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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
Traffic lights
Traffic lights
Read about what's good
what should give you pause 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
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Reviews summary
Mastering databricks data engineering
According to students, "Data Engineering with Databricks" is a highly practical and comprehensive course for mastering the Databricks Lakehouse Platform. Learners frequently commend the hands-on labs and real-world projects, especially those focusing on Delta Lake and Apache Spark for building robust ETL pipelines. The course has been regularly updated, incorporating modern features like Unity Catalog and Delta Live Tables, addressing some earlier concerns about relevancy. While largely positive, a few learners noted the pacing can be fast, suggesting some prior Spark familiarity is beneficial. Newer sections on LLMs and ML pipelines are present but some feel they could be more deeply integrated. Overall, it provides strong foundational skills for data engineering professionals.
Regularly updated to include modern Databricks features and best practices.
"I especially appreciated the updated content on Unity Catalog and Delta Live Tables, which are crucial for modern pipelines."
"It's clear the course has been updated as Databricks evolves, addressing previous concerns about relevancy."
"The instructor frequently updates the material, ensuring I learned current best practices and features."
Covers essential data engineering topics on Databricks extensively.
"This course is a phenomenal resource for anyone looking to master data engineering on Databricks."
"The depth on Delta Lake and its features is impressive, and the real-world projects are a huge plus."
"The course covers a lot of ground, from ETL with Delta Lake to Databricks Jobs and MLOps principles."
Provides highly practical exercises for real-world Databricks application.
"The hands-on labs with Delta Lake and Spark were incredibly practical and cemented my understanding."
"Absolutely brilliant! The most practical Databricks course I've taken. The hands-on sessions are unparalleled."
"The labs are well-designed and really help apply the concepts, providing crucial real-world experience."
Occasional lab environment issues noted by some users.
"I encountered a few lab environment issues that took time to troubleshoot independently."
"Lab instructions sometimes lacked detail, leading to minor setup difficulties in the Databricks environment."
"Be prepared for some independent troubleshooting to get the lab environments fully operational."
Newer sections on ML/LLMs are present but could be more deeply integrated.
"The content on LLMs and Llamafile felt a bit tacked on and not fully integrated with the core data engineering topics."
"The new sections on ML pipelines and local LLMs are interesting, though I agree they could be expanded or integrated more cohesively."
"While a nice addition, the ML/LLM content doesn't always align seamlessly with the core data engineering flow."
Pacing can be fast, benefiting those with some prior Spark familiarity.
"Some parts felt a little fast-paced, assuming a basic familiarity with Spark concepts."
"I also felt it moved too quickly through advanced Spark transformations without enough foundational context for someone newer to the ecosystem."
"Some of the initial setup instructions could be clearer for absolute beginners to Databricks."
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
Language
English
Transcript
English
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