Apache Spark and Scala Course Training

The sessions that are conducted are 24 hours of live sessions, with 70+ hours MCQs and Assignments and 23 hours of hands-on sessions.

To attend the online Spark classes, the following is the list of essential requirements:

• Operating system (Mac OS X, Windows or Linux)
• A web browser like Chrome, FireFox
• Proper internet connection

Yes, our lab facility at KnowledgeHut has the latest version of hardware and software and is very well-equipped. We provide Cloudlabs so that you can get a hands-on experience of the features of Apache Spark. Cloudlabs provides you with real-world scenarios can practice from anywhere around the globe. You will have an opportunity to have live hands-on coding sessions. Moreover, you will be given practice assignments to work on after your class.

Here at KnowledgeHut, we have Cloudlabs for all major categories like cloud computing, web development, and Data Science.

This Apache Spark and Scala training course have three projects, viz Adobe Analysis, Interactive Analysis, and Personalizing news pages for Web visitors in Yahoo.

• Adobe Analysis: Adobe Analytics deal with huge amount of transactions in a day across major mobile and web properties. With the help of this project, you’ll come to know how Spark and Scala are useful in refactoring process.
• Interactive Analysis: Apache Spark has various features like Fog computing, IOT and MLib, GraphX etc. It’s most notable feature is its ability to support interactive analysis.
• Personalizing news pages for Web visitors in Yahoo: Yahoo runs various Spark projects for different applications. Yahoo uses ML algorithms for personalizing news pages.

The trainer who will be conducting our Apache Spark certification has comprehensive experience in developing and delivering Spark applications. He has years of experience in training professionals in Apache Spark. Our coaches are very motivating and encouraging, as well as provide a friendly learning environment for the students who are keen about learning and making a leap in their career.

Yes, you can attend a demo session before getting yourself enrolled for the Apache Spark training.

All our Online instructor-led training is an interactive session. Any point of time during the session you can unmute yourself and ask the doubts/ queries related to the course topics.

There are very few chances of you missing any of the Spark training session at KnowledgeHut. But in case you miss any lecture, you have two options:

• You can watch the online recording of the session
• You can attend the missed class in any other live batch.

At upGrad KnowledgeHut, we strive diligently to make sure that your learning experience with us is second to none and you are assured of the highest standards of quality. However, if for any reason your expectations are not met, we will process refunds in accordance with our Cancellation, Refund, and Deferment Policy.

Typically, KnowledgeHut’s training is exhaustive and the mentors will help you in understanding the concepts in-depth.

However, if you find it difficult to cope, you may discontinue and withdraw from the course right after the first session as well as avail 100% money back. To learn more about the 100% refund policy, visit our Refund Policy.

Yes, we have scholarships available for Students and Veterans. We do provide grants that can vary up to 50% of the course fees.

To avail scholarships, feel free to get in touch with us at the following link:

https://www.knowledgehut.com/contact-us

The team shall send across the forms and instructions to you. Based on the responses and answers that we receive, the panel of experts takes a decision on the Grant. The entire process could take around 7 to 15 days

Yes, you can pay the course fee in instalments. To avail, please get in touch with us at https://www.knowledgehut.com/contact-us. Our team will brief you on the process of instalment process and the timeline for your case.

Mostly the instalments vary from 2 to 3 but have to be fully paid before the completion of the course.

Get yourself registered in any of the training institutes that provides Apache Spark and Scala certification. Participate and get certified.

After successfully completing the Apache Spark and Scala course, you will be accorded with a certificate of course completion from KnowledgeHut.

The certification provided by KnowledgeHut has lifetime validity.

A simple search on YouTube would churn out thousands of resources about learning Apache Spark. While the sheer number of results may be overwhelming, to further complicate things, one would not know which sources are credible.

We have compiled a set of resources from our experts to help you get started with confidence:

1) Apache Spark Videos:

• Advanced Spark Tutorial - Part 1 | Advanced Spark for Developers

• Advanced Spark Tutorial - Part 2 | Advanced Spark for Developers

2) Apache Spark Books:

• Learning Spark: Lightning-Fast Big Data Analysis by Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia
• Mastering Apache Spark by Mike Frampton
• Spark in Action by Petar Zecevic, Marko Bonaci

• Mastering Apache Spark 2.x by Romeo Kienzler
• Big Data Analytics with Spark: A Practitioner's Guide to Using Spark for Large Scale Data Analysis by Mohammed Guller

If you wish to master the skills and features of Apache Spark, you can opt for training sessions to help you. Apache Spark and Scala Training by KnowledgeHut is one such course which provides state-of-the-art, comprehensive hands-on training which will help you master all the skills that you will need to face real-world obstacles.

No, you need not learn Hadoop first to learn Apache Spark.

A while back, the market trend was more inclined towards Hadoop. But with time, there has been a variation in the trend as more and more industries are moving towards Spark as it is faster than Hadoop.

But at the same time, professionals who have the knowledge of Spark and Hadoop are best preferred in the IT industry and are highly paid as well.

Organisations use Apache Spark with ML algorithms. Spark library has a library labelled as MLib, which is a library for ML. This library of Apache Spark contains algorithms for the functions of classification, clustering, regression, dimensionality reduction, collaborative filtering, etc.

Apache Spark provides a powerful API for ML applications, with the goal to make practical ML easier. For the same, it has higher-level pipeline APIs and lower-level optimisation primitives.

With resources and tutorials available, it is easy to learn Apache Spark.

If you are already familiar with Scala, it’ll be easier for you as you already know the basic principles behind Spark and how it works.

Moreover, if you wish to learn and get certified, you can opt for online training on Spark and Scala provided by KnowledgeHut. The curriculum of the course provided by them covers all the relevant topics which are required by the industry. Feel free to take a look at the course content of Apache Spark that KnowledgeHut provides.

According to the Data Science Salary Survey by O’Reilly, there exists a strong link between professionals who utilize Spark and Scala and the change in their salaries. The survey has shown that professionals with Apache Spark skills added $11,000 to the median or average salary, while Scala programming language affected an increase of $4000 to the bottom line of a professional’s salary.

Apache Spark developers have been known to earn the highest average salary among other programmers utilizing ten of the most prominent Hadoop development tools. Real-time big data applications are going mainstream faster and enterprises are generating data at an unforeseen and rapid rate. This is the best time for professionals to learn Apache Spark online and help companies progress in complex data analysis.

Apache Spark has a bright future:

• Many companies have recognized the power of Spark and quickly started working on it. More and more companies have started using Spark. In upcoming days Spark will be the most trending technology and there will be huge scope for Spark developers.
• Apache Spark is the most advanced and popular product of Apache Community that provides the provision to work with streaming data, has various Machine learning libraries, can work on structured and unstructured data, deal with graphs, etc.
• Apache Spark is one of the most active projects of Apache and its future scope will be long-lasting.
• Spark users have exponentially increased and are considered as the future of Big Data Platform

After completing the Apache Spark and Scala course, you will be able to:

• Understand the fundamentals of Scala programming language along with its features
• Master the use of Resilient Distributed Datasets (RDD) to create applications in Spark
• Get a proper understanding of the streaming features of Spark
• Master the features of Spark ML programming and GraphX programming
• Know the limitations of MapReduce and how Spark is used to overcome these limitations.
• Learn and master how Spark can be installed as a standalone cluster
• Acquire the knowledge of SQL using SparkSQL

According to Indeed.com, the average salary for an Apache Spark developer ranges from approximately $97,915 per year for Developer to $133,184 per year for Data Engineer.

Apache Spark is one of the most popular projects in the Hadoop Ecosystem and is, in fact, the most actively developed open source project in Big data. And, it continues to attract more and more people every day.

It is popular not just among Data Scientists but also among Engineers, Developers and everybody else interested in Big Data. It is so popular that a lot of people believe it will grow to replace Map Reduce entirely.

It is popular because of three things: Simplicity, Performance, and Flexibility.

A few things why Spark is so popular :

• One of the major strengths of Spark is its easy integration with the Hadoop ecosystem.

• Spark, written in Scala, embeds in all JVM-based systems. Also, it provides an interactive REPL, Spark-shell, making it easy to test simple programs

• It has API's in Python and Java apart from the native Scala. This makes application development very easy and it makes Spark a great platform for developers.

• It comes with a Machine learning library MLlib making it very easy for a lot of people to start with and is ideally suited to ML applications.

With all these features, Spark has become the center of attraction for almost all of the Big Data developers and Data scientists. Though it has only been a few years, Spark has been evolving quickly and promises to be a sure contender for an industry standard in Big Data.

The advantages/benefits of Apache Spark are:-

Integration with Hadoop

Spark’s framework is built on top of the Hadoop Distributed File System (HDFS). So it’s advantageous for those who are familiar with Hadoop.
Faster

Spark also starts with the same concept of being able to run MapReduce jobs except that it first places the data into RDDs (Resilient Distributed Datasets). This data is now stored in memory so it’s more quickly accessible i.e. the same MapReduce jobs can run much faster because the data is accessed in memory.

Real-time stream processing

Every year, the real-time data being collected from various sources keeps shooting up exponentially. This is where processing and manipulating real-time data can help us. Spark helps us to analyze real-time data as and when it is collected.

Applications are fraud detection, electronic trading data, log processing in live streams (website logs), etc.

Graph Processing

Apart from Steam Processing, Spark can also be used for graph processing. From advertising to social data analysis, graph processing capture relationships in data between entities, say people and objects which are then are mapped out. This has led to recent advances in machine learning and data mining.

Powerful

Today companies manage two different systems to handle their data and hence end up building separate applications for it. One for streaming & storing real-time data. The other to manipulate and analyze this data. This means a lot of space and computational time. Spark gives us the flexibility to implement both batch and stream processing of data simultaneously, which allows organizations to simplify deployment, maintenance and application development.

Top Companies Using Spark

• Microsoft

Including Spark support to Azure HDInsight (its cloud-hosted version of Hadoop).

• IBM

To manage its SystemML machine learning algorithm construction, IBM uses Spark technology.

• Amazon

To run Spark apps developed in Scala, Java, and Python, Amazon uses Apache Spark.

• Yahoo!

Yahoo used to have the origin in Hadoop for analyzing big data. Nowadays, Apache Spark is the next cornerstone.

Apart from them many more names like:

• Conviva
• Netflix
• Pinterest
• Oracle
• Hortonworks
• Cisco
• Verizon
• Visa
• Databricks
• Amazon
• Accenture PLC
• Paxata
• DataStax, Inc.
• UC Berkeley AMPLab
• TripAdvisor
• Samsung Research America
• Shopify
• Premise
• Quantifind
• Radius Intelligence
• OpenTable
• Hitachi Solutions
• The Hive
• IBM Almaden
• eBay!
• Bizo and many more

• Apache Spark is the go-to tool for Data Science at scale. It is an open source, distributed compute platform which is the first tool in the Data Science toolbox which is built specifically with Data Science in mind.
• Spark is different from the myriad other solutions to this problem because it allows Data Scientists to develop simple code to perform distributed computing, and the functionality available in Spark is growing at an incredible rate.
• Much has been made in the Data Science community around Spark’s ability to train Machine Learning models at scale, and this is a key benefit, but the real value comes from being able to put an entire analytics pipeline into Spark, right from the data ingestion and ETL processes, through the data wrangling and feature engineering processes through to training and execution of models.
• What’s more, with Spark streaming and graphx Spark can provide a much more complete analytics solution.

Follow the below steps given below for installing Spark.

• Extracting Spark tar file using following command –

$ tar xvf spark-2.4.3-bin-hadoop2.7.3.tgz

• Move Spark software files to respective directory using following commands –

/usr/local/spark

# cd /home/Hadoop/Downloads/

# mv spark-2.4.3-bin-hadoop2.7.3 /usr/local/spark

• Add the following line to a ~/.bashrc file which will add the location, where the spark software files are located to the PATH variable type.

export PATH = $PATH:/usr/local/spark/bin

• Use the following below command for sourcing the ~/.bashrc file.

$ source ~/.bashrc

• Verify the Installation of Spark application on your system
• The following command will open the Spark shell application version.

$spark-shell

Verify the Installation of Spark application on your system

The following command will open the Spark shell application version:

$spark-shell

If spark is installed successfully then you will be getting the following output.

Spark assembly has been built with Hive, including Datanucleus jars on classpath

Using Spark’s default log4j profile: org/apache/spark/log4j-defaults.properties

12/04/19 15:25:22 INFO SecurityManager: Changing view acls to: hadoop

12/04/19 15:25:22 INFO SecurityManager: Changing modify acls to: hadoop

12/04/04 15:25:22 INFO SecurityManager: SecurityManager: authentication disabled;

ui acls disabled; users with view permissions: Set(hadoop); users with modify permissions: Set(hadoop)

12/04/19 15:25:22 INFO HttpServer: Starting HTTP Server

12/04/19 15:25:23 INFO Utils: Successfully started service naming ‘HTTP class server’ on port 43292.

Welcome to the Spark World

Initializing Spark in Scala

import org.apache.spark.SparkConf

import org.apache.spark.SparkContext

import org.apache.spark.SparkContext._

val conf = new SparkConf().setMaster("local").setAppName("My App")

val sc = new SparkContext(conf)

Initializing Spark in Java

import org.apache.spark.SparkConf;

import org.apache.spark.api.java.JavaSparkContext;

SparkConf conf = new SparkConf().setMaster("local").setAppName("My App");

JavaSparkContext sc = new JavaSparkContext(conf);

Apache Spark can process data from a variety of data repositories, including the Hadoop Distributed File System (HDFS), NoSQL databases and relational data stores, such as Apache Hive. Spark supports in-memory processing to boost the performance of big data analytics applications, but it can also perform conventional disk-based processing when data sets are too large to fit into the available system memory.

The Spark Core engine uses the resilient distributed data set, or RDD, as its basic data type. The RDD is designed in such a way so as to hide much of the computational complexity from users. It aggregates data and partitions it across a server cluster, where it can then be computed and either moved to a different data store or run through an analytic model. The user doesn't have to define where specific files are sent or what computational resources are used to store or retrieve files.

In addition, Spark can handle more than the batch processing applications that MapReduce is limited to running.

Spark Libraries
The Spark Core engine functions partly as an application programming interface (API) layer and underpins a set of related tools for managing and analyzing data. Aside from the Spark Core processing engine, the Apache Spark API environment comes packaged with some libraries of code for use in data analytics applications. These libraries include:

Spark SQL
One of the most commonly used libraries, Spark SQL enables users to query data stored in disparate applications using the common SQL language.

Spark Streaming
This library enables users to build applications that analyze and present data in real time.

MLlib
A library of machine learning code that enables users to apply advanced statistical operations to data in their Spark cluster and to build applications around these analyses.

GraphX
A built-in library of algorithms for graph-parallel computation.

Apache Spark is a general purpose cluster-computing framework that can be deployed by multiple ways like streaming data, graph processing and Machine learning.

Features of Spark are –

1. Lighting Fast Processing
2. Support for Sophisticated Analytics
3. Real-Time Stream Processing
4. Ability to Integrate with Hadoop and Existing Hadoop Data
5. Active and Expanding Community

The different components of Apache Spark are:-
Spark Libraries
The Spark Core engine functions partly as an application programming interface (API) layer and underpins a set of related tools for managing and analyzing data. Aside from the Spark Core processing engine, the Apache Spark API environment comes packaged with some libraries of code for use in data analytics applications. These libraries include:

Spark SQL
One of the most commonly used libraries, Spark SQL enables users to query data stored in disparate applications using the common SQL language.

Spark Streaming
This library enables users to build applications that analyze and present data in real time.

MLlib
A library of machine learning code that enables users to apply advanced statistical operations to data in their Spark cluster and to build applications around these analyses.

GraphX
A built-in library of algorithms for graph-parallel computation.