In this chapter, you’ll learn what data engineering is and why demand for them is increasing. You’ll then discover where data engineering sits in relation to the data science lifecycle, how data engineers differ from data scientists, and have an introduction to your first complete data pipeline.

Data engineering and big data

Go with the flow

Not responsible

Big time

Data engineers vs. data scientists

Tell me the truth

Who is it

The data pipeline

It's not true

Pipeline

What is data engineering?

It’s time to talk about data storage—one of the main responsibilities for a data engineer. In this chapter, you’ll learn how data engineers manage different data structures, work in SQL—the programming language of choice for querying and storing data, and implement appropriate data storage solutions with data lakes and data warehouses.

Data structures

Structures

What's the difference

SQL databases

We can work it out

Columns

Different breeds

Data warehouses and data lakes

Tell the truth

Our warehouse (in the middle of our street)

Storing data

Data engineers make life easy for data scientists by preparing raw data for analysis using different processing techniques at different steps. These steps need to be combined to create pipelines, which is when automation comes into play. Finally, data engineers use parallel and cloud computing to keep pipelines flowing smoothly.

Processing data

Options

Extract

Transform

Load

Connect the dots

Scheduling data

Manual

Time

Condition

Schedules

Batch

Stream

One or the other

Parallel computing

Whenever, whenever

Right

Wrong

Parallel universe

Cloud computing

Obscured by clouds

Computing

Databases

Somewhere I belong

We are the champions

Moving and processing data

Lexicon

Course Glossary: Understanding Data Engineering

In 2019, the average salary for data engineers overtook data scientists. How did this happen? Companies wanting to find the gold within their data realized it wasn’t possible if they hadn’t yet built the mine. Data engineers lay the foundations that make data science possible.
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In this course, you’ll learn about a data engineer’s core responsibilities, how they differ from data scientists, and facilitate the flow of data through an organization. Through hands-on exercises you’ll follow Spotflix, a fictional music streaming company, to understand how their data engineers collect, clean, and catalog their data. By the end of the course, you’ll understand what your company's data engineers do, be ready to have a conversation with a data engineer, and have a solid foundation to start your own data engineer journey.

The videos contain live transcripts, which can be accessed by clicking "Show transcript" at the bottom left of the videos.

The course glossary can be found on the right in the resources section.

To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.

<h2> Understand the Basics of Data Engineering </h2>
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In this course, you’ll learn about a data engineer’s core responsibilities, how they differ from data scientists, and facilitate the flow of data through an organization. Through hands-on exercises you’ll follow Spotflix, a fictional music streaming company, to understand how their data engineers collect, clean, and catalog their data. 
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<h2> Apply in Personal Cases</h2>
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By the end of the course, you’ll understand what your company's data engineers do, be ready to have a conversation with a data engineer, and have a solid foundation to start your own data engineer journey.

Discover how data engineers lay the groundwork that makes data science possible. No coding involved!

Data Engineering for Everyone

Understanding Data Engineering

Yardımcı Veri Mühendisi SQL içinde

Veri Analisti Databricks'te

Veri Konularını Anlama

Running the song encoding pipeline, because engineering changed the encoder and wants to make sure they still pass the validation check.

Generating the Spotflix Weekly Playlist from Chapter 1 every Monday at 00:00 AM.

Processing music videos uploaded by artists every hour.

Collecting data from Google Analytics every morning to check how the promotion campaign is going.

Running validation checks if new videos are being collected.

Updating the number of followers in a playlist table after a user subscribed to it.

Schedules

Understanding Data Engineering

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