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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


02
Sep
Sat and Sun(5 weeks)
7:30 AM - 11:30 AM PST
$67/month
for 6 months

07
Oct
Sat and Sun(5 weeks)
7:30 AM - 11:30 AM PST
$67/month
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.

Real world 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

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News on Apache Spark Training

Databricks Partners with RStudio To Increase Productivity of Data Science Teams.InsideBigData.com, June 29, 2018.


Databricks announced its partnership with RStudio to enhance the productivity of data science teams. This collaboration will let the two companies integrate Databricks Unified Analytics Platform with RStudio server to simplify R programming on big data for data scientists. This collaboration will remove all the major roadblocks that put a fullstop to several R-based AI and Machine Learning projects. This collaboration will help data science teams in the following ways - Provide simplified access to large datasets as all datasets will be accessible in the Unified Analytics Platform and data scientists can work on the code in RStudio. Data scientists will be able to use famiair tools and languages to execute R jobs resulting in enhanced productivity among the data science teams. This partnership will provide data scientists with the ability to auto-scale cloud based clusters to handle jobs whilst keeping the overall TCO low. (Source - https://insidebigdata.com/2018/06/29/databricks-partners-rstudio-increase-productivity-data-science-teams/ )

Apache Spark and Big Data: What's Ahead.TDWI.org, June 22, 2018.


With increasing adoption of Apache Spark in the industry as big data grows, here are five big data trends that deserve attention - i) The shift from storage to computational power Apache Spark is at the center of smart-computation evolution because of its large-scale, in-memory data processing. Apache Spark will see a significant growth particularly in highly competitive business domains such as manufacturing, pharma, and finance. ii) Better and Improved Cloud Infrastructures Organizations are using Apache Spark to leverage their rapid innovation cycles from the open source community. It is faster to upgrade to latest versions of the software in the cloud than on any on-premise implementations.Cloud infrastructure has improved over the period of time with investments from Microsoft, Amazon, and Google making it easier for enterprises with large data volumes to adopt a complete cloud-based Spark implementation resulting in widespread adoption of Apache Spark. iii) Improved Security and Governance Models With increasing adoption of Spark, the availability of enterprise- grade security and data governance frameworks will increase that would attract the most conservative business domains such as insurance and finance to adopt spark. iv) The advent of Big Deep Learning Library by Intel The advent of the deep learning library has paved way for completely a new set of users and business use cases that span across deep-learning landscape. v) Increasing demand and popularity for Python and Spark Big data developers and data scientists have adopted Python as the go-to language for programming with Spark as code readability, maintainability and familiarity are better with Python. (Source - https://tdwi.org/articles/2018/06/22/ta-all-apache-spark-and-big-data-whats-ahead.aspx )

Apache Spark Market Size, Share, Growth, Analysis, Forecast to 2025.thefreenewsman.com, June 15, 2018.


Apache Spark market is highly competitive and characterized by continuous changes in customer requirements, technology, industry standards and novel product enhancements. North America held the highest market share of 48% in terms of revenue of the overall apache spark global market. The North America segment is anticipated to grow at the rate of 32.9% throughout the forecast period. According to a study, North America generates 50% of the global data and is one of the fastest growing regions adopting Apache Spark. The other key highlights from the global apache spark market report 2017-2025 are as follows - Data Tier greater than 10PB segment is expected to grow at a compound annual growth rate of 36% during 2018-2025. UK held the highest market share of nearly 30% for the year 2017 in Europe region. Asia-Pacific is anticipated to grow at the highest CAGR of of 36.4% during 2018-2025. (Source - https://thefreenewsman.com/apache-spark-market-size-share-growth-analysis-forecast-to-2025/53286/ )

A $940 million startup that VCs initially rejected is trying to do for pharma what it did for Netflix.ConsultantsInsider.com, June 6, 2018


The 5 year old data crunching startup , Databricks is worth $940 million after its big data framework Apache Spark gained popularity and increased adoption from enterprises like Netflix to Shell. With its new initiative, it aims to provide similar kind of a personalized experience that it provides to companies like Netflix however the new tool will crunch genetics data for pharma companies.The first client to use this tool is a New-York based company Regeneron that manufactures popular drugs for eye and skin conditions. The company has a sizable genetics database of anonymized information from more than 300, 000 people. The data along with Databricks new platform will let the company speed up its drug discovery and development process which would not have been possible before. (Source- https://consultantsinsider.com/articles/A-940-million-startup-that-VCs-initially-rejected-is-trying-to-do-for-pharma-what-it-did-for-Netflix--5b18050fd1c64b1e338d3a3f )

Databricks Helps Turn Clinical and Genomic Big Data into Insights to Improve Patient Lives.BusinessWire.com, June 6, 2018.


Databricks unveiled the Unified Analytics Platform for genomic data processing, AI and tertiary analytics at the Spark+AI summit, an annual gathering of 4000 data engineers, analytics leaders and data scientists. The unified analytics platform will speed up the discovery of critical medical treatments and help healthcare and life sciences organizations to make advancements in personalized medicines and discover new medical treatments . Using this platform, healthcare organizations can now process and analyze large scale genomics data up to 100 times faster than existing solutions to foster critical research.(Source -https://www.businesswire.com/news/home/20180606006101/en/Databricks-Helps-Turn-Clinical-Genomic-Big-Data )

Apache Spark Training Jobs

Apache Spark Developer

Company Name: Optimal Technologies
Location: Linthicum, MD, US
Date Posted: 21st May, 2018
Description:

 

Description of specific Duties in a typical workday for this position:

  • Provides design recommendations based on long-term IT organization strategy.
  • Develops enterprise level application and custom integration solutions including major enhancements and interfaces, functions and features. Uses a variety of platforms to provide automated systems applications to customers.
  • Provides expertise regarding the integration of applications across the business.
  • Determines specifications, then plans, designs, and develops the most complex...

Apache Spark Developer

Company Name: Optimal Solutions & Technologies (OST, Inc.)
Location: Baltimore, MD , USA
Date Posted: 12th May, 2018
Description:

Description of specific Duties in a typical workday for this position:

  • Provides design recommendations based on long-term IT organization strategy.
  • Develops enterprise level application and custom integration solutions including major enhancements and interfaces, functions and features. Uses a variety of platforms to provide automated systems applications to customers.
  • Provides expertise regarding the integration of applications across the business.
  • Determines specifications, then plans, designs, and develops the most complex and business criti...

Apache Spark Developer

Company Name: Optimal Solutions & Technologies (OST, Inc.)
Location: Baltimore, MD , USA
Date Posted: 12th May, 2018
Description:

Description of specific Duties in a typical workday for this position:

  • Provides design recommendations based on long-term IT organization strategy.
  • Develops enterprise level application and custom integration solutions including major enhancements and interfaces, functions and features. Uses a variety of platforms to provide automated systems applications to customers.
  • Provides expertise regarding the integration of applications across the business.
  • Determines specifications, then plans, designs, and develops the most complex and business criti...
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