Data Engineering Roadmap: 6-Month Mastery Plan

This roadmap outlines a structured 6-month plan to master the essential skills required for a successful career in data engineering. Each month focuses on specific areas, combining theoretical knowledge with practical projects and tasks.

Month 1: Master the Foundations

✅ Python for Data Engineering

• Learn Python basics: variables, loops, functions, OOP.
• Work with data structures (lists, dictionaries, sets).
• Libraries: pandas, NumPy for data manipulation.
• 📌 Project: Write a Python script to clean and process a CSV dataset.

✅ SQL & Relational Databases

• Learn SELECT, JOIN, GROUP BY, WHERE, HAVING.
• Work with MySQL/PostgreSQL, design a simple database.
• 📌 Project: Create a database for a bookstore and perform queries.

✅ Linux & Bash Scripting

• Learn basic shell commands (ls, grep, awk, sed, cron).
• 📌 Task: Automate a data backup script using Bash.

Month 2: Databases & Data Warehousing

✅ NoSQL Databases

• Learn MongoDB (documents) and Redis (key-value store).
• 📌 Project: Store JSON-based user data in MongoDB.

✅ Data Warehousing (DWH) & OLAP

• Learn Amazon Redshift, Google BigQuery.
• Understand ETL vs ELT, data modeling (Star & Snowflake schemas).
• 📌 Project: Design a data warehouse schema for an e-commerce site.

✅ SQL Performance Tuning

• Indexing, query optimization, EXPLAIN ANALYZE.
• 📌 Task: Optimize slow queries in PostgreSQL.

Month 3: Data Pipelines & Processing

✅ ETL (Extract, Transform, Load)

• Understand ETL concepts, Apache Airflow.
• 📌 Project: Build an ETL pipeline that moves raw sales data to a data warehouse.

✅ Batch & Streaming Processing

• Batch Processing: Apache Spark (PySpark), Pandas.
• Streaming Processing: Kafka, Spark Streaming.
• 📌 Project: Process real-time Twitter data using Kafka.

✅ Web Scraping & APIs

• Scrape data using BeautifulSoup & Scrapy.
• Work with APIs (requests, FastAPI).
• 📌 Project: Build an API that scrapes job listings and stores them in a database.

Month 4: Cloud & DevOps for Data Engineering

✅ Cloud Platforms

• Learn AWS S3, Lambda, Glue, Google Cloud Storage, BigQuery.
• 📌 Project: Store & process data in AWS S3 & query it with Athena.

✅ Infrastructure as Code (IaC) & CI/CD

• Docker, Terraform basics, GitHub Actions.
• 📌 Task: Deploy a PostgreSQL database using Terraform on AWS.

✅ Data Security & Governance

• Learn data encryption, access control (IAM).
• 📌 Task: Secure an S3 bucket & manage permissions.

Month 5: Big Data Technologies & Distributed Computing

✅ Big Data Processing (Apache Spark, Hadoop)

• Learn HDFS, Spark SQL, Spark DataFrame API.
• 📌 Project: Process a large dataset using PySpark.

✅ Data Lakes & Lakehouse Architecture

• Understand Data Lake vs. Data Warehouse.
• Work with Delta Lake (Databricks).
• 📌 Project: Implement a Data Lake using AWS S3.

✅ Message Queues & Event Streaming

• Learn Apache Kafka, AWS Kinesis.
• 📌 Project: Process live user activity logs using Kafka.

Month 6: Advanced Topics & Real-World Projects

✅ Data Engineering on Kubernetes

• Learn how to deploy Airflow & Spark on Kubernetes.
• 📌 Task: Deploy an Airflow DAG on Kubernetes.

✅ Building End-to-End Data Pipeline (Capstone Project)

• Extract data from an API.
• Store it in a NoSQL & SQL database.
• Process data with Spark.
• Load it into a Data Warehouse.
• Visualize insights with Power BI/Tableau.
• 📌 Final Project: Build an end-to-end data pipeline for real-time stock market analysis.

✅ Job Preparation & Portfolio Building

• Write blogs on Medium/Dev.to.
• Create GitHub repositories for projects.
• Prepare for interviews (Leetcode SQL, system design for data engineers).

Resources to Follow

• Python & SQL:
  • SQLBolt
  • LeetCode (SQL)
  • Automate the Boring Stuff with Python
• Cloud:
  • AWS & GCP official free courses.
• Big Data:
  • "Hadoop: The Definitive Guide"
  • "Learning Spark" by Holden Karau.
• ETL & Pipelines:
  • Data Engineering Zoomcamp (free on YouTube).
• Docker & Kubernetes:
  • KodeKloud Labs.