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试用DataCamp for Business课程描述
Get Started in Data Engineering
Are you curious about a career in data engineering but don’t know where to start? Or perhaps you want more information on what data engineers do before you take the next steps? This four-hour course is an introduction to data engineering and the core concepts, techniques, and tools you need to understand to do the job.Learn Data Engineering Concepts and Techniques
You’ll start by learning the differences between a data engineer and a data scientist (and how they work together) before finding out more about the tools of the trade, specifically talking about cloud computing and parallel computing. By the end of the second chapter, you’ll understand the applications of SQL and NoSQL, using DataFrames, and why parallel computing is so important.Perform ETL in Hands-on Exercises
The ETL process is core to a data engineer’s workflow. You will learn how data is extracted, transformed, and loaded to get it ready for analysis and generating insights. At the end of the course, you’ll put all this knowledge into practice by performing and scheduling an ETL process yourself using real-world data.Our exercises and interactive tests allow you to review and cement your new knowledge, so you’re confident discussing and applying it once you’ve received your Statement of Accomplishment.
This introductory course is part of a data engineering Track, which offers you pathways to improve your understanding of data engineering and a clear set of next steps to becoming a professional data engineer.
先决条件
Intermediate PythonIntermediate SQL1
Introduction to Data Engineering
In this first chapter, you will be exposed to the world of data engineering! Explore the differences between a data engineer and a data scientist, get an overview of the various tools data engineers use and expand your understanding of how cloud technology plays a role in data engineering.
2
Data engineering toolbox
Now that you know the primary differences between a data engineer and a data scientist, get ready to explore the data engineer's toolbox! Learn in detail about different types of databases data engineers use, how parallel computing is a cornerstone of the data engineer's toolkit, and how to schedule data processing jobs using scheduling frameworks.
3
Extract, Transform and Load (ETL)
Having been exposed to the toolbox of data engineers, it's now time to jump into the bread and butter of a data engineer's workflow! With ETL, you will learn how to extract raw data from various sources, transform this raw data into actionable insights, and load it into relevant databases ready for consumption!
4
Case Study: DataCamp
Cap off all that you've learned in the previous three chapters by completing a real-world data engineering use case from DataCamp! You will perform and schedule an ETL process that transforms raw course rating data, into actionable course recommendations for DataCamp students!
Introduction to Data Engineering
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