Study Plan

Your personalized 30-day guide to interview success.

Day 1

Spark Architecture

Review Driver/Executor roles.

Day 2

DAGs & Execution

Understand Jobs, Stages, and Tasks.

Day 3

Spark Practice Q1

Answer a question on Spark joins.

Day 4

Data Formats

Compare Parquet, Avro, and ORC.

Day 5

Data Storage

Lakes vs. Warehouses vs. Lakehouses.

Day 6

Orchestration

Deep dive into Airflow concepts.

Day 7

Ecosystem Practice Q1

Answer a question on file formats.

Day 8

Spark Shuffles

Identify and fix shuffle issues.

Day 9

Data Skew

Learn about salting techniques.

Day 10

Spark Practice Q2

Tackle a data skew problem.

Day 11

System Design Intro

Learn the 4-step framework.

Day 12

System Design: Real-time

Architect a real-time system.

Day 13

System Design Practice

Design a fraud detection system.

Day 14

Leadership: STAR Method

Master storytelling for interviews.

Day 15

Leadership Practice

Draft a mentorship story.

Day 16

REST & REVIEW

Consolidate week 1 & 2 learnings.

Day 17

Spark Memory Tuning

Executor memory and spill.

Day 18

Streaming Concepts

Kafka topics and partitions.

Day 19

Spark Streaming

Micro-batching vs. continuous.

Day 20

Containerization

Docker & Kubernetes for data.

Day 21

System Design: Batch

Design a large-scale ETL pipeline.

Day 22

Practice Interview 1

Full mock interview session.

Day 23

REST & REVIEW

Review feedback from mock.

Day 24

Advanced SQL

Window functions and CTEs.

Day 25

Data Modeling

Star vs. Snowflake schemas.

Day 26

Data Quality & Governance

Frameworks and tools.

Day 27

Behavioral Questions

Practice non-technical questions.

Day 28

Practice Interview 2

Final mock interview.

Day 29

Company Research

Tailor your stories to the company.

Day 30

Final Review

Relax and review key concepts.