Cloudera Developer for Spark and Hadoop Training in Denmark

• This course is designed for developers and engineers who have programming experience, but prior knowledge of Hadoop is not required
• Apache Spark examples and hands-on exercises are presented in Scala and Python. The ability to program in one of those languages is required
• Basic familiarity with the Linux command line is assumed
• Basic knowledge of SQL is helpful

• Through expert-led discussion and interactive, hands-on exercises, participants will learn how to:
• Distribute, store, and process data in a Hadoop cluster
• Write, configure, and deploy Apache Spark applications on a Hadoop cluster
• Use the Spark shell for interactive data analysis
• Process and query structured data using Spark SQL
• Use Spark Streaming to process a live data stream
• Use Flume and Kafka to ingest data for Spark Streaming

Introduction

Introduction to Apache Hadoop and the Hadoop Ecosystem

• Apache Hadoop Overview
• Data Storage and Ingest
• Data Processing
• Data Analysis and Exploration
• Other Ecosystem Tools
• Introduction to the Hands-On Exercises
• Apache Hadoop File Storage
• Problems with Traditional

Large-Scale Systems

• HDFS Architecture
• Using HDFS
• Apache Hadoop File Formats

Data Processing on an Apache Hadoop Cluster

• YARN Architecture
• Working With YARN

Importing Relational Data with Apache Sqoop

• Apache Sqoop Overview
• Importing Data
• Importing File Options
• Exporting Data

Apache Spark Basics

• What is Apache Spark?
• Using the Spark Shell
• RDDs (Resilient Distributed Datasets)
• Functional Programming in Spark

Working with RDDs

• Creating RDDs
• Other General RDD Operations

Aggregating Data with Pair RDDs

• Key-Value Pair RDDs
• Map-Reduce
• Other Pair RDD Operations

Writing and Running Apache Spark Applications

• Spark Applications vs. Spark Shell
• Creating the SparkContext
• Building a Spark Application

(Scala and Java)

• Running a Spark Application
• The Spark Application Web UI

Configuring Apache Spark Applications

• Configuring Spark Properties
• Logging

Parallel Processing in Apache Spark

• Review: Apache Spark on a Cluster
• RDD Partitions
• Partitioning of File-Based RDDs
• HDFS and Data Locality
• Executing Parallel Operations
• Stages and Tasks

RDD Persistence

• RDD Lineage
• RDD Persistence Overview
• Distributed Persistence

Common Patterns in Apache Spark

Data Processing

• Common Apache Spark Use Cases
• Iterative Algorithms in Apache Spark
• Machine Learning
• Example: k-means

DataFrames and Spark SQL

• Apache Spark SQL and the SQL Context
• Creating DataFrames
• Transforming and Querying DataFrames
• Saving DataFrames
• DataFrames and RDDs
• Comparing Apache Spark SQL, Impala, and Hive-on-Spark
• Apache Spark SQL in Spark 2.x

Message Processing with Apache Kafka

• What is Apache Kafka?
• Apache Kafka Overview
• Scaling Apache Kafka
• Apache Kafka Cluster Architecture
• Apache Kafka Command Line Tools

Capturing Data with Apache Flume

• What is Apache Flume?
• Basic Flume Architecture
• Flume Sources
• Flume Sinks
• Flume Channels
• Flume Configuration

Integrating Apache Flume and Apache Kafka

• Overview
• Use Cases
• Configuration

Apache Spark Streaming:

Introduction to DStreams

• Apache Spark Streaming Overview
• Example: Streaming Request Count
• DStreams
• Developing Streaming Applications

Apache Spark Streaming:

Processing Multiple Batches

• Multi-Batch Operations
• Time Slicing
• State Operations
• Sliding Window Operations

Apache Spark Streaming: Data Sources

• Streaming Data Source Overview
• Apache Flume and Apache Kafka

Data Sources

• Example: Using a Kafka Direct Data Source

Conclusion