Veritabanı türleri

Videoda, veritabanlarının veri mühendisleri için temel bir araç olduğunu gördün. Bir veri mühendisinin iş akışı çoğu zaman veritabanlarıyla başlar ve veritabanlarıyla biter.

Yine de, veritabanları her zaman ilk adım olmayabilir. Bazen veriler harici API'lerden veya ham dosyalardan çekilmelidir. Şemalardaki veritabanını bulabilir misin?

Bu egzersiz, kursun bir parçasıdır

Data Engineering'e Giriş

Kursa Göz Atın

Uygulamalı etkileşimli egzersiz

Teoriyi etkileşime dönüştürün, interaktif egzersizlerimizden biriyle

Egzersize başla

Bu egzersiz, kursun bir parçasıdır

Data Engineering'e Giriş

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başla

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: Veri mühendisliği nedir?Exercise 2: Veri mühendisinin görevleri Exercise 3: Data engineer mı yoksa data scientist mi?Exercise 4: Veri mühendisliği problemleri Exercise 5: Veri mühendisinin araçları Exercise 6: Veritabanı türleri

Geçerli egzersiz

Exercise 7: İşleme görevleri Exercise 8: Zamanlama araçları Exercise 9: Bulut sağlayıcıları Exercise 10: Neden bulut bilişim?Exercise 11: Bulut bilişimde büyük oyuncular Exercise 12: Bulut hizmetleri

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: Data sources Exercise 3: Fetch from an API Exercise 4: Read from a database Exercise 5: Transform Exercise 6: Splitting the rental rate Exercise 7: Prepare for transformations Exercise 8: Joining with ratings Exercise 9: Loading Exercise 10: OLAP or OLTP Exercise 11: Writing to a file Exercise 12: Load into Postgres Exercise 13: Putting it all together Exercise 14: Defining a DAG Exercise 15: Setting up Airflow Exercise 16: Interpreting the DAG

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