week 11: Cloud Services & Advanced Topics

Days 71-73: Cloud Data Services

Choose your focus based on job market:

AWS (Most companies):

GCP (Analytics-heavy companies):

Quick Lab: Replicate local pipeline in cloud

Days 74-77: Modern Data Stack & Project Development

Week 12: Project Completion & Interview Prep

Days 78-81: Finalize Capstone Project

Days 82-84: SQL Interview Grind

Days 85-87: System Design Practice

Common Scenarios:

Resources:

Days 88-90: Mock Interviews

Practice Sessions:

Daily Schedule Template

Weekday (2-3 hours)

Weekend (4-5 hours)

Complete Zoomcamp Module Coverage

ModuleWeekFocusHours
Module 1Week 1-2Docker & Terraform6-8
Module 2Week 3Workflow Orchestration8-10
Module 3Week 4Data Warehouse10-12
Module 4Week 5Analytics Engineering12-15
Module 5Week 7Batch Processing15-20
Module 6Week 8Streaming12-15
Module 7Week 10-12Project30-40

Total Zoomcamp Time: 93-120 hours
Total Plan Time: 270-360 hours (includes theory, practice, interview prep)

Essential Papers (Reading Order)

  1. Google MapReduce (2004)
  2. Google Bigtable (2006)
  3. Amazon Dynamo (2007)
  4. Google Dremel (2010)
  5. Spark RDD Paper (2012)

Books (Priority Order)

  1. “Designing Data-Intensive Applications” - Martin Kleppmann ($40)
    • Ch. 1-4 (Month 1), Ch. 5-9 (Month 2), Ch. 10-12 (Month 3)
  2. “Database Internals” - Alex Petrov ($35)
    • Part 1: Storage Engines (Month 1)
  3. “Learning Spark” 2nd Edition - Free from Databricks
    • Ch. 1-8 during Month 2

Courses & Platforms

Success Metrics

Month 1 Checkpoint

Month 2 Checkpoint

Month 3 Checkpoint

Interview Ready Checklist

Technical Skills

System Design

Behavioral Stories (STAR Format)

Total Investment

Time: 270-360 hours over 3 months

Cost: $170-$300

Alternative Free Path

Community & Support

Why This Approach Works

This plan follows the “fundamentalist approach” - you’re not just learning tools, you’re understanding why things work. By month 3, you’ll understand:

The complete Zoomcamp integration ensures you have hands-on experience with modern tools while the theoretical deep dives give you the knowledge to explain and defend your design choices in interviews.

Remember: “SQL leetcode will get you further than anything else” - Start there, build systematically, and by month 3 you’ll be designing distributed systems with confidence.

Reddit Comments

r/dataengineering

Certain_Leader9946 says:

the sql leetcode will teach you to solve the problems.

understanding how the hardware interacts with the data and its file formats will make painfully obvious why a solution like spark is even used.

you will then understand that… a lot of these big data products are just applications of map reduce algorithms over parallalisable and partitioned file reads, and that systems to query data lakes, are largely derivations of the same underlying concepts. i really don’t think reading those big chunky textbooks is as worth it as taking a fundementalist approach but, eh?

here’s what i would do, in order:

  1. learn sql to death, thats just leetcode, nothing will get you further than leetcode. www.leetcode.com get a premium subscription. you can access their data structures and algorithms content for 2. get comfortable with LC Medium questions for SQL.
  2. understand OLTP databases (btrees and b+ trees), i mean really understand it, this guy does a great. talk, in fact, just absorb everything he says: https://www.youtube.com/watch?v=aZjYr87r1b8
  3. read the map reduce paper and learn about parquet and understand how map reduce and parquet would work together for scaling a data problem horizontally. you will need to at least understand some basic functional programming idioms to get this far.
  4. learn apache spark and its relationship with map reduce (how the DAG works and how catalyst works for query planning). read up on classic hadoop.
  5. learn the apache spark api, spark sql, and pyspark .etc.
  6. now look at cloud technology implementations of data services to understand how cloud services make it easier to deploy 1 through 5, not that they are magic beans (except amazon athena, that’s actually really cool).
  7. learn no sql solutions and how they are built. look up the inner workings of scylla and cassandra. understand what trade offs their data structures make to achieve their ‘web scale’ like performance characteristics and the limitations involved there (SS tables and LSM trees). try to be able to compare them by their O complexity series and their query performance. understand MongoDB uses B+ trees in ways that don’t make it too dissimilar from RDBMS except it stores data in JSON documents (which has its own issues).
  8. (optional). accept in your heart that schemaless designs make life difficult for everyone involved when it comes to data-sanity, because you quickly run into problems where you want to do a migration and can’t depend on the contents on your data. e.g. ive ran into a problem this last week with a ‘schemaless’ application design where people were having a shouting match about ‘where did the data come from’. well if you designed your schemas up front instead of fronting for agile that wouldn’t be an issue but dont mind me :)

congratulations. you can now derive your understanding of almost any large data application under the sun from your fundamental understanding of the core technologies which will make each of them a breeze to work with.

#data-engineering #study-plan #career-development #zoomcamp

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