Veri kaynakları

Önceki videoda veriyi çıkarmanın üç yolunu öğrendin:

.txt veya .csv gibi metin dosyalarından çıkarma
Hacker News API gibi web servislerinin API’lerinden çıkarma
Müşteri verileri için bir SQL uygulama veritabanı gibi bir veritabanından çıkarma

Ayrıca kısaca satır odaklı veritabanları ve OLTP’ye de değindik.

Bu konularla ilgili aşağıdaki ifadelerden hangisi doğru değildir?

Bu egzersiz

Data Engineering'e Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

İnteraktif egzersizlerimizden biriyle teoriyi pratiğe dökün

Egzersizi başlat

Bu egzersiz

Data Engineering'e Giriş

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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.

Exercise 1: What is data engineering?Exercise 2: Tasks of the data engineer Exercise 3: Data engineer or data scientist?Exercise 4: Data engineering problems Exercise 5: Tools of the data engineer Exercise 6: Kinds of databases Exercise 7: Processing tasks Exercise 8: Scheduling tools Exercise 9: Cloud providers Exercise 10: Why cloud computing?Exercise 11: Big players in cloud computing Exercise 12: Cloud services

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.

Exercise 1: Databases Exercise 2: SQL vs NoSQL Exercise 3: The database schema Exercise 4: Joining on relations Exercise 5: Star schema diagram Exercise 6: What is parallel computing Exercise 7: Why parallel computing?Exercise 8: From task to subtasks Exercise 9: Using a DataFrame Exercise 10: Parallel computation frameworks Exercise 11: Spark, Hadoop and Hive Exercise 12: A PySpark groupby Exercise 13: Running PySpark files Exercise 14: Workflow scheduling frameworks Exercise 15: Airflow, Luigi and cron Exercise 16: Airflow DAGs

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!

Exercise 1: Extract Exercise 2: Veri kaynakları

Geçerli Egzersiz

Exercise 3: Bir API'den veri çek Exercise 4: Bir veritabanından oku Exercise 5: Dönüştür Exercise 6: Kira ücretini bölme Exercise 7: Dönüşümlere hazırlan Exercise 8: Derecelendirmelerle birleştirme Exercise 9: Yükleme Exercise 10: OLAP mı OLTP mi Exercise 11: Dosyaya yazma Exercise 12: Postgres'e Yükleme Exercise 13: Hepsini bir araya getirme Exercise 14: Bir DAG Tanımlamak Exercise 15: Airflow'u kurma Exercise 16: DAG'i yorumlama

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!

Exercise 1: Course ratings Exercise 2: Exploring the schema Exercise 3: Querying the table Exercise 4: Average rating per course Exercise 5: From ratings to recommendations Exercise 6: Filter out corrupt data Exercise 7: Using the recommender transformation Exercise 8: Scheduling daily jobs Exercise 9: The target table Exercise 10: Defining the DAG Exercise 11: Enable the DAG Exercise 12: Querying the recommendations Exercise 13: Congratulations