Introduction to Spark 3 with Scala Training Course

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Introduction to Spark 3 with Scala Training


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Java Programming Stats-Uses and Pros-Cons
Course Description
This course introduces the Apache Spark distributed computing engine, and is suitable for developers, data analysts, architects, technical managers, and anyone who needs to use Spark in a hands-on manner. It is based on the Spark 3.x release. The course provides a solid technical introduction to the Spark architecture and how Spark works. It covers the basic building blocks of Spark (e.g. RDDs and the distributed compute engine), as well as higher-level constructs that provide a simpler and more capable interface (e.g. DataSets/DataFrames and Spark SQL). It includes in-depth coverage of Spark SQL, DataFrames, and DataSets, which are now the preferred programming API. This includes exploring possible performance issues and strategies for optimization. The course also covers more advanced capabilities such as the use of Spark Streaming to process streaming data, and integrating with the Kafka server. The course is very hands-on, with many labs. Participants will interact with Spark through the Spark shell (for interactive, ad-hoc processing) as well as through programs using the Spark API. After taking this course, you will be ready to work with Spark in an informed and productive manner. Labs are supported in Scala. There is a separate course for Python users. Course Length: 4 Days Course Tuition: $1890 (US)

Prerequisites
Working knowledge of some programming language - no Java experience needed

Course Outline

Session 1 (Optional): Scala Ramp Up

Scala Introduction, Variables, Data Types, Control Flow

The Scala Interpreter

Collections and their Standard Methods (e.g. map())

Functions, Methods, Function Literals

Class, Object, Trait, case Class

Session 2: Introduction to Spark

Overview, Motivations, Spark Systems

Spark Ecosystem

Spark vs. Hadoop

Acquiring and Installing Spark

The Spark Shell, SparkContext

Session 3: RDDs and Spark Architecture

RDD Concepts, Lifecycle, Lazy Evaluation

RDD Partitioning and Transformations

Working with RDDs - Creating and Transforming (map, filter, etc.)

Session 4: Spark SQL, DataFrames, and DataSets

Overview

SparkSession, Loading/Saving Data, Data Formats (JSON, CSV, Parquet, text ...)

Introducing DataFrames and DataSets (Creation and Schema Inference)

Supported Data Formats (JSON, Text, CSV, Parquet)

Working with the DataFrame (untyped) Query DSL (Column, Filtering, Grouping, Aggregation)

SQL-based Queries

Working with the DataSet (typed) API

Mapping and Splitting (flatMap(), explode(), and split())

DataSets vs. DataFrames vs. RDDs

Session 5: Shuffling Transformations and Performance

Grouping, Reducing, Joining

Shuffling, Narrow vs. Wide Dependencies, and Performance Implications

Exploring the Catalyst Query Optimizer (explain(), Query Plans, Issues with lambdas)

The Tungsten Optimizer (Binary Format, Cache Awareness, Whole-Stage Code Gen)

Session 6: Performance Tuning

Caching - Concepts, Storage Type, Guidelines

Minimizing Shuffling for Increased Performance

Using Broadcast Variables and Accumulators

General Performance Guidelines

Session 7: Creating Standalone Applications

Core API, SparkSession.Builder

Configuring and Creating a SparkSession

Building and Running Applications - sbt/build.sbt and spark-submit

Application Lifecycle (Driver, Executors, and Tasks)

Cluster Managers (Standalone, YARN, Mesos)

Logging and Debugging

Session 8: Spark Streaming

Introduction and Streaming Basics

Structured Streaming (Spark 2+)

Continuous Applications

Table Paradigm, Result Table

Steps for Structured Streaming

Sources and Sinks

Consuming Kafka Data

Kafka Overview

Structured Streaming - "kafka" format

Processing the Stream

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Java Programming Uses & Stats

Java Programming is Used For:

Android & IOS Development Software Products Video Games Desktop GUI's

Difficulty

Popularity

Year Created

1995

Pros

Most Commonly Used:
According to Oracle, three billion devices run on Java. And, because of its real-world applications, it consistently ranks at the top of the TIOBE Programming Community Index.

Great Career Choice:
Some of the fastest-growing salaries in the U.S. in 2018 are for Java developers. (Glassdoor)

Android Apps Development:
Developers predominatly use their Java skills in building apps for Google's Android. The Android platform is the number one mobile paltform in the world

It Can Run On Any Platform:
Java can compile on Windows and run the same compiled file on Linux, Windows and Mac.

Great Supporting IDE's:
Over the years, coding in Java has become simpler with the introduction of open source development tools, i.e. Eclipse and NetBeans that use Java capabilities for debugging.

Cons

Uses a Lot of Memory:
Performance can be significantly slower with Java and more memory-consuming than natively compiled languages such as C or C++.

Difficulty in Learning:
Learning Java can be a bit challenging if you are a beginner. However, once you get the hang of Object Oriented Programming and a decent grasp of the syntax, you will be well on your way.

Slow Start Up Times:
There is quite a bit of one-time initialization done by JDK classes before compiling as well as loading classes and verification (making sure code doesn't do evil things, all of which takes longer that some other languages such as C.

Verbose and Complex Code:
Long, over-complicated sentences make code less readable and scannable. Compare to let's say Python, we can see how clear Python code appears: It doesn’t require semicolons; uses “and,” “or,” and “not” as operators instead of Java’s “&&,” “||,” and “!”; and generally has fewer bells and whistles such as parentheses or curly braces.

Commercial License Cost:
Companies have to prepare for the changes that Oracle will institute in 2019 . Today, the current version of Java is free and available for redistribution for general purpose computing. However, If you are a DEVELOPER, Oracle recommends you review the roadmap information for Java SE 8 and beyond and take appropriate action depending on the type of application you develop and your distribution mode.

Java Programming Job Market

Average Salary $102,000	Job Count 26,856	Top Job Locations New York City San Jose Washington D.C,

Average Salary

$102,000

Job Count

26,856

Top Job Locations

New York City
San Jose
Washington D.C,

Complimentary Skills to have along with Java Programming

- If you are an experienced Java developer, learning a complimentary language to Java should come much more naturally. As an example JetBrains recently created the Kotlin programming language which is officially supported by Google for mobile development. Kotlin compiles to Java bytecode and runs on the JVM; it's purported to address many of Java's shortcomings...