Apache Spark for Data Scientists

Course Code: 1341

$2195

Overview
Schedule classes
What you'll learn
Outline
Prerequisites
Who should attend

Overview

This three-day course equips participants with the knowledge, skills and tools to use Apache Spark effectively for their data analysis needs. Participants will gain practical training in Apache Spark and will be taught how to build comprehensive big data applications by integrating batch, streaming, and interactive analytics on all their data. The course explores using Apache Spark for common data related activities. With Spark, participants will learn how to write sophisticated parallel applications to execute faster, better decisions, and real-time actions, applied to a wide variety of use cases, architectures, and industries.

Schedule Classes

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Course Delivery

This course is available in the following formats:

Live Classroom
Duration: 5 days

Live Virtual Classroom
Duration: 5 days

What You'll learn

Introduction to Spark
Spark and data science
Build comprehensive big data applications by integrating batch, streaming, and interactive analytics on all data
Use Apache Spark for common data related activities
Understand data frames and Spark SQL
Work with Spark MLib, Spark GraphX and Spark streaming
Understand memory management

Outline

Module 1: Spark and Data Science

Data Science: The state of the art
Hadoop, Yarn, and Spark
Architectural overview
Spark and Storm
MLib and Mahout
Distributed vs. local run modes
Hello, Spark

Module 2: Spark overview

Spark core
Spark SQL
Spark and Hive
MLib
Mahout
Spark streaming
Spark API

Module 3: DataFrames

DataFrames and resilient distributed datasets (RDDs)
Partitions
DataFrame types
DataFrame operations
Map/Reduce with DataFrames

Module 4: Spark SQL

Spark SQL overview
Data stores: HDFS, Cassandra, HBase, Hive, and S3
Table definitions
ETL in Spark
Queries

Module 5: Spark MLib

MLib overview
MLib algorithms overview

Module 6: Spark streaming

Streaming overview
Real-time data ingestion
State
Window operations

Module 7: Spark GraphX

GraphX overview
ETL with GraphX
Graph computation

Module 8: Performance and tuning

Broadcast variables
Accumulators
Memory management

Module 9: Cluster mode

Standalone cluster
Masters and workers
Configurations
Working with large data sets

Prerequisites

There are no mandatory prerequisites for this course, however, completing the Foundations of Agile course prior to taking up this course would be beneficial.

Who Should Attend

This course is intended for systems administrators, testers or technical data related roles who need to learn to use Spark for data analysis or processing data.

This course is highly recommended for: