Apache Spark and Scala Training

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. 

If you are a professional who is keen on learning Apache Spark, then the following resources might help you do so:

Apache Spark Tutorials:

tutorialspoint.com

Edureka.com

Intellipaat.com

Simplilearn.com

Apache Spark Videos:

What is Apache Spark by Mike Olson

What is Apache Spark?

Spark Tutorials for Beginners

Apache Spark Books:

• Learning Spark: Lightning-Fast Big Data Analysis
• Mastering Apache Spark
• Spark in Action
• Spark Cookbook
• Mastering Apache Spark 2.x
• Big Data Analytics with Spark: A Practitioner's Guide to Using Spark for Large Scale Data Analysis

If you wish to master the skills and features of Apache Spark, you can opt for training sessions to help you. Here is a list of few training institutes which will help you do so:

• Udemy
• KnowledgeHut
• Edx
• Coursera
• Lynda

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.

Technical skills and knowledge required to become a Spark Professional are:-

• Fundamental knowledge of any programming language
• Basic understanding of any database, SQL and query language for databases
• Working knowledge of Linux- or Unix-based systems (not mandatory)
• Certification training as a Big Data Hadoop developer (recommended)

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.

There is a huge demand for Apache Spark professionals today. With the increasing needs for rapid analysis and processing of Big Data, Spark, the in-memory stack, is being preferred as a faster and simpler alternative to MapReduce, either within a Hadoop framework or outside it.

Therefore, Big Data enthusiasts with in-depth knowledge of Spark are hugely rewarded by employers.

Facebook, Twitter, Linkedin, Yahoo, eBay, Alibaba, Cloudspace, Fox Audience Network, Adobe, etc. are some of the companies who are hiring Spark developers. There is also a great demand for Spark developers and architects from the Retail industry, Manufacturing, Healthcare, Banking, and Finance industries.

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.

• Apache Spark can handle both batch and real-time analytics and data processing workloads.

• 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

• Spark is 100 times faster than Map Reduce

Apache Spark supports Java, Scala, Python APIs.

Scala is the language of the future and is the best language to learn for Apache Spark. Apache Spark is completely written in Scala.

Spark supports the Scala APIs as Spark is written completely in Scala. Hence Spark programs written in Scala might have some performance benefits. Since Scala is based on JVM, it is native for Hadoop and so seamlessly work with it. And, in almost all cases, it outperforms Python.

Scala is the most upcoming programming language. Scala combines object-oriented and functional programming in one concise, high-level language. Scala's static types help avoid bugs in complex applications, and its JVM and JavaScript runtimes let you build high-performance systems with easy access to huge ecosystems of libraries.

The main difference between Spark and Scala is that the Apache Spark is a cluster computing framework designed for fast Hadoop computation while the Scala is a general-purpose programming language that supports functional and object-oriented programming.

Apache Spark is implemented in Scala because Scala combines object-oriented and functional programming in one concise, high-level language. Scala's static types help avoid bugs in complex applications, and its JVM and JavaScript runtimes let you build high-performance systems with easy access to huge ecosystems of libraries.

No. Spark programs can be written using Java and Python too.

Apache Spark in widely considered as the future of the Big Data platform. When Spark stepped into the Big data industry, it has met the enterprises’ expectations in a better way with respect to data processing, querying analytics reports in a faster way.

Spark is written in Scala and Scala gives you access to many advanced features of Spark.

Yes, Spark is an open source, cluster-computing framework which supports various programming languages like Scala, Python, Java and R.

Some of the great applications of Apache Spark are:

Spark is a widely-used technology adopted by most of the industries. Some of the prominent applications of Apache Spark are –

Machine Learning – Apache Spark is equipped with a scalable Machine Learning Library called  MLlib that can perform advanced analytics such as clustering, classification, dimensionality reduction, etc. Some of the prominent analytics jobs like predictive analysis, customer segmentation, sentiment analysis, etc., make Spark an intelligent technology.

Fog computing – With the influx of big data concepts, IoT has acquired a prominent space for the invention of more advanced technologies. Based on the theory of connecting digital devices with the help of small sensors this technology deals with a humongous amount of data emanating from numerous mediums. This requires parallel processing which is certainly not possible on cloud computing. Therefore Fog computing which decentralizes the data and storage uses Spark streaming as a solution to this problem.

Event detection – The feature of Spark streaming allows the organization to keep track of rare and unusual behaviors for protecting the system. Institutions like financial institutions, security organizations, and health organizations use triggers to detect the potential risk.

Interactive analysis – Among the most notable features of Apache Spark is its ability to support interactive analysis. Unlike MapReduce that supports batch processing, Apache Spark processes data faster because of which it can process exploratory queries without sampling.