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. 

What is data engineering?

Tasks of the data engineer

Data engineer or data scientist?

Data engineering problems

Tools of the data engineer

Kinds of databases

Processing tasks

Scheduling tools

Cloud providers

Why cloud computing?

Big players in cloud computing

Cloud services

Introduction to Data Engineering

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.

Databases

SQL vs NoSQL

The database schema

Joining on relations

Star schema diagram

What is parallel computing

Why parallel computing?

From task to subtasks

Using a DataFrame

Parallel computation frameworks

Spark, Hadoop and Hive

A PySpark groupby

Running PySpark files

Workflow scheduling frameworks

Airflow, Luigi and cron

Airflow DAGs

Data engineering toolbox

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! 

Ekstraksi

Sumber data

Mengambil dari sebuah API

Membaca dari database

Transformasi

Memisahkan rental rate

Persiapkan untuk transformasi

Melakukan join dengan ratings

Memuat

OLAP atau OLTP

Menulis ke berkas

Muat ke Postgres

Menggabungkan semuanya

Mendefinisikan DAG

Menyiapkan Airflow

Menafsirkan DAG

Extract, Transform and Load (ETL)

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! 

Course ratings

Exploring the schema

Querying the table

Average rating per course

From ratings to recommendations

Filter out corrupt data

Using the recommender transformation

Scheduling daily jobs

The target table

Defining the DAG

Enable the DAG

Querying the recommendations

Congratulations

Case Study: DataCamp

datacamp_application.sql

Pernah mendengar orang membicarakan data engineer dan bertanya-tanya apa yang mereka kerjakan? Sudah tahu peran data engineer tetapi belum yakin bagaimana menjadi salah satunya? Kursus ini adalah pengantar yang tepat. Anda akan mempelajari seluruh hal yang perlu diketahui untuk merampingkan pemrosesan data Anda. Kursus pengantar ini memberi cukup konteks untuk mulai menjelajahi dunia data engineering. Kursus ini ideal bagi Anda yang bekerja di perusahaan dengan banyak sumber data namun belum memiliki gambaran jelas tentang cara memanfaatkan semua sumber tersebut secara terukur. Jadilah yang pertama memperkenalkan teknik-teknik ini di perusahaan Anda dan raih predikat karyawan bintang.

Intermediate Python

Intermediate SQL

Pengantar Data Engineering

Pelajari dunia rekayasa data dalam kursus singkat ini, mencakup alat dan topik seperti ETL dan komputasi awan. 

Menafsirkan DAG

Pengantar Data Engineering

Latihan interaktif praktis