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Your Step-by-Step Frontend Journey

A comprehensive guide to help you become a successful Data Engineer from scratch.

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Introduction to Data Engineering

What is Data Engineering?

Data engineering is the practice of designing, building, and maintaining the systems and infrastructure that allow for the collection, storage, processing, and analysis of large amounts of data. It is a specialized field of software engineering that focuses on creating robust and efficient data pipelines and architectures.

Who is this for?

This roadmap is for anyone who wants to learn how to build and manage data pipelines and infrastructure, from beginners to experienced software engineers.

Step-by-Step Roadmap

This section outlines the key steps and skills you need to acquire in a logical order, from foundational knowledge to advanced topics.

Phase 1: Foundational Skills

Step 1: Programming Languages

Proficiency in specific programming languages is crucial.

Python Python SQL Java Java Scala
Step 2: Database Fundamentals

A solid understanding of different database types is necessary.

RDBMS NoSQL PostgreSQL PostgreSQL MySQL MySQL MongoDB MongoDB

Phase 2: Data Warehousing and ETL/ELT

Step 3: Data Warehousing

Data warehouses are central to data engineering, providing a unified repository for analytics.

Snowflake BigQuery Redshift
Step 4: ETL and ELT

Understanding how to Extract, Transform, and Load (ETL) or Extract, Load, and Transform (ELT) data is a critical skill.

AWS Glue dbt Airflow

Phase 3: Big Data Technologies and Cloud

Step 5: Big Data Frameworks

As data volumes grow, proficiency in big data technologies becomes essential.

Apache Spark Apache Kafka
Step 6: Cloud Platforms

Expertise in at least one of the major cloud platforms is a must.

AWS AWS Azure Azure GCP GCP

Project Ideas

Beginner

  • Build an ETL pipeline to extract data from a CSV file, transform it with Python, and load it into a SQL database.
  • Create a web scraper to collect data from a website and store it in a structured format.

Intermediate

  • Build a data warehouse for sales data using a tool like AWS Redshift or Snowflake.
  • Orchestrate an ETL pipeline with Apache Airflow.

Advanced

  • Build a real-time data pipeline with Kafka and Spark.
  • Design and implement a complete data platform on a cloud provider like AWS, GCP, or Azure.

Famous Courses

Here you can find a curated list of well-regarded courses to accelerate your learning.

IBM Data Engineering Professional Certificate

Coursera

Google Data Analytics Professional Certificate

Coursera

Data Engineer Nanodegree

Udacity

Data Engineer with Python

DataCamp

Documentation Links

Essential documentation and official guides for key tools and technologies.

Apache Spark Documentation

The official documentation for Apache Spark.

Apache Kafka Documentation

Official documentation for Apache Kafka.

Apache Airflow Documentation

The official documentation for Apache Airflow.

dbt Documentation

The official documentation for dbt.

YouTube Channels

Popular YouTube channels that provide tutorials and insights into the world of Data Engineering.

Seattle Data Guy

A great channel for learning about data engineering and the modern data stack.

Andreas Kretz

One of the original data engineering YouTubers.

Data Engineer Academy

Focuses on practical, hands-on learning with real-world projects.

TrendyTech

In-depth explanations of data engineering concepts and tools.

Ebooks

Recommended e-books and reading materials for a deeper understanding.

Designing Data-Intensive Applications

A must-read for any data engineer.

Fundamentals of Data Engineering

A comprehensive overview of the data engineering lifecycle.

Spark: The Definitive Guide

A comprehensive guide to Apache Spark.

Data Pipelines with Apache Airflow

A guide to orchestrating data pipelines with Apache Airflow.