Get hands-on experience
Through narrated demonstrations and hands-on exercises, learners achieve proficiency in CDSW and develop the skills required to:
- Navigate CDSW’s options and interfaces with confidence
- Create projects in CDSW and collaborate securely with other users and teams
- Develop and run reproducible Python and R code
- Customize projects by installing packages and setting environment variables
- Connect to a secure (Kerberized) Cloudera or Hortonworks cluster
- Work with large-scale data using Apache Spark 2 with PySpark and sparklyr
- Perform end-to-end machine learning workflows in CDSW using Python or R (read, inspect, transform, visualize, and model data)
- Measure, track, and compare machine learning models using CDSW’s Experiments capability
- Deploy models as REST API endpoints serving predictions using CDSW’s Models capability
- Work collaboratively using CDSW together with Git
What To Expect
This course is designed for learners at organizations using CDSW under an enterprise license or a trial license. The learner must have access to a CDSW environment on a Cloudera or Hortonworks cluster running Apache Spark 2. Some experience with data science using Python or R is helpful but not required. No prior knowledge of Spark or other Hadoop ecosystem tools is required.
Book the course
How would you like to train?
- How would you like to train?
- Classroom
- Virtual Classroom
- On-Demand
- Private
Course Outline
Overview of CDSW
- Introduction to CDSW
- Who Can Use CDSW
- How to Access CDSW
- Navigating around CDSW
- User Settings
- Hadoop Authentication
Projects in CDSW
- Creating a New Project
- Navigating around a Project
- Project Settings
The CDSW Workbench Interface
- Using the Workbench
- Using the Sidebar
- Using the Code Editor
- Engines and Sessions
Running Python and R Code in CDSW
- Running Code
- Using the Session Prompt
- Using the Terminal
- Installing Packages
- Using Markdown in Comments
Using Apache Spark 2 in CDSW
- Scenario and Dataset
- Copying Files to HDFS
- Interfaces to Apache Spark 2
- Connecting to Spark
- Reading Data
- Inspecting Data
Data Science and Machine Learning in CDSW
- Transforming Data
- Using SQL Queries
- Visualizing Data from Spark
- Machine Learning with MLlib
- Session History
Experiments and Models in CDSW
- Machine Learning Workflow
- Running Experiments
- Using Packages in Experiments
- Deploying Models
- Calling Models
- Using Packages in Models
Teams and Collaboration in CDSW
- Collaboration in CDSW
- Teams in CDSW
- Using Git for Collaboration
- Conclusion