DEV-BL-343 | Spark  Developer (Blended)

DEV-BL-343 | Spark Developer (Blended)

This course introduces the Apache Spark distributed computing engine, and is suitable for developers, data analysts, architects, technical managers, and anyone who needs to use Spark in a hands-on manner.

About this course

Subject-Matter Expert Self-Paced Live Micro-Lessons

Overview:
This course introduces the Apache Spark distributed computing engine, and is suitable for developers, data analysts, architects, technical managers, and anyone who needs to use Spark in a hands-on manner. It is based on the Spark 2.x release.The course provides a solid technical introduction to the Spark architecture and how Spark works. It covers the basic building blocks of Spark (e.g. RDDs and the distributed compute engine), as well as higher-level constructs that provide a simpler and more capable interface.It includes in-depth coverage of Spark SQL, DataFrames, and DataSets, which are now the preferred programming API. This includes exploring possible performance issues and strategies for optimization. Thecourse also covers more advanced capabilities such as the use of Spark Streaming to process streaming data, and integrating with the Kafka server. Regularly scheduled live micro-learning sessions will also be delivered by Hortonworks University Instructors to discuss various course related topics to further enhance and supplement the self-paced content.

Target Audience:
Software engineers that are looking to develop in-memory applications for time sensitive and highly iterative applications in an Enterprise HDP environment.

Prerequisites:
Students should be familiar with programming principles and have previous experience in software development using Scala. Previous experience with data streaming, SQL, and HDP is also helpful, but not required.

Format:
Self-Paced Lesson Slides (These slides do not contain audio, narration or video)
Hands-On Lab Exercises (PDF Lab Guides)
Regularly Scheduled Live Micro-learning Sessions (Check the live session schedule for upcoming dates)

Note: In order to perform the hands-on lab exercises for this course, you will need to create your own Amazon AWS account. Cluster setup instructions are provided.

Credit Hours: 4


(DEV-343-REV1.3-041318)

 

Curriculum

  • Live Micro-Learning Sessions
  • Blended Learning: Spark Developer | Live Micro-Learning Session #1
  • Blended Learning: Spark Developer | Live Micro-Learning Session #2
  • Blended Learning: Spark Developer | Live Micro-Learning session #3
  • Blended Learning: Spark Developer | Live Micro-Learning session #4
  • Blended Learning: Spark Developer | Live Micro-Learning session #5
  • Recorded Live Micro-Learning Sessions
  • Recorded Micro-Session #1 and #2 (Important Note)
  • Recorded Micro-Session #3
  • Recorded Micro-Session #4
  • Recorded Micro-Session #5
  • Spark Lab Files
  • Download Spark the Lab Files
  • Lesson PDF
  • Download All Lesson Slides for Offline Viewing
  • IMPORTANT - Reaching the Spark UI.pdf
  • Lesson 1:
  • A Scala Primer
  • Lesson Review
  • Lab 0.1 - Set up lab environment.pdf
  • Lab 1.1 - Start Interpreter.pdf
  • Lesson 2:
  • An Introduction to Spark
  • Lesson Review
  • Lab 2.1 - First Look at Spark (Optional).pdf
  • Lab 2.2 - Spark Shell.pdf
  • Lesson 3:
  • RDDs and Spark Architecture
  • Lesson Review
  • Lab 3.1 - RDD Basics operations.pdf
  • Lab 3.2 - Operations On Multiple RDDs.pdf
  • Lesson 4:
  • Spark SQL, DataFrames and Datasets
  • Lesson Review
  • Lab 4.1 - Data Formats.pdf
  • Lab 4.2 - Spark SQL Basics.pdf
  • Lab 4.3 - DataFrame Transformations.pdf
  • Lab 4.4 - The Dataset Typed API.pdf
  • Lab 4.5 - Splitting Text Data.pdf
  • Lesson 5:
  • Shuffling Transformations and Performance
  • Lesson Review
  • Lab 5.1 - Exploring Grouping.pdf
  • Lab 5.2 - Seeing Catalyst at Work.pdf
  • Lab 5.3 - Seeing Tungsten at Work.pdf
  • Lesson 6:
  • Performance Tuning
  • Lesson Review
  • Lab 6.1 - Caching.pdf
  • Lab 6.2 - Joins and Broadcasts.pdf
  • Lesson 7:
  • Creating Stand Alone Applications
  • Lesson Review
  • Lab 7.1 - Spark Job Submission.pdf
  • Lab 7.2 - More Complex Spark Standalong Appliction (Optional).pdf
  • Lesson 8:
  • Spark Streaming Overview
  • Lesson Review
  • Lab 8.1 - Spark Streaming (1.0+).pdf
  • Lab 8.2 - Spark Structured Streaming (2.0+).pdf
  • Lab 8.3- Spark Structured Streaming with Kafka (2.0+).pdf
  • Spark 2.3 Structured Streaming Integration with Apache Kafka :: Problem and Solution.pdf
  • Bonus Lab
  • Lab - Moving Average (Optional)
  • Additional Resources
  • Why Scala? (Optional)
  • The Apache Spark Definitive Guide Excerpt (Optional)
  • Give Us Your Feedback
  • Course Survey

About this course

Subject-Matter Expert Self-Paced Live Micro-Lessons

Overview:
This course introduces the Apache Spark distributed computing engine, and is suitable for developers, data analysts, architects, technical managers, and anyone who needs to use Spark in a hands-on manner. It is based on the Spark 2.x release.The course provides a solid technical introduction to the Spark architecture and how Spark works. It covers the basic building blocks of Spark (e.g. RDDs and the distributed compute engine), as well as higher-level constructs that provide a simpler and more capable interface.It includes in-depth coverage of Spark SQL, DataFrames, and DataSets, which are now the preferred programming API. This includes exploring possible performance issues and strategies for optimization. Thecourse also covers more advanced capabilities such as the use of Spark Streaming to process streaming data, and integrating with the Kafka server. Regularly scheduled live micro-learning sessions will also be delivered by Hortonworks University Instructors to discuss various course related topics to further enhance and supplement the self-paced content.

Target Audience:
Software engineers that are looking to develop in-memory applications for time sensitive and highly iterative applications in an Enterprise HDP environment.

Prerequisites:
Students should be familiar with programming principles and have previous experience in software development using Scala. Previous experience with data streaming, SQL, and HDP is also helpful, but not required.

Format:
Self-Paced Lesson Slides (These slides do not contain audio, narration or video)
Hands-On Lab Exercises (PDF Lab Guides)
Regularly Scheduled Live Micro-learning Sessions (Check the live session schedule for upcoming dates)

Note: In order to perform the hands-on lab exercises for this course, you will need to create your own Amazon AWS account. Cluster setup instructions are provided.

Credit Hours: 4


(DEV-343-REV1.3-041318)

 

Curriculum

  • Live Micro-Learning Sessions
  • Blended Learning: Spark Developer | Live Micro-Learning Session #1
  • Blended Learning: Spark Developer | Live Micro-Learning Session #2
  • Blended Learning: Spark Developer | Live Micro-Learning session #3
  • Blended Learning: Spark Developer | Live Micro-Learning session #4
  • Blended Learning: Spark Developer | Live Micro-Learning session #5
  • Recorded Live Micro-Learning Sessions
  • Recorded Micro-Session #1 and #2 (Important Note)
  • Recorded Micro-Session #3
  • Recorded Micro-Session #4
  • Recorded Micro-Session #5
  • Spark Lab Files
  • Download Spark the Lab Files
  • Lesson PDF
  • Download All Lesson Slides for Offline Viewing
  • IMPORTANT - Reaching the Spark UI.pdf
  • Lesson 1:
  • A Scala Primer
  • Lesson Review
  • Lab 0.1 - Set up lab environment.pdf
  • Lab 1.1 - Start Interpreter.pdf
  • Lesson 2:
  • An Introduction to Spark
  • Lesson Review
  • Lab 2.1 - First Look at Spark (Optional).pdf
  • Lab 2.2 - Spark Shell.pdf
  • Lesson 3:
  • RDDs and Spark Architecture
  • Lesson Review
  • Lab 3.1 - RDD Basics operations.pdf
  • Lab 3.2 - Operations On Multiple RDDs.pdf
  • Lesson 4:
  • Spark SQL, DataFrames and Datasets
  • Lesson Review
  • Lab 4.1 - Data Formats.pdf
  • Lab 4.2 - Spark SQL Basics.pdf
  • Lab 4.3 - DataFrame Transformations.pdf
  • Lab 4.4 - The Dataset Typed API.pdf
  • Lab 4.5 - Splitting Text Data.pdf
  • Lesson 5:
  • Shuffling Transformations and Performance
  • Lesson Review
  • Lab 5.1 - Exploring Grouping.pdf
  • Lab 5.2 - Seeing Catalyst at Work.pdf
  • Lab 5.3 - Seeing Tungsten at Work.pdf
  • Lesson 6:
  • Performance Tuning
  • Lesson Review
  • Lab 6.1 - Caching.pdf
  • Lab 6.2 - Joins and Broadcasts.pdf
  • Lesson 7:
  • Creating Stand Alone Applications
  • Lesson Review
  • Lab 7.1 - Spark Job Submission.pdf
  • Lab 7.2 - More Complex Spark Standalong Appliction (Optional).pdf
  • Lesson 8:
  • Spark Streaming Overview
  • Lesson Review
  • Lab 8.1 - Spark Streaming (1.0+).pdf
  • Lab 8.2 - Spark Structured Streaming (2.0+).pdf
  • Lab 8.3- Spark Structured Streaming with Kafka (2.0+).pdf
  • Spark 2.3 Structured Streaming Integration with Apache Kafka :: Problem and Solution.pdf
  • Bonus Lab
  • Lab - Moving Average (Optional)
  • Additional Resources
  • Why Scala? (Optional)
  • The Apache Spark Definitive Guide Excerpt (Optional)
  • Give Us Your Feedback
  • Course Survey