Apache Spark (Basic) on Hadoop
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Overview:
This three-day course covers the essentials for developers who need to create applications to analyze big data stored in Apache Hadoop using Spark. Each student has their own Spark cluster and have access to dozens of hands-on labs.
Duration
3 days
Who is the course for
Data Scientists and Software Developers
Prerequisites
To get the most out of this training, you should have the following knowledge or experience as they will not be discussed during class:
- Hadoop Distributed File System (HDFS), YARN (Yet Another Resource Manager) and MapReduce processing engine
- Scala or Python coding
- Linux command line experience
Course Outline
Module 0. Introduction and Setup:
- How to start Spark and Zeppelin services in Ambari
- How to login to Spark using Python and Scala
Module 1. Spark Architecture:
- What is Apache Spark?
- Spark processing (Jobs, Stages, Tasks)
- Spark components (Driver, Context, Yarn, HDFS, Workers, Executors)
Module 2. Getting Started with RDDs:
- Running queues in Python, Scala and Zeppelin
- Queries using most popular Transformations and Actions
- Creating RDDs
Module 3. Pair RDDs:
- Difference between RDDs and Pair RDD
- 1 Pair Actions, 1 Pair Transformations and 2 Pair Transformations
Module 4. Spark SQL:
- Working with DataFrames and Tables and DataSets
- Catalyst optimizer overview
Module 5. Spark Streaming):
- Working with DStreams
- Stateless and Stateful Streaming labs using HDFS and Sockets
Module 6. Visualizations using Zeppelin:
- Creating various Charts using DataFrames and Tables
- How to create Pivot charts and Dynamic forms
Module 7. Spark UI
- Overview of Job, Stage and Tasks
- Monitoring Spark jobs in Spark UI
Module 8. Performance Tuning:
- Caching, Checkpoint, Accumulators and Broadcast Variables
- Hashed Partitions, Tungsten, Executor memory and Serialization
Module 9. Spark Applications
- Creating an application via spark-submit
- Parameter configurations (number executors, driver memory, executor cores, etc.)
Module 10. Spark 2.0 Machine Learning (ML)
- How ML Pipelines work
- Making Predictions using Decision Tree
Format
Lecture / Lab
Additional Information
The course content can be customised to cover any specialised material you may require for your specific training needs.
This course can be offered as private on-site training hosted at your offices. For more information, please contact us at info@unicom.co.uk
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