Databricks Concepts
Offered By: DataCamp
Course Description
Overview
Learn about the power of Databricks Lakehouse and help you scale up your data engineering and machine learning skills.
This course guides you from start to finish on how the Databricks Lakehouse Platform provides a single, scalable, and performant platform for your data processes. As you work through a real-world dataset, you will learn how to accomplish a variety of tasks within the Databricks platform. Throughout this course, you will learn about and practice the different features of the Databricks Lakehouse platform and see how they can be applied to different data use cases.
This course guides you from start to finish on how the Databricks Lakehouse Platform provides a single, scalable, and performant platform for your data processes. As you work through a real-world dataset, you will learn how to accomplish a variety of tasks within the Databricks platform. Throughout this course, you will learn about and practice the different features of the Databricks Lakehouse platform and see how they can be applied to different data use cases.
Syllabus
- Welcome to Databricks
- Learn about the new lakehouse paradigm for your cloud data strategy and how the Databricks Lakehouse platform can modernize your data architecture. Understand the foundational components of the Databricks platform and how they all fit together.
- Data Engineering
- Learn how to process, transform, and clean your data using Databricks functionality. Practice using capabilities such as the Delta storage format, Delta Live Tables, and Workflows together to create an end-to-end data pipeline.
- Databricks SQL and Data Warehousing
- Use the Databricks Lakehouse platform as your data warehousing solution for your Business Intelligence (BI) use cases. Use the built-in SQL-optimized capabilities within Databricks to create queries and dashboards on your data.
- Databricks for Large-scale Applications and Machine Learning
- Use Databricks to manage your Machine Learning pipelines with managed MLFlow. Follow the model development lifecycle from end-to-end with the Feature Store, Model Registry, and Model Serving Endpoints to create a robust MLOps platform in the lakehouse.
Taught by
Kevin Barlow
Related Courses
内存数据库管理openHPI CS115x: Advanced Apache Spark for Data Science and Data Engineering
University of California, Berkeley via edX Processing Big Data with Azure Data Lake Analytics
Microsoft via edX Google Cloud Big Data and Machine Learning Fundamentals en Español
Google Cloud via Coursera Google Cloud Big Data and Machine Learning Fundamentals 日本語版
Google Cloud via Coursera