HDP Developer: Quick Start Training in United States of America

Developers and data engineers who need to understand and develop applications on HDP.

Part I: High Level Overview

• Describe the Case for Hadoop
• Identify the Hadoop Ecosystem via architectural categories

Part II: Deeper Look & Demos (2 hrs)

• Detail the HDFS architecture
• Describe data ingestion options and frameworks for batch and real-time streaming
• Explain the fundamentals of parallel processing
• Detail the architecture and features of YARN
• Understand backup and recovery options
• Describe how to secure Hadoop

Live Demonstrations

• Operational overview with Ambari
• Loading data into HDFS

Objectives

• Use Pig to explore and transform data in HDFS
• Transfer data between Hadoop and a relational database
• Understand how Hive tables are defined and implemented
• Use Hive to explore and analyze data sets
• Explain and use the various Hive file formats
• Create and populate a Hive table that uses ORC file formats
• Use Hive to run SQL-like queries to perform data analysis
• Use Hive to join datasets using a variety of techniques
• Write efficient Hive queries
• Explain the uses and purpose of HCatalog
• Use HCatalog with Pig and Hive

Hands-On Labs

• Use HDFS commands to add/remove files and folders
• Use Sqoop to transfer data between HDFS and a RDBMS
• Explore, transform, split and join datasets using Pig
• Use Pig to transform and export a dataset for use with Hive
• Use HCatLoader and HCatStorer
• Use Hive to discover useful information in a dataset
• Describe how Hive queries get executed as MapReduce jobs
• Perform a join of two datasets with Hive
• Use advanced Hive features: windowing, views, ORC files

Objectives

• Describe Spark and Spark specific use cases
• Explore data interactively through the spark shell utility
• Explain the RDD concept
• Understand concepts of functional programming
• Use the Python or Scala Spark APIs
• Create all types of RDDs: Pair, Double, and Generic
• Use RDD type-specific functions
• Explain interaction of components of a Spark Application
• Explain the creation of the DAG schedule
• Build and package Spark applications
• Use application configuration items
• Deploy applications to the cluster using YARN
• Use data caching to increase performance of applications
• Understand join techniques
• Learn general application optimization guidelines/tips
• Create applications using the Spark SQL library
• Create/transform data using dataframes
• Read, use, and save to different Hadoop file formats

Spark Python or Scala Hands-On Labs

• Create a Spark “Hello World” word count application
• Use advanced RDD programming to perform sort, join, pattern matching and regex tasks
• Explore partitioning and the Spark UI
• Increase performance using data caching
• Build/package a Spark application using Maven
• Use a broadcast variable to efficiently join a small dataset to a massive dataset
• Create a data frame and perform analysis
• Load/transform/store data using Spark with Hive tables