Cloudera Developer Training for Apache Spark

Course Details
Code:
DEV-SPARK
Tuition (USD):
$2,595.00 • Classroom (3 days)

Cloudera University’s three-day training course for Apache Spark enables participants to build complete, unified big data applications combining batch, streaming, and interactive analytics on all their data. With Spark, evelopers can write sophisticated parallel applications to execute faster decisions, better decisions, and real-time actions, applied to a wide variety of use cases, architectures, and industries.

Skills Gained

Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning topics such as:

  • Using the Spark shell for interactive data analysis
  • The features of Spark’s Resilient Distributed Datasets
  • How Spark runs on a cluster
  • How Spark parallelizes task execution
  • Writing Spark applications
  • Processing streaming data with Spark

Who Can Benefit

This course is best suited to developers and engineers with prior knowledge and experience with Hadoop.

Prerequisites

Course examples and exercises are presented in Python and Scala, so knowledge of one of these programming languages is required. Basic knowledge of Linux is assumed.

Course Details

Advance Your Ecosystem Expertise

Apache Spark is the next-generation successor to MapReduce. Spark is a powerful, opensource processing engine for data in the Hadoop cluster, optimized for speed, ease of use, and sophisticated analytics. The Spark framework supports streaming data processing and complex, iterative algorithms, enabling applications to run up to 100x aster than traditional Hadoop MapReduce programs.

Introduction to Spark

  • What is Spark?
  • Review: From Hadoop MapReduce to Spark
  • Review: HDFS
  • Review: YARN
  • Spark Overview

Spark Basics

  • Using the Spark Shell
  • RDDs (Resilient Distributed Datasets)
  • Functional Programming in Spark

Working with RDDs in Spark

  • Creating RDDs
  • Other General RDD Operations

Aggregating Data with Pair RDDs

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

Writing and Deploying 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
  • Hands-On Exercise: Write and Run
  • Spark Application
  • Configuring Spark Properties
  • Logging

Parallel Processing

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

Spark RDD Persistence

  • RDD Lineage
  • RDD Persistence Overview
  • Distributed Persistence

Basic Spark Streaming

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

Advanced Spark Streaming

  • Multi-Batch Operations
  • State Operations
  • Sliding Window Operations
  • Advanced Data Sources

Common Patterns in Spark Data Processing

  • Common Spark Use Cases
  • Iterative Algorithms in Spark
  • Graph Processing and Analysis
  • Machine Learning
  • Example: k-means

Improving Spark Performance

  • Shared Variables: Broadcast Variables
  • Shared Variables: Accumulators
  • Common Performance Issues
  • Diagnosing Performance Problems

Spark SQL and DataFrames

  • Spark SQL and the SQL Context
  • Creating DataFrames
  • Transforming and Querying DataFrames
  • Saving DataFrames
  • DataFrames and RDDs
  • Comparing Spark SQL, Impala and Hive-on-Spark

Conclusion

Course Details
Code:
DEV-SPARK
Tuition (USD):
$2,595.00 • Classroom (3 days)