Talk to our career counsellor
1-844-696-6465 (US Toll Free)

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


28
May
Sat and Sun(5 weeks)
7:00 AM - 11:00 AM PST
$399

24
Jun
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.

feature

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.

feature

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.

feature

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.

feature

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

May 28th

  • 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

June 24th

  • 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 53 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

View all 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

View all Blogs

Top Apache Spark Certifications to Choose from in 2017


Apache Spark, a fast moving apache project with significant features and enhancements being rolled out rapidly is one of the most in-demand big data skills along with Apache Hadoop. Apache Spark skills are in high-demand, with no end to this pattern in ...

Recap of Apache Spark News for April 2017


News on Apache Spark - April 2017 ...

Recap of Hadoop News for April 2017


News on Hadoop-April 2017 ...

News on Apache Spark Training

ESG Lab Affirms Trifacta's Photon Compute Engine is the Fastest, Most Efficient Non-Distributed Processing Engine for Data Wrangling. MarketWired.com, May 16, 2017.


Enterprise Strategy Group (ESG) evaluated the efficiency and speed of Trifacta’s Photon Compute Engine for data wrangling. Apart from Photon, Trifacta also supports other multi-purpose engines like Apache Spark and Google Dataflow. However, Trifacta’s in-memory engine, Photon was evaluated as the most efficient and fastest data processing engine for wrangling data sets which do not require parallel processing. Photon can complete transformations on single node environments 6 times faster than spark whilst using 98% less memory when compared to Spark. (Source : http://www.marketwired.com/press-release/esg-lab-affirms-trifactas-photon-compute-engine-is-fastest-most-efficient-non-distributed-2216670.htm)

Impetus Technologies to Host Webinar Highlighting Real-Time Data360 on Apache Spark.PRNewsWire.com, May 10, 2017


Impetus Technologies hosted a “Real-Time Data360 on Apache Spark” webinar on May 12 for people who want to learn all-in-one apache spark strategy for big data analytics. The main focus of the webinar was to discuss about the challenges IT teams face when choosing one vendor for data ingestion , one vendor for data wrangling and another one for machine learning analytics and some other vendor for data visualization. For organizations that have already opted to use Apache Spark as a big data framework , it is easier to do big data analytics as it is an all-in-one platform.The only challenge here in using Spark as the all-in-one platform is finding skilled and talented Scala / Java programmers for building Spark applications.(Source : http://www.prnewswire.com/news-releases/impetus-technologies-to-host-webinar-highlighting-real-time-data360-on-apache-spark-300455601.html)

Nordea banks on move to machine-led decision making.Diginomica.com, May 9, 2017.


Swedish Bank Nordea is moving to machine-led decision making. It has realized the need to implement a better analytic system. The bank has implemented Cloudera data lake architecture based on Hadoop in an attempt to enhance its data gathering capabilities. This architecture helps the bank to produce, report, and monitor core data at a rapid pace.Alasdair Anderson, Nordea’s head of data engineering states that the key to its set-up is the use of cluster technology -Apache Spark that underlies the hadoop data lake. For the team at Nordea, Apache Spark is a natural evolution from the MapReduce era of Hadoop.(Source : http://diginomica.com/2017/05/09/nordea-banks-move-machine-led-decision-making/)

Using Apache Spark Machine Learning for Pattern Detection. RTInsights.com, May 2, 2017.


Predicting consumer behavior is the key to successful marketing but a major challenge here is on how to filter out the noise from customers who are ready to buy. Consumer behavior data is usually on the scale of petabytes, analytic queries can tax data stores. Alexander Sadovsky, director of data science at Oracle said that they are solving the problem by moving Oracle Data Cloud from an on-premise single machine for data processing to cloud based Hive and ultimately to Apache Spark cluster. Moving to spark cluster will help them process data faster with several in-built machine learning libraries.(Source : https://www.rtinsights.com/using-apache-spark-machine-learning-to-predict-consumer-behavior/)

Impetus Technologies Reveals Winners of Spark Streaming Innovation Contest. PRNewsWire.com, April 18,2017.


600 people participated in the Spark Streaming Innovation Contest. The registrants around the world were competing to build a real-world anomaly detection problem using the visual development platform StreamAnalytix which leverages Apache Spark in batch and streaming modes to create real-time ML applications. The participants were evaluated based on the quality of the application built, extent and quality of StreamAnalytix usage and also on how well the solution was documented. A total of $18,000 prize money was awarded to the winners - Grand prize winner (awarded $10,000) – Venu Kanaparthy, Redlands, California, First runner-up (awarded $5,000) – Anindya Saha, Foster City, California and Second runner-up (awarded $3,000) – Kalyan Janaki, Denver, Colorado. (Source : http://www.prnewswire.com/news-releases/impetus-technologies-reveals-winners-of-spark-streaming-innovation-contest-300440594.html)

Apache Spark Training Jobs

View all Jobs

Sr. Platform Application Developer

Company Name: Starbucks
Location: Seattle, WA
Date Posted: 21st May, 2017
Description:

Responsibilities and essential job functions include but are not limited to the following: 

  • Collaborates with application development teams to understand application development requirements and use cases.
  • Tracks, studies and understands application development platform technology trends.
  • Codes, tests, debugs, implements and documents prototypes to validate and demonstrate new application platforms.
  • Designs, implements and documents new platform technologies including best patterns and engineering practices as well as integration with exis...

Big Data Developer

Company Name: JP Morgan Chase & Co
Location: Jersey City, NJ
Date Posted: 21st May, 2017
Description:
  • Component Software Design & Development.
  • Ensuring excellent practices are utilized in delivering Big Data Management and Integration Solutions.
  • Ensuring design decisions can be actioned by the development team.
  • Participating in agile development projects.
  • Acting as a role model for all best practices, ensuring consistency across entire team.
  • Mentoring technical development team on optimal utilization of Big Data solutions and Apache Open Source Software.
  • Helping build a great team.
  • Leveraging new and emerging practices for Enterprise Data Architecture.
  • Engage in enterprise-level systems component design and implementation.
  • Systems integration, includin...

Big Data Developer

Company Name: Mitchell Martin
Location: Pennington, NJ
Date Posted: 15th May, 2017
Description:

Job Responsibilities:
-Participate in Agile development on a large Hadoop-based data platform as a member of a distributed team. 
-Code programs to load data from diverse data sources into Hive structures using SQOOP and other tools. 
-Translate complex functional and technical requirements into detailed design. 
-Analyze vast data stores. 
-Code business logic using Python/Scala on Apache Spark. 
-Create workflows using Oozie. 
-Code and test prototypes. 
-Code ...