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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

Sat and Sun(4 weeks)
7:00 AM - 11:00 AM PST
for 6 months

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


06th May

  • Duration: 4 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 $67/month for 6 months
  • Enroll

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:
  • 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.


    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 March 2018

News on Apache Spark - March 2018 ...

Recap of Hadoop News for March 2018

News on Hadoop - March 2018 ...

Recap of Apache Spark News for February 2018

News on Apache Spark - February 2018 ...

News on Apache Spark Training

Big Data Information Access :Spark, Presto and Apache Hive (Oh My), channele2e.com, April 18, 2018.

A recent research from the “Big Data-as-a-service” company Qubole found that more than three-quarters of organizations depend on multiple open source big data frameworks to glean insights from data.Apache Spark and Presto are among the fastest growing open source big data engines. The findings show that Apache Spark has grown by 365% in the total number of commands run whereas Presto has increased to 420% in its compute hours. Another major big data engine that also witnessed growth is Apache Hive with number of commands increasing by 129% over the year.The study also revealed the number of users accessing each of the big data platforms.Apache Spark and Hadoop saw 171% and 136% increase in users running commands on the platform respectively. (Source - https://www.channele2e.com/software/big-data/spark-presto-apache-hive/ )

Prostate cancer: Big data unlocks 80 new drug targets.MedicalNewsToday.com,April 17, 2018.

An international team used double -pronged approach of big data and DNA analysis to dig deep into the genetics of prostate cancer.Prostate cancer is among one of the most common types of cancer in men in US with an average of 164,690 cases of prostate cancer and approximately 30,000 deaths to the disease.The researchers took data from 112 men with prostate cancer and combined with data from other studies. A total of 930 patients data was used as a sample for analysis.With the help of latest big data methods , the team gathered novel insights into genetic changes the result in the development and progress of prostate cancer. Having understood the genes involved, they were able to create a map of the proteins that can be coded by these genes.Scientists have found that 80 of the proteins that they have uncovered were potential drug targets and 11 of them were targeted by existing drugs and 7 could be targeted by drugs already present in clinical trials.(Source -https://www.medicalnewstoday.com/articles/321511.php )

Immuta Introduces Apache Spark Ecosystem Support and Automated Governance Reporting for Data Science Programs.BusinessWire.com, April 9, 2018.

Immuta unveiled novel features of its data management platform that includes native Apache Spark SQL policy enforcement and automated governance reporting.These latest additions in Immuta v2.1 will allow organizations to process, secure and audit on massive scale in Apache Spark while providing greater visibility and control on how data is being used with integrated compliance reporting. The latest release extends Immuta’s powerful capability for the Apache Spark ecosystem for large scale processing with native policy enforcement (policies include time windowing, minimization, purpose limitation, dynamix row and column level controls, and automated differential privacy) within spark. (Source : https://www.businesswire.com/news/home/20180409005470/en/Immuta-Introduces-Apache-Spark-Ecosystem-Support-Automated)

MapR Introduces New Capabilities to Build Real-Time Streaming and Global IoT Applications.martechadvisor.com, April 6, 2018

MapR Technologies announced the incorporation of some breakthrough capabilities to its data platform across every cloud. The new enhancements to the platform will help build , powerful, real-time streaming and global IoT applications. The novel enhancements to the MapR Converged Data Platform release 6.0.1 and MapR Expansion Pack 5.0 include Event Streams (MapR-ES), Apache Spark and Apache Drill release 1.13.These enhancements will support streaming pipelines which can stretch across millions of endpoints whilst also providing support for rich analytics that can be used to divide and aggregate streams. (Source : https://www.martechadvisor.com/news/bi-ci-amp-data-visualization/mapr-introduces-new-capabilities-to-build-real-time-streaming-and-iot-applications/ )

Databricks to Host AI Thought Leaders at Spark + AI Summit 2018. Globenewswire.com, April 4, 2018.

The provider of the leading Unified Analytics Platform, Databricks is hosting a Spark + AI summit conference in San Francisco from June 4-6, 2018. The conference will have Marc Andreessen as the keynote speaker who will participate in Fireside Chat with Databricks Co-founder and CEO, Ali Ghodsi. The Spark + AI summit is a leading event for data engineers, data scientists, and business professionals to converse on hot topics in analytics, big data and practical applications of AI.The agenda also has a talk featured by Matei Zaharia, co-founder of Databricks on why large-scale data processing is important to AI applications and how Spark enables data and AI projects. (Source : https://globenewswire.com/news-release/2018/04/04/1460213/0/en/Databricks-to-Host-AI-Thought-Leaders-at-Spark-AI-Summit-2018.html )

Apache Spark Training Jobs

Hive/Spark Big Data Developers/Engineers

Company Name: Knowledgent Group Inc.
Location: Massachusetts
Date Posted: 19th Apr, 2018

Roles and Responsibilities

  • Build high volume data integrations
  • Develop Hadoop architecture, HDFS commands
  • Design & optimize analytical jobs and queries against data in the HDFS/Hive environments
  • Develop Spark framework
  • Build data transformation and processing solutions
  • Develop bash shell scripts, UNIX utilities & UNIX Commands
  • Integration with Salesforce / Veeva CRM / Adobe Experience Manager / Google Analytics / External data sources and other applications.

Spark Developer

Company Name: Hashmap
Location: Charlotte.NC
Date Posted: 26th Mar, 2018
  • Candidates will enhance their platform to add additional functionality as well as write components to ingest transform and export data.
  • They will also have to work with Data Science Team and Data Modelers

Sr Spark Developer

Company Name: CyberCoders
Location: Redwood City
Date Posted: 26th Mar, 2018
  • Work on our data pipeline, ETL systems, and real-time data
  • Come up with solutions for scaling data infrastructure
  • Define and extend an interface for expressing domain-specific analytic queries
  • Translate product requirements into analytic queries and data structures, extending the processing framework and query language as needed
  • Optimize computational primitives for performance and parallelism
  • Architect for performance and scalability: we deliver a real-time experience to customers at scale. You should have experience ...