Loved by learners at thousands of companies
Have you heard people talk about data engineers and wonder what it is they do? Do you know what data engineers do but you're not sure how to become one yourself? This course is the perfect introduction. It touches upon all things you need to know to streamline your data processing. This introductory course will give you enough context to start exploring the world of data engineering. It's perfect for people who work at a company with several data sources and don't have a clear idea of how to use all those data sources in a scalable way. Be the first one to introduce these techniques to your company and become the company star employee.
Introduction to Data EngineeringFree
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?50 xpTasks of the data engineer50 xpData engineer or data scientist?100 xpData engineering problems50 xpTools of the data engineer50 xpKinds of databases50 xpProcessing tasks50 xpScheduling tools50 xpCloud providers50 xpWhy cloud computing?50 xpBig players in cloud computing100 xpCloud services100 xp
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.Databases50 xpSQL vs NoSQL100 xpThe database schema100 xpJoining on relations100 xpStar schema diagram50 xpWhat is parallel computing50 xpWhy parallel computing?50 xpFrom task to subtasks100 xpUsing a DataFrame100 xpParallel computation frameworks50 xpSpark, Hadoop and Hive100 xpA PySpark groupby100 xpRunning PySpark files50 xpWorkflow scheduling frameworks50 xpAirflow, Luigi and cron50 xpAirflow DAGs100 xp
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!Extract50 xpData sources50 xpFetch from an API100 xpRead from a database100 xpTransform50 xpSplitting the rental rate100 xpPrepare for transformations50 xpJoining with ratings100 xpLoading50 xpOLAP or OLTP50 xpWriting to a file100 xpLoad into Postgres100 xpPutting it all together50 xpDefining a DAG100 xpSetting up Airflow50 xpInterpreting the DAG50 xp
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!Course ratings50 xpExploring the schema50 xpQuerying the table100 xpAverage rating per course100 xpFrom ratings to recommendations50 xpFilter out corrupt data100 xpUsing the recommender transformation100 xpScheduling daily jobs50 xpThe target table100 xpDefining the DAG100 xpEnable the DAG50 xpQuerying the recommendations100 xpCongratulations50 xp
In the following tracksData Engineer
Data and Software Engineer @DataCamp
Vincent has a Master's degree in Computer Science and has several years of experience scaling up the DataCamp's platform as a Software Engineer. He experienced first-hand the difficulties that come with building scalable data products. This made him passionate about teaching people how to do tackle these problems the right way.