JumpStart to Developing in Spark | Spark Programs, RDDs, NoSQL, Spark Machine Learning & More

Course Code: 1340

$2195

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

Overview

This course offers a holistic overview in some of the most cutting-edge technologies in the data science spectrum, with an emphasis on Spark and related tools. The framework of this course is structured for developers interested in enhancing their skills and learning enterprise-grade Spark programming. The course covers a wide array of topics ranging from features of Spark to practical experience with the specific set of technologies.

Schedule Classes

Delivery Format
Starting Date
Starting Time
Duration

Live Classroom
Monday, 7 December 2020
10:00 AM - 6:00 PM EST
3 Days

$2195 Add to cart

Delivery Format
Starting Date
Starting Time
Duration

Live Classroom
Monday, 26 October 2020
10:00 AM - 6:00 PM EST
3 Days

$2195 Add to cart

Looking for more sessions of this class?

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

Basics of Spark architecture and applications
Executing Spark Programs
Creating and manipulating both RDDs (Resilient Distributed Datasets) and UDFs (Unified Data Frames)
Restoring data frames
Essential NOSQL access
Integrating machine learning into Spark applications
Using Spark Streaming and Kafka to create streaming applications

Outline

Module 1: Getting started with Spark

Overview of Spark
Hadoop ecosystem
Hadoop YARN vs. Mesos
Spark vs. Map/Reduce
Spark: Lambda architecture
Spark in the enterprise data science architecture

Module 2: Spark Component overview

Spark shell
RDDs: Resilient distributed datasets
DataFrames
Spark 2 unified DataFrames
Spark sessions
Functional programming
Spark SQL
MLib
Structured streaming
Spark R
Spark and Python
Exercise: Hello, Spark

Module 3: RDDs: Resilient distributed datasets

Coding with RDDs
Transformations
Actions
Lazy evaluation and optimization
RDDs in Map/Reduce
Exercise: Working with RDDs

Module 4: DataFrames

RDDs vs. DataFrames
Unified Dataframes (UDF) in Spark 2.x
Partitioning
Exercise: Working with unified DataFrames

Module 5: DataFrame persistence

RDD persistence
DataFrame and unified DataFrame persistence
Distributed persistence
Exercise: Saving and restoring DataFrames

Module 6: Accessing NOSQL Data

Ingesting data
Relational databases and Sqoop
Interacting with Hive
Graph data
Accessing Cassandra data
Exercise: NoSQL data access

Module 7: Spark SQL

Spark SQL
SQL and DataFrames
Spark SQL and Hive
Spark SQL and JDBC
Exercise: Working with SparkSQL

Module 8: Machine Learning

ML Lib
Mahout
Exercise: Hello, MLib

Module 9: Spark streaming

Streaming overview
Streams
Structured streaming
Lambda streaming
Spark and Kafka
Exercise: Hello, Spark Streaming

Prerequisites

Participants must be proficient in Java Programming Fundamentals. They need to have a thorough understanding of the basics of Python programming and SQL.

Who Should Attend

This course is geared for experienced developers and architects (with development experience) who seek to be proficient in advanced, modern development skills, working with Apache Spark in an enterprise data environment.

This course is highly recommended for: