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Apache Spark Online Training in 30 days

  • Live online faculty led training.
  • Create applications using Spark Streaming, Spark SQL, MLlib and Graphx.
  • Learn how to run Apache Spark on a cluster
  • Learn RDDs operations on dataframes.

Upcoming Live Apache Spark Training


25
Nov
Sat and Sun(6 weeks)
5:30 PM - 8:30 PM PST
$399

09
Dec
Sat and Sun(5 weeks)
7:00 AM - 11:00 AM PST
$399

Want to work 1 on 1 with a mentor. Choose the project track

About Apache Spark Training Course

Project Portfolio

Build an online project portfolio with your project code and video explaining your project. This is shared with recruiters.

36 hrs live hands-on sessions with industry expert

The live interactive sessions will be delivered through online webinars. All sessions are recorded. All instructors are full-time industry Architects with 14+ years of experience.

Remote Lab and Projects

Lab will test your practical knowledge. Assignments include creating streaming applications with Apache Spark, pairing RDD operations on dataframes and writing efficient Spark SQL queries. The final project will give you a complete understanding of working with Apache Spark.

Lifetime Access & 24x7 Support

Once you enroll for a batch, you are welcome to participate in any future batches free. If you have any doubts, our support team will assist you in clearing your technical doubts.

Weekly 1-on-1 meetings

If you opt for the project track, you will get 6 thirty minute one-on-one sessions with an experienced Apache Spark Developer who will act as your mentor.

Benefits of Apache Spark Certification

How will this help me get jobs?

  • Display Project Experience in your interviews

    The most important interview question you will get asked is "What experience do you have?". Through the DeZyre live classes, you will build projects, that have been carefully designed in partnership with companies.

  • Connect with recruiters

    The same companies that contribute projects to DeZyre also recruit from us. You will build an online project portfolio, containing your code and video explaining your project. Our corporate partners will connect with you if your project and background suit them.

  • Stay updated in your Career

    Every few weeks there is a new technology release in Big Data. We organise weekly hackathons through which you can learn these new technologies by building projects. These projects get added to your portfolio and make you more desirable to companies.

What if I have any doubts?

For any doubt clearance, you can use:

  • Discussion Forum - Assistant faculty will respond within 24 hours
  • Phone call - Schedule a 30 minute phone call to clear your doubts
  • Skype - Schedule a face to face skype session to go over your doubts

Do you provide placements?

In the last module, DeZyre faculty will assist you with:

  • Resume writing tip to showcase skills you have learnt in the course.
  • Mock interview practice and frequently asked interview questions.
  • Career guidance regarding hiring companies and open positions.

Apache Spark Training Course Curriculum

Module 1

Introduction to Big Data and Spark

  • Overview of BigData and Spark
  • MapReduce limitations
  • Spark History
  • Spark Architecture
  • Spark and Hadoop Advantages
  • Benefits of Spark + Hadoop
  • Introduction to Spark Eco-system
  • Spark Installation
Module 2

Introduction to Scala

  • Scala foundation
  • Features of Scala
  • Setup Spark and Scala on Unbuntu and Windows OS
  • Install IDE's for Scala
  • Run Scala Codes on Scala Shell
  • Understanding Data types in Scala
  • Implementing Lazy Values
  • Control Structures
  • Looping Structures
  • Functions
  • Procedures
  • Collections
  • Arrays and Array Buffers
  • Map's, Tuples and Lists
Module 3

Object Oriented Programming in Scala

  • Implementing Classes
  • Implementing Getter & Setter
  • Object & Object Private Fields
  • Implementing Nested Classes
  • Using Auxilary Constructor
  • Primary Constructor
  • Companion Object
  • Apply Method
  • Understanding Packages
  • Override Methods
  • Type Checking
  • Casting
  • Abstract Classes
Module 4

Functional Programming in Scala

  • Understanding Functional programming in Scala
  • Implementing Traits
  • Layered Traits
  • Rich Traits
  • Anonymous Functions
  • Higher Order Functions
  • Closures and Currying
  • Performing File Processing
Module 5

Foundation to Spark

  • Spark Shell and PySpark
  • Basic operations on Shell
  • Spark Java projects
  • Spark Context and Spark Properties
  • Persistance in Spark
  • HDFS data from Spark
  • Implementing Server Log Analysis using Spark
Module 6

Working with Resilient Distributed DataSets (RDD)

  • Understanding RDD
  • Loading data into RDD
  • Scala RDD, Paired RDD, Double RDD & General RDD Functions
  • Implementing HadoopRDD, Filtered RDD, Joined RDD
  • Transformations, Actions and Shared Variables
  • Spark Operations on YARN
  • Sequence File Processing
  • Partitioner and its role in Performance improvement
Module 7

Spark Eco-system - Spark Streaming & Spark SQL

  • Introduction to Spark Streaming
  • Introduction to Spark SQL
  • Querying Files as Tables
  • Text file Format
  • JSON file Format
  • Parquet file Format
  • Hive and Spark SQL Architecture
  • Integrating Spark & Apache Hive
  • Spark SQL performance optimization
  • Implementing Data visualization in Spark

Upcoming Classes for Apache Spark Training

 

Apache Spark Training Course Reviews

FAQs for Apache Spark Training Online Course

  • What should be the system requirements for me to learn apache spark online?

    For you to pursue this online spark training –

    1. Your system must have a 64 bit operating system.
    2. Minimum 8GB of RAM.
  • I want to know more about Apache Spark Certification training online. Whom should I contact?

    You can click on the Request Info button on top of the page to request a callback from one of our career counsellors to have your query resolved.  For instant support, click on the Live Chat option popping up on the page.

  • Who should do this Apache Spark online course?

    Students or professionals planning to pursue a lucrative career in the field of big data analytics must do this spark online course. Research and analytics professionals, BI professionals, Data Scientists, IT testers, Data warehouse professionals who would like to learn about the emerging big data tools and technologies must pursue this online spark course.

     

  • What are prerequisites for learning Apache Spark?

    This course is designed for people who are into coding like, software engineers, data analysts/engineers or ETL developers. You need to have basic knowledge of Unix/Linux commands. It would help if you are familiar with Python/Java or Scala programming.

  • Who will be my faculty?

    You will be learning from industry experts who have more than 9 years of experience in this field. 

  • Do I need to know Hadoop to learn Apache Spark?

    No prior knowledge of Hadoop or distributing programming concepts is required to learn this Apache Spark course.

  • What is Apache Spark?

    Apache Spark was developed at UC Berkeley. It is an open source fast, general cluster computing framework developed for big data processing and analytics. Apache Spark is written in Scala which is a functional programming language that runs in a JVM. Apache Spark can run on top of Hadoop, Mesos, cloud environment or in standalone. 

  • What is the difference between Apache Spark and Hadoop MapReduce?

    Apache Spark takes the Mapreduce concepts to the next level. Apache Spark has a higher level API for faster, easier development. Apache Spark has low latency near real time processing. Its in-memory data storage is huge and can give up to 100x performance improvement.

  • What is the career scope after learning Apache Spark?

    Pinterst, Baidu, Alibaba Taobao, Amazon, eBay Inc, Hitachi Solutions, Shopify, Yahoo! are just some of the companies who are powered by Apache Spark. More companies are adopting Spark for faster data processing. Spark is one of the hottest skills to have right now for a high paying developer position.

  • Do I need to learn Hadoop first to learn Apache Spark?

    Apache Spark makes use of HDFS component of the Hadoop ecosystem but it is not mandaotry for one to know Hadoop to work with Apache Spark. As a big data developer, you will not find any overlap between the two. Apache Spark promotes parallel computations through function calls whereas in Hadoop you write MapReduce jobs by inheriting Java classes.The specifics of running a Hadoop Cluster and a Spark Cluster are completely different. So,even if a person does not know Hadoop ,he/she can get started with learning apache spark.

Apache Spark Training short tutorials

  • Do you need to know machine learning in order to be able to use Apache Spark?

    Apache Spark is a distributed computing platform for managing large datasets and is oftenly assoicated with machine learning. However, machine learning is not the only use case for Apache Spark , it is an excellent framework for lambda architecture applications, MapReduce applications, Streaming applications, graph based applications and for ETL.Working with a Spark instance requires no machine learning knowledge.

  • What kinds of things can one do with Apache Spark Streaming?

    Apache Spark Streaming is particularly meant for real-time predictions and recommendations.Spark streaming lets users run their code over a small piece of incoming stream in a scale. Few Spark use cases where Spark Streaming plays a vital role -

    • You just walk by the Walmart store and the Walmart app sends you a push notification with a 20% discount on your favorite clothing brand.
    • Spark streaming can also be used to get the top most visited pages of a website.
    • For a stream of weblogs, fi you want to get alerts within seconds-Spark Streaming is helpful.

     

     

  • How to save MongoDB data to parquet file format using Apache Spark?

    The objective of this questions is to extract data from local MongoDB database, to alter save it in parquet file format with the hadoop-connector using Apache Spark. The first step is to convert MongoRDD variable to Spark DataFrame, which can be done by following the steps mentioned below:

    1. A Case class needs to be created to represent the data saved in the DBObject.

    case class Data(x: Int, s: String)

    2. This is to be follwed by mapping vaues of RDD instances to the respective Case Class

    val dataRDD = mongoRDD.value.map {obj => Data(obj.get("x", obj.get("s")))}

    3. Using sqlContext RDD data can be converted to DataFrame

    val SampleDF = sqlContext.createDataFrae(dataRDD)

     

  • What are the differences between Apache Storm and Apache Spark?

    Apache Spark is an in-memory distributed data analysis platform, which is required for interative machine learning jobs, low latency batch analysis job and processing interactive graphs and queries. Apache Spark uses Resilient Distributed Datasets (RDDs). RDDs are immutable and are preffered option for pipelining parallel computational operators. Apache Spark is fault tolerant and executes Hadoop MapReduce jobs much faster.
    Apache Storm on the other hand focuses on stream processing and complex event processing. Storm is generally used to transform unstructured data as it is processed into a system in a desired format.

    Spark and Storm have different applications, but a fair comparison can be made between Storm and Spark streaming. In Spark streaming incoming updates are batched and get transformed to their own RDD. Individual computations are then performed on these RDDs by Spark's parallel operators. In one sentence, Storm performs Task-Parallel computations and Spark performs Data Parallel Computations.

  • How to setup Apache Spark on Windows?

    This short tutorial will help you setup Apache Spark on Windows7 in standalone mode. The prerequisites to setup Apache Spark are mentioned below:

    1. Scala 2.10.x
    2. Java 6+
    3. Spark 1.2.x
    4. Python 2.6+
    5. GIT
    6. SBT

    The installation steps are as follows:

    1. Install Java 6 or later versions(if you haven't already). Set PATH and JAVE_HOME as environment variables.
    2. Download Scala 2.10.x (or 2.11) and install. Set SCALA_HOME and add %SCALA_HOME%\bin in the PATH environmental variable.
    3. The next step is install Spark, which can be done in either of two ways:
    • Building Spark from SBT
    • Using pre-built Spark package

    In oder to build Spark with SBT, follow the below mentioned steps:

    1. Download SBT and install. Similarly as we did for Java, set PATH AND SBT_HOME as environment variables.
    2. Download the source code of Apache Spark suitable with your current version of Hadoop.
    3. Run SBT assembly and command to build the Spark package. If Hadoop is not setup, you can do that in this step.
    sbt -Pyarn -pHadoop 2.3 assembly
    1. If you are using prebuilt package of Spark, then go through the following steps:
    2. Download and extract any compatible Spark prebuilt package.
    3. Set SPARK_HOME and add %SPARK_HOME%\bin in PATH for environment variables.
    4. Run this command in the prompt:
    bin\spark-shell
  • How to read multiple text files into a single Resilient Distributed Dataset?

    The objective here is to read data from multiple text files after extracting them from a HDFS location and process them as a single Resilient Distributed Dataset for further MapReduce implementation. Some of the ways to accomplish this task are mentioned below:

    1. The command 'sc.textFile' can mention entire directories of HDFS, as well as multiple directories and wildcards separated by commas.

    sc.textFile("/system/directory1,/system/paths/file1,/secondary_system/directory2")

    2. A union function can be used to create a centralized Resilient Distributed Dataset.

    var file1 = sc.textFile("/address/file1")
    var file2 = sc.textFile("/address/file2")
    var file3 = sc.textFile("/address/file3")
    
    val rdds = Seq(file1, file2, file3)
    var sc = new SparkContext(...)
    
    val unifiedRDD = sc.union(rdds)

Articles on Apache Spark Training

Recap of Apache Spark News for June 2017


News on Apache Spark - June 2017 ...

Recap of Hadoop News for June 2017


News on Hadoop - June 2017 ...

Hadoop Cluster Overview: What it is and how to setup one?


What is a Hadoop Cluster? ...

News on Apache Spark Training

Five Big Data Trends To Influence AI In 2018.CXOtoday.com, November 22, 2017.


With the growth of AI expected to be greater in 2018, the convergence of AI and Big Data is the most important development for businesses across the globe. Some of the opportunities and big data trends that will influence AI in the next year - AI is all set to transform the global workforce - A Gartner report predicts that AI will create 2.3 million jobs by end of 2020. Growth of Chatbots - It is difficult for a human assistant to keep up with the billions of bytes of data generated every second. To keep up with the information deluge, businesses will focus on making consumer lives easier through real-time support on digital channel with the use of chat bots. AI-integrated mobile and web applications -AI driven technologies like machine learning and deep learning to play a major role in the development of mobile and web apps for real-time predictions. AI and Cloud Computing -Forrester predicts that organizations will embrace on public-cloud-first policy in 2018 for big data analytics as cloud implementations cut down the entry cost for AI driven technologies and companies can host all data on a single instance. Machine Learning and Cognitive Technologies - 2018 will see machine learning evolve into a quicker and smarter tool for business sectors as diverse as healthcare, finance, retail , online gaming, travel and more. (Source : http://www.cxotoday.com/story/five-big-data-trends-to-influence-ai-in-2018/)

Cloudera Bets Its Future on Scalability for Spark, GATK Support.Genomeweb.com, November 15, 2017.


Shawn Dolley, global industry leader of health and life sciences at Cloudera said - “Spark "is becoming the lingua franca of research computing pipeline generation”. Earlier Cloudera was a support organization for most of the big data technologies but now one third of the demand for Cloudera services is from folks working on computational pipelines and they want it to be in Apache Spark. Cloudera ( of which Intel holds a stake of 18% ) is among the leading providers of support for Apache Spark when it comes to clinical data. (Source : https://www.genomeweb.com/informatics/cloudera-bets-its-future-scalability-spark-gatk-support)

Microsoft launches Azure Databricks, a new cloud data platform based on Apache Spark.GeekWire.com, November 15, 2017.


Azure users interested in gleaning meaningful business insights by parsing huge amounts of data will soon be able to use Azure Databricks built around the popular open source big data framework and developed in collaboration with Databricks. The first Spark-as-a-service of any of the cloud vendors , Azure Databricks will be used to model real-time data patterns. For instance, the platform would be used to measure how guests in a hotel move around the lobby so the hotel can decide on the best place furniture and guest service. (Source : https://www.geekwire.com/2017/microsoft-launches-azure-databricks-new-cloud-data-platform-based-apache-spark/)

The future of the future: Spark, big data insights, streaming and deep learning in the cloud.Zdnet.com, November 1, 2017


With Apache Spark booming and its community growing at a rapid pace, spark is making waves in the big data ecosystem.Though Spark in the cloud is nothing new , Databricks is announcing it latest addition Delta - smart cache layer in the cloud which will offer scalability and elasticity in the cloud.A smart cache layer like Delta brings an array of benefits for people working in the cloud only if they are willing to shell out big bucks. However, Databricks major focus is on growing its proprietary platform by making streaming and deep learning work together in the cloud. (Source : http://www.zdnet.com/article/the-future-of-the-future-spark-big-data-insights-streaming-and-deep-learning-in-the-cloud/ )

NEC claims new machine learning capabilities 50X faster than Apache Spark. SiliconAngle.com, July 3, 2017


Japanese computer organization NEC Corp. that the new data processing technology developed by them speeds up machine learning on vector computers by up to 50 times that of Apache Spark. The new data processing technology leverages Sparse Matrix data structure to boost the performance of machine learning tasks on vector computers.Apart from the new data processing technology , NEC has also developed a middleware using sparse matrix data structures to simplify the use of machine learning. The middleware can be directly launched from Spark infrastructure without having to do any additional programming.(Source : https://siliconangle.com/blog/2017/07/03/nec-claims-new-machine-learning-capabilities-50x-faster-apache-spark/)

Apache Spark Training Jobs

Technology Engineer IV

Company Name: Technology Engineer IV
Location: Reston, VA
Date Posted: 23rd Nov, 2017
Description:

Job Responsibilities -

  • Interface, partner, and influence stakeholders to promote simplification, standardization and innovation and to ensure risk are understood and minimized
  • Establish policies and standard operating procedures to enable consistent and repeatable execution
  • Propose appropriate changes to standards, policies, and procedure based on emerging business/technology trends and operational challenges with the existing guidelines utilizing performance metric and operational SLA
  • Plan for adequate capability on systems based on utilization metrics and planned projects to establish supply and demand forecasts
  • Design and verify the technology solution meets business and technical requirements and is in compliance with enterprise ar...

JAVA or C++ developer with Scala / Spark skills

Company Name: Planaxis| Groupaxis
Location: Toronto, ON
Date Posted: 01st Nov, 2017
Description:

Responsibilities 

  • Responsible for analysis, design, coding and testing of new applications or enhancements to existing applications using Java-based & C++ technologies.
  • Must be able to apply SDLC concept and have a proven track record of delivering solid, robust applications.
  • Contribute and support to operational problems/resolutions to new and existing applications. Big Data environment : Scala / Spark, Very interesting technical environment (NoSQL, Big Data, Java8, Spring).
  • Provide l...

Software Development Engineer, Apache Spark and EMR

Company Name: Amazon Lab126
Location: Cupertino, CA
Date Posted: 01st Nov, 2017
Description:

Responsibilities 

  • You will provide technical leadership and also contribute to the definition, development, integration, test, documentation, and support of software applications across multiple platforms.​
  • Successful candidates must be motivated to work in a data driven environment, have a desire to drive process improvement, and be capable of translating high-level, ambiguous business goals to working software solutions.
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