HDP Developer: Quick Start, United Kingdom

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This training course is designed for developers who need to create applications to analyze Big Data stored in Apache Hadoop using Apache Pig and Apache Hive, and developing applications on Apache Spark.

Topics include: Essential understanding of HDP and its capabilities, Hadoop, YARN, HDFS, MapReduce/Tez, data ingestion, using Pig and Hive to perform data analytics on Big Data and an introduction to Spark Core, Spark SQL, Apache Zeppelin, and additional Spark features.

Students should be familiar with programming principles and have experience in software development. SQL and light scripting knowledge is also helpful. No prior Hadoop knowledge is required.

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

Upcoming Trainings

Join our public courses in our United Kingdom facilities. Private class trainings will be organized at the location of your preference, according to your schedule.

Classroom / Virtual Classroom

27 May 2024