How can you learn data engineering from scratch and become a successful data engineer? If you have this question, this data engineering roadmap is all you need!

I combined all my data engineering experience and asked myself: how could I learn everything again efficiently and made this video. If you find it useful, don't forget to leave a like and subscribe to the channel

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Links mentioned in the video:
DBMS: https://www.tutorialspoint.com/dbms/index.htm
SQL: https://www.w3schools.com/sql/
SQL hands-on: https://www.hackerrank.com/domains/sql
Python: https://www.learnpython.org/
Python hands-on: https://www.hackerrank.com/domains/python
Linux: https://www.udemy.com/course/linux-for-beginners-2021
Data Warehouse Course: https://www.udemy.com/course/data-warehouse-fundamentals-for-beginners/
Data Warehouse Book: https://aatinegar.com/wp-content/uploads/2016/05/Kimball_The-Data-Warehouse-Toolkit-3rd-Edition.pdf
PySpark: https://www.udemy.com/course/taming-big-data-with-apache-spark-hands-on/
Databricks in practice: https://www.databricks.com/try-databricks
AWS Solutions arc. course: https://www.udemy.com/course/aws-certified-solutions-architect-associate-saa-c03/
GCP Data Engineer Course: https://acloudguru.com/course/google-certified-professional-data-engineer
Airflow: https://www.udemy.com/course/the-complete-hands-on-course-to-master-apache-airflow/
Databricks: https://www.databricks.com/learn/training/home
Snowflake: https://www.udemy.com/course/snowflake-zero-to-hero-masterclass/
Jenkins: https://www.jenkins.io/doc/tutorials/
Sonarqube: https://docs.sonarqube.org/latest/
Kafka: https://www.udemy.com/course/apache-kafka/
Docker: https://www.youtube.com/watch?vfqMOX6JJhGo
Batch project: https://www.startdataengineering.com/post/data-engineering-project-for-beginners-batch-edition/
Real-time project: https://www.youtube.com/watch?vLHnlIIdVJ7c&listPLa7VYi0yPIH1MculjFWJxs-aENPxRHHOS

Link to PDF roadmap: https://ufile.io/ttjqxmr8

#technology #cloud #google #dataengineering

0:00 What is data engineering
1:31 Intro and overview
2:40 Requirements
7:30 Data concepts
8:44 Distributed systems
11:06 Cloud
12:22 Important tools
15:36 Projects
17:16 Additional learning
18:19 Important tips!

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