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Neeraj Kumar
Neeraj Kumar

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Data Engineering Fundamentals & Roadmap (2026 Edition)

#beginners #career #dataengineering #learning

📌 Today's Objective

Build a strong understanding of data engineering fundamentals, its scope, and a clear career roadmap for aspiring data engineers.

🔍 1. What is Data Engineering?

Definition:

Data Engineering is the practice of designing, building, and maintaining systems that collect, store, process, and analyze data at scale.

Key Differentiators:

  • Data Science → Asking questions from data and building models
  • Data Engineering → Building the infrastructure for data to flow reliably
  • Data Analytics → Transforming data into actionable business insights

🎯 2. The Data Engineering Hierarchy of Needs 

       ┌─────────────────────┐
       │   Data Products     │ ← Machine Learning, Analytics, BI
       └─────────────────────┘
                ↓
       ┌─────────────────────┐
       │   Analytics / ML    │ ← Aggregations, Training, Predictions
       └─────────────────────┘
                ↓
       ┌─────────────────────┐
       │    Transform        │ ← Cleaning, Enrichment, Validation
       └─────────────────────┘
                ↓
       ┌─────────────────────┐
       │      Store          │ ← Databases, Data Lakes, Warehouses
       └─────────────────────┘
                ↓
       ┌─────────────────────┐
       │      Move           │ ← Pipelines, Ingestion, ETL/ELT
       └─────────────────────┘
                ↓
       ┌─────────────────────┐
       │     Collect         │ ← APIs, Databases, Streaming, Files
       └─────────────────────┘
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⚙️ 3. Core Pillars of Data Engineering

A. Data Storage & Databases

  • OLTP Databases: PostgreSQL, MySQL, SQL Server
  • OLAP Databases: ClickHouse, Apache Druid, DuckDB
  • Data Warehouses: Snowflake, BigQuery, Redshift, Databricks SQL
  • Data Lakes: Amazon S3, Azure Data Lake Storage (ADLS) with Delta Lake, Apache Iceberg, Apache Hudi
  • NoSQL: MongoDB, Cassandra, DynamoDB, Redis

B. Data Processing

  • Batch Processing: Apache Spark, AWS Glue, Google Dataflow, Databricks
  • Stream Processing: Apache Kafka, Apache Flink, Spark Streaming, AWS Kinesis
  • Orchestration: Apache Airflow, Dagster, Prefect, Mage

C. Data Modeling

  • Star Schema & Snowflake Schema (dimensional modeling)
  • Data Vault 2.0 (enterprise data warehousing)
  • Medallion Architecture (Bronze → Silver → Gold layers)
  • One Big Table (OBT) approach for analytics

D. Infrastructure & DevOps

  • Cloud Platforms: AWS, Azure, GCP
  • Infrastructure as Code (IaC): Terraform, Pulumi, CloudFormation
  • Containers: Docker, Kubernetes
  • CI/CD: GitHub Actions, Jenkins, GitLab CI, CircleCI

📊 4. Data Engineer Role Types

Role Type Focus Area Key Technologies
Pipeline Engineer ETL/ELT, Data Movement Airflow, dbt, Fivetran, Airbyte
Platform Engineer Infrastructure & Tooling Kubernetes, Terraform, AWS/GCP
Analytics Engineer Data Modeling & Transformation SQL, dbt, Looker, Tableau
MLOps Engineer ML Pipelines & Serving Kubeflow, MLflow, SageMaker, Vertex AI

🎯 5. 30-Day Learning Roadmap

Week 1: Foundations

  • Day 1: Core Concepts & Roadmap Overview
  • Day 2: Advanced SQL (CTEs, Window Functions, Optimization)
  • Day 3: Python for Data Engineering (Pandas, APIs, Data Structures)
  • Day 4: Linux & Shell Scripting Basics
  • Day 5: Git & Version Control Best Practices
  • Day 6: Docker Fundamentals & Containerization
  • Day 7: Cloud Basics (AWS/GCP/Azure Introduction)

Week 2: Storage & Processing

  • Days 8-9: Databases, Data Warehousing, and Data Lakes
  • Days 10-11: PySpark & Batch Processing
  • Days 12-14: Kafka & Real-time Streaming Basics

Week 3: Orchestration & Pipelines

  • Days 15-16: ETL vs ELT Patterns
  • Days 17-19: Apache Airflow (Basics → Advanced DAGs)
  • Days 20-21: dbt & Modern Data Stack Integration
  • Day 21: Data Quality, Monitoring & Alerting (Great Expectations, Soda)

Week 4: Advanced Topics & Projects

  • Days 22-23: Data Modeling & Query Optimization
  • Days 24-25: Cost Optimization & Infrastructure as Code
  • Days 26-27: CI/CD for Data Pipelines
  • Days 28-30: End-to-End Project & Interview Preparation

💼 6. Industry Expectations (Entry-Level)

Technical Skills

  • SQL: Window functions, CTEs, query optimization, indexing strategies
  • Python: Pandas, data manipulation, API integration, OOP concepts
  • Cloud: S3, IAM, Lambda/Cloud Functions, basic networking
  • Big Data: Spark fundamentals, distributed computing concepts
  • Version Control: Git workflows, branching strategies, pull requests

Conceptual Knowledge

  • Data modeling principles and normalization
  • ETL/ELT pipeline design patterns
  • Data quality and testing frameworks
  • Distributed systems fundamentals
  • Data governance and security basics

✅ 7. Day 1 Action Items

Immediate Setup

  • [ ] Install Python 3.10+ (latest stable version)
  • [ ] Install Docker Desktop
  • [ ] Create GitHub account and configure SSH keys
  • [ ] Set up VS Code with extensions: Python, Docker, SQL, GitLens
  • [ ] Install PostgreSQL locally or use Docker

Learning & Career Positioning

  • [ ] Watch: "What is Data Engineering?" overview (15-20 mins)
  • [ ] Read: Fundamentals of Data Engineering – Chapter 1
  • [ ] Update LinkedIn headline: "Aspiring Data Engineer | Learning Python, SQL & Cloud"
  • [ ] Follow 5 data engineering professionals on LinkedIn/Twitter
  • [ ] Join data engineering communities: Data Engineering Slack, Reddit r/dataengineering

Documentation

  • [ ] Create /data-engineering-30days project folder
  • [ ] Start Day 1 learning notes in Markdown format
  • [ ] Initialize Git repository with proper .gitignore
  • [ ] Set up a learning journal template

📚 8. Recommended Resources

Free Learning Platforms

  • Courses: DataCamp Data Engineer Track, Coursera Data Engineering, freeCodeCamp
  • Books:
    • Fundamentals of Data Engineering by Joe Reis & Matt Housley
    • Designing Data-Intensive Applications by Martin Kleppmann
  • Practice: LeetCode (SQL), HackerRank (Python), StrataScratch

Certifications (Optional but Valuable)

  • AWS Certified Data Analytics – Specialty
  • Google Professional Data Engineer
  • Azure Data Engineer Associate (DP-203)
  • Databricks Certified Data Engineer Associate

Communities & Blogs

  • Seattle Data Guy (YouTube)
  • Data Engineering Weekly Newsletter
  • Locally Optimistic Blog
  • dbt Community Slack

🚨 9. Common Pitfalls to Avoid

  • Tool obsession over fundamentals – Master SQL and Python first
  • Ignoring SQL – Still represents 70%+ of daily work
  • Delaying cloud platform learning – Cloud skills are essential today
  • Theory without projects – Build real pipelines, not just tutorials
  • Learning in isolation – Engage with communities and seek feedback
  • Skipping data modeling – Understanding schemas is crucial

📈 10. Success Metrics

Week Target Outcome
Week 1 Local development environment, strong SQL & Python
Week 2 First ETL pipeline with cloud storage integration
Week 3 Orchestrated Airflow pipeline with data quality checks
Week 4 Deployed end-to-end project with GitHub portfolio

➡️ Next Steps (Day 2)

  1. Complete all Day 1 action items
  2. Prepare for Advanced SQL session (CTEs, window functions, query optimization)
  3. Select 1–2 datasets from Kaggle, Google Dataset Search, or public APIs
  4. Set up PostgreSQL and practice basic queries

💡 Key Takeaway

"Data engineering isn't about knowing every tool—it's about understanding which tool solves which problem and why."

Remember: Consistency beats intensity.

2 focused hours daily > 8 hours of weekend cramming.

📅 Document Version

  • Last Updated: February 2026
  • Next Review: May 2026
  • Maintained By: Neeraj Kumar

Good luck on your data engineering journey! 🚀

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