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Noah Gift
and
Alfredo Deza
Master Data Engineering on Databricks Lakehouse Platform
- Learn Databricks architecture, cluster management & notebook analysis
- Build reliable ETL pipelines with Delta Lake for data transformation
- Implement advanced data processing techniques with Apache Spark
Course Highlights:
Read more
Master Data Engineering on Databricks Lakehouse Platform
- Learn Databricks architecture, cluster management & notebook analysis
- Build reliable ETL pipelines with Delta Lake for data transformation
- Implement advanced data processing techniques with Apache Spark
Course Highlights:
Read more
Master Data Engineering on Databricks Lakehouse Platform
- Learn Databricks architecture, cluster management & notebook analysis
- Build reliable ETL pipelines with Delta Lake for data transformation
- Implement advanced data processing techniques with Apache Spark
Course Highlights:
- Create & scale Databricks clusters for workloads
- Load data from diverse sources into notebooks
- Explore, visualize & profile datasets with notebooks
- Version control & share notebooks via Git integration
- Read & ingest data in various file formats
- Transform data with SQL & DataFrame operations
- Handle complex data types like arrays, structs, timestamps
- Deduplicate, join & flatten nested data structures
- Identify & fix data quality issues with UDFs
- Load cleansed data into Delta Lake for reliability
- Build production-ready pipelines with Delta Live Tables
- Schedule & monitor workloads using Databricks Jobs
- Secure data access with Unity Catalog
Gain comprehensive skills in data engineering on Databricks through hands-on labs, real-world projects and best practices for the modern data lakehouse.
Register for this course and see more details by visiting:
OpenCourser.com/course/ujfzl3/data
What's inside
Learning objectives
- Use databricks for data engineering and ml workloads
- Create and design ml pipelines
- Use llamafile and other local llms like mixtral
- Use databricks for data engineering and ml workloads
- Create and design ml pipelines
- Use llamafile and other local llms like mixtral
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores advanced data processing techniques, which are highly relevant to professionals engaging in data transformation and engineering
Introduces key components of Databricks, such as clusters and notebooks, which are essential for practical use
Offers hands-on experience through real-world projects, providing learners with valuable practical skills
Provides in-depth coverage of Delta Lake for reliable data storage, aligning with industry standards
Taught by experienced instructors in the field, providing learners with access to expert knowledge
Save this course
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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 Data Engineering with Databricks with these
activities:
Review Data Structures and Algorithms
Show steps
Refresh your knowledge of data structures and algorithms to strengthen your foundation for data engineering concepts and improve your problem-solving skills.
Browse courses on
Data Structures
Show steps
-
Review concepts like arrays, linked lists, stacks, queues, graphs, and trees
-
Practice implementing these data structures and algorithms using your preferred programming language
Connect with Data Engineering Experts
Show steps
Enhance your learning by seeking guidance from experienced data engineers to gain insights, clarify doubts, and expand your knowledge.
Browse courses on
Data Engineering
Show steps
-
Identify potential mentors in your network or online communities
-
Reach out and introduce yourself, expressing your interest in data engineering and your desire for mentorship
-
Schedule regular meetings or discussions to ask questions and receive feedback
Attend Data Engineering Meetups or Conferences
Show steps
Broaden your network and gain exposure to current trends and best practices in data engineering by attending industry events.
Browse courses on
Networking
Show steps
-
Research and identify relevant meetups or conferences
-
Register for the event and prepare to actively participate
-
Attend sessions, connect with other attendees, and engage in discussions
Five other activities
Expand to see all activities and additional details
Show all eight activities
Spark Tutorial: Getting Started with Spark for Data Engineering
Show steps
Reinforce your understanding of Spark concepts by following a guided tutorial that provides clear explanations and step-by-step instructions.
Browse courses on
Spark
Show steps
-
Access the tutorial and set up your environment
-
Follow the tutorial's instructions to create a Spark session
-
Load data into a Spark DataFrame and explore its structure
-
Perform basic data transformations and aggregations
Delta Lake Exercises: Hands-on Practice with Delta Lake Operations
Show steps
Solidify your understanding of Delta Lake operations through hands-on exercises that provide a deeper dive into its capabilities.
Browse courses on
Data Engineering
Show steps
-
Create a Delta Lake table and load data into it
-
Perform CRUD (Create, Read, Update, Delete) operations on the Delta Lake table
-
Explore time travel and versioning features of Delta Lake
-
Analyze the performance of Delta Lake operations
Design and Implement a Data Pipeline with Databricks
Show steps
Apply your knowledge by designing and implementing a data pipeline using Databricks, showcasing your ability to integrate various components and automate data processing tasks.
Browse courses on
Data Engineering
Show steps
-
Define the scope and requirements of the data pipeline
-
Design the pipeline architecture and data flow
-
Implement the pipeline using Databricks components like Spark, Delta Lake, and Databricks Jobs
-
Test and validate the pipeline's functionality
-
Deploy and monitor the pipeline in production
Write a Blog Post on a Data Engineering Topic
Show steps
Deepen your understanding of a specific data engineering topic by researching, writing, and sharing your knowledge through a blog post, enhancing your communication and documentation skills.
Browse courses on
Blogging
Show steps
-
Choose a topic of interest and conduct thorough research
-
Outline your post and write a compelling introduction
-
Develop the body of your post, providing clear explanations, examples, and insights
-
Proofread and finalize your post, ensuring clarity and accuracy
-
Publish your post on a relevant platform and share it with your network
Data Lakehouse Project: Build a Data Platform for a Real-World Use Case
Show steps
Challenge yourself by building a data platform using Databricks that solves a real-world data engineering problem, giving you a comprehensive understanding of the practical applications of the concepts learned in the course.
Browse courses on
Data Engineering
Show steps
-
Identify a real-world data engineering problem
-
Design the architecture and components of the data platform
-
Implement the platform using Databricks technologies like Spark, Delta Lake, and Unity Catalog
-
Integrate external data sources and ensure data quality
-
Build data processing and visualization pipelines
-
Evaluate the platform's performance and make enhancements
Review Data Structures and Algorithms
Show steps
Refresh your knowledge of data structures and algorithms to strengthen your foundation for data engineering concepts and improve your problem-solving skills.
Browse courses on
Data Structures
Show steps
Show steps
Show steps
- Review concepts like arrays, linked lists, stacks, queues, graphs, and trees
- Practice implementing these data structures and algorithms using your preferred programming language
Connect with Data Engineering Experts
Show steps
Enhance your learning by seeking guidance from experienced data engineers to gain insights, clarify doubts, and expand your knowledge.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Identify potential mentors in your network or online communities
- Reach out and introduce yourself, expressing your interest in data engineering and your desire for mentorship
- Schedule regular meetings or discussions to ask questions and receive feedback
Attend Data Engineering Meetups or Conferences
Show steps
Broaden your network and gain exposure to current trends and best practices in data engineering by attending industry events.
Browse courses on
Networking
Show steps
Show steps
Show steps
- Research and identify relevant meetups or conferences
- Register for the event and prepare to actively participate
- Attend sessions, connect with other attendees, and engage in discussions
Five other activities
Expand to see all activities and additional details
Show all eight activities
Spark Tutorial: Getting Started with Spark for Data Engineering
Show steps
Reinforce your understanding of Spark concepts by following a guided tutorial that provides clear explanations and step-by-step instructions.
Browse courses on
Spark
Show steps
Show steps
Show steps
- Access the tutorial and set up your environment
- Follow the tutorial's instructions to create a Spark session
- Load data into a Spark DataFrame and explore its structure
- Perform basic data transformations and aggregations
Delta Lake Exercises: Hands-on Practice with Delta Lake Operations
Show steps
Solidify your understanding of Delta Lake operations through hands-on exercises that provide a deeper dive into its capabilities.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Create a Delta Lake table and load data into it
- Perform CRUD (Create, Read, Update, Delete) operations on the Delta Lake table
- Explore time travel and versioning features of Delta Lake
- Analyze the performance of Delta Lake operations
Design and Implement a Data Pipeline with Databricks
Show steps
Apply your knowledge by designing and implementing a data pipeline using Databricks, showcasing your ability to integrate various components and automate data processing tasks.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Define the scope and requirements of the data pipeline
- Design the pipeline architecture and data flow
- Implement the pipeline using Databricks components like Spark, Delta Lake, and Databricks Jobs
- Test and validate the pipeline's functionality
- Deploy and monitor the pipeline in production
Write a Blog Post on a Data Engineering Topic
Show steps
Deepen your understanding of a specific data engineering topic by researching, writing, and sharing your knowledge through a blog post, enhancing your communication and documentation skills.
Browse courses on
Blogging
Show steps
Show steps
Show steps
- Choose a topic of interest and conduct thorough research
- Outline your post and write a compelling introduction
- Develop the body of your post, providing clear explanations, examples, and insights
- Proofread and finalize your post, ensuring clarity and accuracy
- Publish your post on a relevant platform and share it with your network
Data Lakehouse Project: Build a Data Platform for a Real-World Use Case
Show steps
Challenge yourself by building a data platform using Databricks that solves a real-world data engineering problem, giving you a comprehensive understanding of the practical applications of the concepts learned in the course.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Identify a real-world data engineering problem
- Design the architecture and components of the data platform
- Implement the platform using Databricks technologies like Spark, Delta Lake, and Unity Catalog
- Integrate external data sources and ensure data quality
- Build data processing and visualization pipelines
- Evaluate the platform's performance and make enhancements
Career center
Learners who complete Data Engineering with Databricks will develop knowledge and skills
that may be useful to these careers:
Data Management Consultant
Data Management Consultant
This course is highly relevant for those looking to enter the field of data management consulting. As a data management consultant, you'll be responsible for helping organizations manage their data assets effectively. This course will provide you with a strong understanding of data engineering principles and best practices, which will enable you to advise clients on how to improve their data management practices. Additionally, the data management consulting field is growing rapidly, meaning there will be a high demand for qualified professionals with the skills you'll gain from this course.
Data Integration Architect
Data Integration Architect
For those interested in a career as a data integration architect, this course will provide you with a strong foundation in data engineering and data integration. You'll learn how to design and implement data integration solutions that can seamlessly connect different data sources. This knowledge will give you a competitive edge when applying for data integration architect jobs, as you'll have a deep understanding of the challenges and solutions involved in data integration.
Data Engineer
Data Engineer
If you want to work with large datasets and have a solid understanding of data engineering, this course is a great fit. It will teach you how to use Apache Spark and Databricks to build reliable ETL pipelines and implement advanced data processing techniques. You'll also learn how to handle complex data types and identify and fix data quality issues. This will give you a competitive edge when applying for data engineering jobs.
Data Governance Analyst
Data Governance Analyst
A data governance analyst is responsible for developing and implementing data governance policies and procedures. This course will provide you with a solid understanding of data governance principles and best practices. You'll learn how to create a data governance framework, as well as how to assess and mitigate data governance risks.
Big Data Architect
Big Data Architect
This course is well-suited for those who want to build a career as a big data architect, as it provides a comprehensive overview of big data technologies and their applications. You'll learn how to design and implement big data solutions that can handle the volume, variety, and velocity of big data. You'll also gain experience with popular big data tools such as Apache Spark and Hadoop.
Software Engineer
Software Engineer
This course is a great starting point for software engineers looking to enter the field of data engineering. You'll learn the fundamentals of data engineering, including data modeling, data warehousing, and data analysis. This knowledge will give you a competitive edge when applying for software engineering jobs in the data engineering field.
Machine Learning Engineer
Machine Learning Engineer
This course provides a strong foundation for those who want to start or advance a career in machine learning engineering. It covers a wide range of ML concepts and techniques, including how to create and deploy ML models. The course also provides hands-on experience with popular ML libraries such as TensorFlow and Keras. By completing this course, you will be well-prepared to apply for ML engineering jobs.
Database Administrator
Database Administrator
As a database administrator, you'll be responsible for managing and maintaining databases. This course will provide you with a solid foundation in data engineering and database management, giving you the skills you need to succeed in this role. You'll learn how to design and implement database solutions, as well as how to monitor and troubleshoot database performance.
Business Intelligence Analyst
Business Intelligence Analyst
Taking this course will be helpful for those pursuing a career as a business intelligence analyst, as it provides a strong foundation in data engineering and data analysis. Through hands-on labs, you'll learn how to use Apache Spark to process and analyze big data, and how to visualize data using popular BI tools. This real-world experience will give you an advantage when applying for BI analyst jobs, as you'll be able to demonstrate your skills and knowledge in these areas.
Data Analyst
Data Analyst
This course provides a good foundation for data analysts, as it covers a wide range of data analysis techniques. You'll learn how to clean and prepare data, perform exploratory data analysis, and create data visualizations. This will give you the skills you need to succeed in a data analyst role, where you'll be responsible for analyzing data to identify trends and patterns. Additionally, the data analyst role is in high demand across various industries, meaning you'll have a wide range of job opportunities to choose from.
Cloud Architect
Cloud Architect
Taking this course while pursuing a career as a cloud architect may be beneficial, as you will learn about the design and implementation of data engineering solutions in the cloud. This will give you a competitive edge when applying for cloud architect jobs, as you will have a solid understanding of the cloud computing landscape. Additionally, the cloud architect role is in high demand, meaning your chances of securing a well-paying job will be higher.
Data Scientist
Data Scientist
Learning about data engineering through this course may be useful as data scientists need to work closely with data engineers. This course can help you develop a solid understanding of data engineering principles and best practices, which will enable you to communicate more effectively with data engineers and contribute to the design and implementation of data engineering solutions in your organization.
For more career information including salaries, visit:
OpenCourser.com/course/ujfzl3/data
Reading list
We've selected 11 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
Data Engineering with Databricks.
Save
Comprehensive guide to data engineering with Databricks. It covers all the essential concepts, from cluster management to data transformation and analysis. It valuable resource for anyone who wants to learn more about data engineering with Databricks.
Provides a comprehensive introduction to data science with Python. It covers all the essential concepts, from data preparation to model training and evaluation. It valuable resource for anyone who wants to learn more about data science with Python.
Provides a comprehensive introduction to data analysis with Pandas, a popular Python library for data manipulation and analysis. It covers all the essential concepts, from data loading to data cleaning and analysis. It valuable resource for anyone who wants to learn more about data analysis with Pandas.
Save
Provides a comprehensive introduction to Apache Spark, which key component of the Databricks Lakehouse Platform.
Provides a comprehensive introduction to data visualization with Python. It covers all the essential concepts, from data exploration to data visualization and presentation. It valuable resource for anyone who wants to learn more about data visualization with Python.
Save
Provides a comprehensive introduction to deep learning. It covers all the essential concepts, from neural networks to deep learning architectures. It valuable resource for anyone who wants to learn more about deep learning.
Provides a comprehensive introduction to reinforcement learning. It covers all the essential concepts, from Markov decision processes to deep reinforcement learning. It valuable resource for anyone who wants to learn more about reinforcement learning.
Provides a hands-on introduction to Spark, including real-world examples and best practices.
Provides a comprehensive introduction to PyTorch for deep learning. It covers all the essential concepts, from neural networks to deep learning architectures. It valuable resource for anyone who wants to learn more about PyTorch for deep learning.
Provides a comprehensive introduction to Keras for deep learning. It covers all the essential concepts, from neural networks to deep learning architectures. It valuable resource for anyone who wants to learn more about Keras for deep learning.
Provides a comprehensive introduction to machine learning from a Bayesian and optimization perspective. It covers all the essential concepts, from Bayesian inference to optimization algorithms. It valuable resource for anyone who wants to learn more about machine learning from a Bayesian and optimization perspective.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ujfzl3/data
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Effort
3 to 6 hours per week for 3 weeks
Level
Introductory
Via
edX
Institution
Pragmatic AI Labs
Instructors
Noah Gift
Alfredo Deza
Session
March 12, 2024
- March 13, 2025
Language
English
Transcript
English
Good to know
Explores advanced data processing techniques, which are highly relevant to professionals engaging in data transformation and engineering
Introduces key components of Databricks, such as clusters and notebooks, which are essential for practical use
Offers hands-on experience through real-world projects, providing learners with valuable practical skills
Provides in-depth coverage of Delta Lake for reliable data storage, aligning with industry standards
Taught by experienced instructors in the field, providing learners with access to expert knowledge
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