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In this era of Artificial intelligence, machine learning, and data science, algorithms that run on Distributed Iterative computation make the task of distributing and computing huge volumes of data easy. Spark is a lightning-fast, in-memory, cluster computing framework that can be used for a variety of purposes. This JVM-based open source framework can be used for processing and analyzing huge volumes of data and at the same time can be used to distribute data over a cluster of machines. It is designed in such a way that it can perform batch and stream processing and hence is known as a cluster computing platform. Scala is the language in which Spark is developed. Scala is a powerful and dynamic programming language that doesn’t compromise on type safety.
Do you know the secret behind Uber’s flawless map functioning? Here’s a hint, the images gathered by the Map Data Collection Team are accessed by the downstream Apache Spark team and are assessed by operators responsible for map edits. A number of file formats are supported by Apache Spark which allows multiple records to be stored in a single file.
According to a recent survey by DataBricks, 71% of Spark users use Scala for programming. Spark with Scala is a perfect combination to stay grounded in the Big Data world. 9 out of 10 companies have this successful combination running in their organizations. Spark has over 1000 contributors across 250+ organizations making it the most popular open source project ever. The Apache Spark Market is expected to grow at a CAGR of 67% between 2019 and 2022 jostling a high demand for trained professionals.
Although you don't have to meet any prerequisites to take up Apache Spark and Scala certification training, having familiarity with Python/Java or Scala programming will be beneficial. Other than this, you should possess:
Learning Objectives:
Understand Big Data and its components such as HDFS. You will learn about the Hadoop Cluster Architecture. You will also get an introduction to Spark and the difference between batch processing and real-time processing.
Topics:
Hands-on: Scala REPL Detailed Demo.
Learning Objectives:
Learn the basics of Scala that are required for programming Spark applications. Also learn about the basic constructs of Scala such as variable types, control structures, collections such as Array, ArrayBuffer, Map, Lists, and many more.
Topics:
Hands-on: Scala REPL Detailed Demo
Learning Objectives:
Learn about object-oriented programming and functional programming techniques in Scala.
Topics
Hands-on: OOPs Concepts- Functional Programming
Learning Objectives:
Learn about the Scala collection APIs, types and hierarchies. Also, learn about performance characteristics.
Topics
Learning Objectives:
Understand Apache Spark and learn how to develop Spark applications.
Topics:
Hands-on:
Learning Objectives:
Get an insight of Spark - RDDs and other RDD related manipulations for implementing business logic (Transformations, Actions, and Functions performed on RDD).
Topics
Hands-on:
Learning Objectives:
Learn about SparkSQL which is used to process structured data with SQL queries, data-frames and datasets in Spark SQL along with different kinds of SQL operations performed on the data-frames. Also, learn about the Spark and Hive integration.
Topics
Hands-on:
Learning Objectives:
Learn why machine learning is needed, different Machine Learning techniques/algorithms, and SparK MLlib.
Topics
Learning Objectives:
Implement various algorithms supported by MLlib such as Linear Regression, Decision Tree, Random Forest and so on
Topics
Hands-on:
Learning Objectives:
Understand Kafka and its Architecture. Also, learn about Kafka Cluster, how to configure different types of Kafka Clusters. Get introduced to Apache Flume, its architecture and how it is integrated with Apache Kafka for event processing. At the end, learn how to ingest streaming data using flume.
Topics
Hands-on:
Understand Big Data, its components and the frameworks, Hadoop Cluster architecture and its modes.
Understand Scala programming, its implementation, basic constructs required for Apache Spark.
Gain an understanding of the concepts of Apache Spark and learn how to develop Spark applications.
Master the concepts of the Apache Spark framework and its associated deployment methodologies.
Learn Spark Internals RDD and use of Spark’s API and Scala functions to create and transform RDDs.
Master the RDD and various Combiners, SparkSQL, Spark Context, Spark Streaming, MLlib, and GraphX.
Prerequisites for Spark are.
These are the reasons why you should learn Apache Spark:-
You will get in-depth knowledge on Apache Spark and the Spark Ecosystem, which includes Spark RDD, Spark SQL, Spark MLlib and Spark Streaming. You will get comprehensive knowledge on Scala Programming language, HDFS, Sqoop, FLume, Spark GraphX and Messaging System such as Kafka.
All of the training programs conducted by us are interactive in nature and fun to learn as a great amount of time is spent on hands-on practical training, use case discussions, and quizzes. An extensive set of collaborative tools and techniques are used by our trainers which will improve your online training experience.
The Apache Spark and Scala training conducted at KnowledgeHut is customized according to the preferences of the learner. The training is conducted in three ways:
Online Classroom training: You can learn from anywhere through the most preferred virtual live and interactive training
Self-paced learning: This way of learning will provide you lifetime access to high-quality, self-paced e-learning materials designed by our team of industry experts
Team/Corporate Training: In this type of training, a company can either pick an employee or entire team to take online or classroom training. Flexible pricing options, standard Learning Management System (LMS), and enterprise dashboard are the add-on features of this training. Moreover, you can customize your curriculum based on your learning needs and also get post-training support from the expert during your real-time project implementation.
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:
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.
We provide our students with Environment/Server access for their systems. This ensures that every student experiences a real-time experience as it offers all the facilities required to get a detailed understanding of the course.
If you get any queries during the process or the course, you can reach out to our support team.
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:
We accept the following payment options:
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.
Among various training institutes that provide Apache Spark Training, KnowledgeHut stands out due to the focus on job-readiness and hands-on training. Featuring best-in-class training, meticulously designed by our team of experts helps you gain not just theoretical knowledge but also the practical ability to put the learnings into action on the job.
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.
The collection of KnowledgeHut’s tutorials, guides and courses will help understand Spark as well as master it. These tutorials will help you dive deep into the underlying concepts of Spark, after which our certification training will help you to master the technology with real-world hands-on experience and instructor-led sessions.
Feel free to have a look at our blog to get a basic foundational knowledge of Spark.
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:
2) Apache Spark Books:
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.
Apache Spark is a Big Data framework which is in high demand. Spark provides streaming as well as batch capabilities, making it one of the biggest revolutionary changes in the environment of Big Data processing. Hence, it is an ideal framework for people and organizations who are looking for speed data analysis.
Learning this framework will help you climb up the ladder of your career as nowadays more and more companies are eager to adopt Spark in their system.
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:
After completing the Apache Spark and Scala course, you will be able to:
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 has a few big value propositions:
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 :
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
Including Spark support to Azure HDInsight (its cloud-hosted version of Hadoop).
To manage its SystemML machine learning algorithm construction, IBM uses Spark technology.
To run Spark apps developed in Scala, Java, and Python, Amazon uses Apache Spark.
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:
Apache Spark 2.3, SBT, Eclipse, Scala, IntelliJ Idea, PySpark(for Spark with Python)
Follow the below steps given below for installing Spark.
$ tar xvf spark-2.4.3-bin-hadoop2.7.3.tgz
/usr/local/spark
# cd /home/Hadoop/Downloads/
# mv spark-2.4.3-bin-hadoop2.7.3 /usr/local/spark
export PATH = $PATH:/usr/local/spark/bin
$ source ~/.bashrc
$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 is an open-source parallel processing framework for running large-scale data analytics applications across clustered computers. It 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, 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 –
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.