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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


24
Jun
Sat and Sun(6 weeks)
6:00 PM - 09:00 PM PST
$399

15
Jul
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.

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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.

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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.

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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.

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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

June 24th

  • Duration: 6 weeks
  • Days: Sat and Sun
  • Time: 6:00 PM - 09:00 PM PST
  • 6 thirty minute 1-to-1 meetings with an industry mentor
  • Customized doubt clearing session
  • 1 session per week
  • Total Fees $399
    Pay as little as $66/month for 6 months, during checkout with PayPal
  • Enroll

July 15th

  • Duration: 5 weeks
  • Days: Sat and Sun
  • Time: 7:00 AM - 11:00 AM PST
  • 6 thirty minute 1-to-1 meetings with an industry mentor
  • Customized doubt clearing session
  • 1 session per week
  • Total Fees $399
    Pay as little as $66/month for 6 months, during checkout with PayPal
  • Enroll
 

Apache Spark Training Course Reviews

See all 58 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

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  • 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

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


What is a Hadoop Cluster? ...

Spark SQL for Relational Big Data Processing


With increasing usage of Spark in production, big data developers often combine various spark components...

Getting to Know Hadoop 3.0 -Features and Enhancements


Hadoop was first made publicly available as an open source in 2011, since then it has undergone major changes ...

News on Apache Spark Training

Impetus Technologies Unveils New, TensorFlow-Based Deep Learning Feature on Apache Spark for StreamAnalytix.PRNewswire.com, June 15, 2017.


The leading provider of big data software and service company, Impetus Technologies released an integrated , deep learning capability for its Stream Analytix platform which will be showcased at the DataWorks Summit 2017 in San Jose, California. The company will showcase an image recognition application that will run on a Spark Streaming pipeline on Stream Analytix. Stream Analytics and deep learning in combination provides new breed of application with machine learning capabilities in voice analytics, anomaly detection and IoT. (Source : http://www.prnewswire.com/news-releases/impetus-technologies-unveils-new-tensorflow-based-deep-learning-feature-on-apache-spark-for-streamanalytix-300474651.html)

Microsoft’s new Machine Learning library make data scientists more productive on Apache Spark. Mspoweruser.com, June 8, 2017.


Microsoft released a new machine learning library for data scientists to be more productive on Apache Spark. The MMLSpark library will provide simplified consistent API’s for handling various data types like categoricals or text,will increase the rate of experimentation and will help leverage cutting edge machine learning methods on large datasets. Data scientists just need to pass the data to the model and the MMLSpark library will do the rest. Data scientists can easily make changes to the feature space and algorithm without having to worry about recording the pipeline. Some of the capabilities of MMLSpark include -scalable image processing pipelines, DNN Featurization, Training on a GPU node, etc.(Source : https://mspoweruser.com/microsofts-new-machine-learning-library-make-data-scientists-productive-apache-spark/)

MemSQL Showcases Machine Learning Image Recognition for Apache Spark.GlobeNewsWire.com, June 5, 2017.


The provider of fastest real-time data warehouse , MemSQL is hosting a session on June 7, 2017 at the Spark Summit 2017 that will dig in about the various image recognition techniques using Apache Spark and how these techniques can be applied in production.The session will be led by the CTO of MemSQL, Nikita Shamgunov at Kiosk 7 in the Expo Hall at Moscone West, in San Francisco from 2.40 PM to 3.10 PM. The key highlights of the sessions include - Use of a fast relational datastore to persist data from Spark Architectural considerations in building an image recognition pipeline Real-time capabilities for instant matches Advantages and pitfalls of using particular approaches.(Source: http://www.globenewswire.com/news-release/2017/06/05/1008243/0/en/MemSQL-Showcases-Machine-Learning-Image-Recognition-for-Apache-Spark.html)

Apache Spark MapR Connector Provides JSON Support. I-programmer.info, June 5, 2017


MapR-DB , a high performance NoSQL database provides support for 2 primary data models - wide column tables and JSON documents.A new spark connector has been unveiled for MapR-DB JSON data model that will provide developers API’s access to MapR-DB JSON documents from Spark through the Open JSON application interface (OJAI). This connector will provide support for loading data from MapR-DB table as a Spark RDD of OJAI documents and save a Spark RDD into a MapR-DB JSON table. The connector provides support for data frames and dataset API’s making it easy to query MapR-DB binary tables and HBase tables directly with Apache Spark. This makes it easier to construct faster data pipelines by removing intermediary layers, if any and also reduces latency related with data movement. (Source : http://www.i-programmer.info/news/167-javascript/10822-apache-spark-mapr-connector-provides-json-support.html)

IBM sparks conversations about analytics, processing and the hunt for ET.Computing.co.uk,June 5, 2017.


IBM data scientists and developers will present multiple talks on various uses of Apache Spark framework that will include its applications in parallel processing and storage. The key highlights of the presentation by IBM scientists at the Spark Summit include - Understanding how to make the most of distributed storage that will be presented through a talk on “‘Running Apache Spark on a High-Performance Cluster Using RDMA and NVMe Flash”. Another talk on how to do perfect parallel processing will be presented by Kazuaki Ishizaki's focussing on machine learning library framework and its internal API’s. IBM scientist Gil Vernick will present a talk on NASA’s SETI project on IBM Cloud platform. (Source : https://www.computing.co.uk/ctg/news/3011305/ibm-sparks-conversations-about-analytics-processing-and-the-hunt-for-et)

Apache Spark Training Jobs

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Big Data Developer

Company Name: United Health Group
Location: Minnetonka, MN or Basking Ridge, NJ
Date Posted: 20th Jun, 2017
Description:

Primary Responsibilities:

  • Performing all phases of software engineering including requirements analysis, application design, and code development and testing
  • Designing/implementing product features in collaboration with business and IT stakeholders
  • Designing reusable Java components, frameworks and libraries
  • Working closely with the Architecture group and driving solutions
  • Implementing the data management framework for the Data Lake
  • Supporting the implementation and driving to stable state in production
  • Providing alternative design solutions and project estimates

Big Data Platform Engineer

Company Name: Celgene Corporation
Location: Summit, NJ US
Date Posted: 16th Jun, 2017
Description:

•Perform configuration, patching, upgrades of the Cloudera environments and associated tools

•Own and resolve any opportunities or issues related to the operations of the platform across multiple tenants.

•Create detailed designs and POCs to enable new workloads and technical capabilities on the Platform.  Work with the platform and infrastructure engineers to implement these capabilities in production.

•Create full visibility into the health and utilization of the platform through use of real-time dashboards, alerts and other mechanisms.

•Manage workloads and enable workload optimization including managing resource allocation and sche...

Software Engineer - Spark Stack

Company Name: Noblis NSP, LLC
Location: Reston, VA
Date Posted: 14th Jun, 2017
Description:
  • Work with a small team to develop a distributed framework
  • Fine tune framework to meet large scale processing requirements
  • Participates in all facets of project development including requirements development, implantation of analytics, testing, and deployment.
  • Help provision and maintain a mid-scale staging environment