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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
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
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/m4fw6s/automate
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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
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
In this lesson, you'll learn about the components of a data pipeline including Directed Acyclic Graphs (DAGs). You'll practice creating data pipelines with DAGs and Apache Airflow
This lesson creates connections between Airflow and AWS first by creating credentials, then copying S3 data, leveraging connections and hooks, and building S3 data to the Redshift DAG.
Students will learn how to track data lineage and set up data pipeline schedules, partition data to optimize pipelines, investigating Data Quality issues, and write tests to ensure data quality.
In this last lesson, students will learn how to build Pipelines with maintainability and reusability in mind. They will also learn about pipeline monitoring.
Students work on a music streaming company’s data infrastructure by creating and automating a set of data pipelines with Airflow, monitoring and debugging production pipelines
Good to know
Good to know
Know what's good ,
what to watch for , 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
Save Automate Data Pipelines to your list so you can find it easily later:
Save
Save
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Review basic Python programming concepts
Show steps
Strengthen your understanding of Python fundamentals, such as data types, control flow, and functions.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Review online tutorials or documentation on Python basics
- Practice writing simple Python programs
Organize course materials and create a study guide
Show steps
Prepare effectively for the course by compiling and reviewing key materials and creating a personalized study guide.
Show steps
Show steps
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
Show steps
Familiarize yourself with open source data pipeline frameworks and tools to enhance your professional growth.
Show steps
Show steps
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
Show steps
Build a basic data pipeline using Airflow to understand the fundamental concepts and components.
Show steps
Show steps
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
Show steps
Connect with other learners, share knowledge, and collaborate on assignments to enhance understanding.
Show steps
Show steps
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
Show steps
Reinforce your understanding of Airflow concepts by solving coding problems related to data pipelines.
Show steps
Show steps
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
Show steps
Gain practical experience by designing and implementing a data pipeline for a project that meets your interests or needs.
Show steps
Show steps
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
Show steps
Share your knowledge and insights about data pipelines by creating a written piece that explains concepts or best practices.
Show steps
Show steps
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
Show steps
Gain real-world experience and support the open source community by contributing to data pipeline projects.
Show steps
Show steps
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:
For more career information including salaries, visit:
OpenCourser.com/course/m4fw6s/automate
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.
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.
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.
Provides a comprehensive guide to building data pipelines with Apache Hadoop, covering topics such as data ingestion, transformation, and data storage.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/m4fw6s/automate
Share
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Effort
4 weeks
Level
Intermediate
Via
Udacity
Instructors
Sean Murdock
Matt Swaffer
Ben Goldberg
Amanda Moran
Valerie Scarlata
Language
English
Good to know
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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