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Automate Data Pipelines

Sean Murdock, Matt Swaffer, Ben Goldberg, Amanda Moran, and Valerie Scarlata

Create streamlined data pipelines with Airflow and learn best practices with Udacity's Automated Data Pipelines Training Course. Enroll today & grow your career

Prerequisite details

To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:

  • Data modeling basics
  • Intermediate Python
  • Database fundamentals
  • Intermediate SQL
  • Amazon web services basics
  • Command line interface basics
Read more

Create streamlined data pipelines with Airflow and learn best practices with Udacity's Automated Data Pipelines Training Course. Enroll today & grow your career

Prerequisite details

To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:

  • Data modeling basics
  • Intermediate Python
  • Database fundamentals
  • Intermediate SQL
  • Amazon web services basics
  • Command line interface basics

You will also need to be able to communicate fluently and professionally in written and spoken English.

Enroll now

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What's inside

Syllabus

Welcome to Automating Data Pipelines. In this lesson, you'll be introduced to the topic, prerequisites for the course, and the environment and tools you'll be using to build data pipelines.
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Examines important data engineering concepts like data modeling, SQL, and AWS, which are crucial for data engineering
Leverages Airflow, a widely-used orchestration tool in the industry for building data pipelines
Suitable for junior data engineers or aspiring data professionals looking to enhance their data engineering skills
Taught by seasoned instructors with extensive experience in data engineering and data pipelines, ensuring high-quality instruction
Provides hands-on experience through interactive exercises and a capstone project, allowing learners to apply their knowledge practically
Covers essential topics in data pipeline engineering, including data lineage, data quality, and pipeline monitoring

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Automate Data Pipelines with these activities:
Review basic Python programming concepts
Strengthen your understanding of Python fundamentals, such as data types, control flow, and functions.
Browse courses on Python Basics
Show steps
  • Review online tutorials or documentation on Python basics
  • Practice writing simple Python programs
Organize course materials and create a study guide
Prepare effectively for the course by compiling and reviewing key materials and creating a personalized study guide.
Show steps
  • Review the course syllabus and identify core concepts
  • Collect and organize notes, assignments, and quizzes from lectures and discussions
  • Create a study guide that summarizes and connects the key concepts
Explore open source data pipeline tools
Familiarize yourself with open source data pipeline frameworks and tools to enhance your professional growth.
Show steps
  • Identify popular open source data pipeline tools like Apache Airflow, Luigi, and Prefect
  • Review documentation and tutorials on the selected tools
  • Experiment with the tools by building simple data pipelines
Six other activities
Expand to see all activities and additional details
Show all nine activities
Create a data pipeline from scratch
Build a basic data pipeline using Airflow to understand the fundamental concepts and components.
Show steps
  • Set up a development environment with Airflow
  • Create a simple DAG with tasks to read data from a source, transform it, and load it to a destination
  • Schedule the DAG to run regularly
  • Monitor the DAG and troubleshoot any issues
Join a study group or online forum
Connect with other learners, share knowledge, and collaborate on assignments to enhance understanding.
Show steps
  • Identify relevant study groups or online forums
  • Introduce yourself and participate in discussions
  • Ask questions, share ideas, and provide peer support
Solve Airflow coding challenges
Reinforce your understanding of Airflow concepts by solving coding problems related to data pipelines.
Show steps
  • Find online coding platforms or resources that offer Airflow challenges
  • Attempt to solve the challenges on your own
  • Review solutions and compare your approach
Build a data pipeline for a real-world project
Gain practical experience by designing and implementing a data pipeline for a project that meets your interests or needs.
Show steps
  • Identify a project idea and define the scope
  • Gather the necessary data and resources
  • Design and implement the data pipeline using Airflow
  • Test and deploy the pipeline
  • Monitor and maintain the pipeline over time
Write a blog post or article on data pipelines
Share your knowledge and insights about data pipelines by creating a written piece that explains concepts or best practices.
Show steps
  • Choose a topic related to data pipelines
  • Research and gather information on the topic
  • Write and edit your blog post or article
  • Publish your content on a relevant platform
Contribute to open source data pipeline projects
Gain real-world experience and support the open source community by contributing to data pipeline projects.
Show steps
  • Identify open source data pipeline projects on platforms like GitHub
  • Review the project documentation and identify areas where you can contribute
  • Make code changes, fix bugs, or improve documentation
  • Submit your contributions for review and merge

Career center

Learners who complete Automate Data Pipelines will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected three books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Automate Data Pipelines.
Cover image
Secret Colors
Save
Provides a deep dive into the design and implementation of data-intensive applications, including topics such as data modeling, data storage, and data processing.
Secret Colors
Paperback
Check
Secret Colors
Kindle Edition
Check
Cover image
Kafka: The Definitive Guide
Save
Provides a comprehensive overview of data pipelines using Kafka. It covers the basics of data pipelines, including data sources, transformations, and destinations. It also discusses more advanced topics, such as scheduling, monitoring, and debugging data pipelines.
Kafka: The Definitive Guide: Real-Time Data and...
Paperback
$$
Kafka: The Definitive Guide
Kindle Edition
$$
Cover image
Hadoop: The Definitive Guide
Save
Provides a comprehensive guide to building data pipelines with Apache Hadoop, covering topics such as data ingestion, transformation, and data storage.
Hadoop: The Definitive Guide: Storage and Analysis...
Paperback
$$
Hadoop: The Definitive Guide
Kindle Edition
$$
Hadoop: The Definitive Guide
Paperback
$$
Hadoop: The Definitive Guide
Paperback
$$

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Effort
4 weeks
Level
Intermediate
Via
Udacity
Instructors
Sean Murdock
Matt Swaffer
Ben Goldberg
Amanda Moran
Valerie Scarlata
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Examines important data engineering concepts like data modeling, SQL, and AWS, which are crucial for data engineering
Leverages Airflow, a widely-used orchestration tool in the industry for building data pipelines
Suitable for junior data engineers or aspiring data professionals looking to enhance their data engineering skills
Taught by seasoned instructors with extensive experience in data engineering and data pipelines, ensuring high-quality instruction
Provides hands-on experience through interactive exercises and a capstone project, allowing learners to apply their knowledge practically
Covers essential topics in data pipeline engineering, including data lineage, data quality, and pipeline monitoring
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