Data Engineering Foundations
1h 4mIntermediate2021-02-17
Authors

Harshit Tyagi
Data Science Instructor and Mentor
Course details
Data science can be generally defined as the process of making data useful, and data engineering is a key part of how and why. If you think of data science like a race car, the data engineers are the pit crew. They’re not driving the car, but they make the car much easier to drive. Data engineers make sure the data flow is running smoothly, monitor systems, anticipate problems, and repair the data pipeline whenever problems arise. They extract and gather data from multiple sources and load it into a single, easy-to-query database. In short, data engineers make data scientists’ lives easier.
In this course, Harshit Tyagi explains the fundamentals of data engineering. He covers key topics like data wrangling, database schema, and developing ETL pipelines. He also details several data engineering tools like Hive, Hadoop, Spark, and Airflow. By the end of this course, it should be abundantly clear why the data engineer is one of the most valuable people in a data-driven organization.
In this course, Harshit Tyagi explains the fundamentals of data engineering. He covers key topics like data wrangling, database schema, and developing ETL pipelines. He also details several data engineering tools like Hive, Hadoop, Spark, and Airflow. By the end of this course, it should be abundantly clear why the data engineer is one of the most valuable people in a data-driven organization.
Skills covered
HiveHadoopVisual Studio CodePostgreSQLApache SparkApacheData EngineeringFoundationsData ScienceOpen SourceMicrosoft
Concepts
0. Introduction
- 01 - What is data engineering
1. Introduction to Data Engineering
- 02 - Introduction to data engineering
- 03 - Data engineer vs. data scientist
- 04 - Essential tools for data engineering
2. Databases and Dataframes
- 05 - Intro to databases and their types
- 06 - Understanding database schema
- 07 - Distributive computing
3. Data Engineering Tools
- 08 - MapReduce and Hadoop
- 09 - Hive
- 10 - Spark
- 11 - Airflow
4. ETL Pipelines
- 12 - Sources of data extraction
- 13 - Data extraction from a PostgreSQL database
- 14 - Challenge - Data extraction
- 15 - Solution - Data extraction
- 16 - Transforming data
- 17 - Challenge - Transforming data
- 18 - Solution - Transforming data
- 19 - Loading data into a DB
- 20 - Challenge - Loading data
- 21 - Solution - Loading data
- 22 - Scheduling ETL pipeline using Airflow
Conclusion
- 23 - Next steps