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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


28
Jan
Sat and Sun(5 weeks)
7:00 AM - 11:00 AM PST
$67/month
for 6 months

11
Feb
Sat and Sun(5 weeks)
7:00 AM - 11:00 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.

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

28th Jan

  • 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 $67/month for 6 months
  • Enroll

11th Feb

  • 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 $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:
    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 December 2017


News on Apache Spark - December 2017 ...

Recap of Hadoop News for December 2017


News on Hadoop - December 2017 ...

Recap of Apache Spark News for November 2017


News on Apache Spark - November 2017 ...

News on Apache Spark Training

How Big Data Can Help Rebuild America's Aging Infrastructure.ScientificAmerican.com, January 17,2018.


America’s infrastructure systems were built decades ago and studies show that the increased maintenance costs and delayed maintenance are obstructing the economic performance.Moreover, engineers are raising safety concerns and warnings that most of the bridges are structurally unsound and obsolete waste water systems pose threat to the common public.Big data and related technologies can help America establish the foundation for future adoption of AI and robotics that will create a zero failure, sustainable and highly resilient infrastructure systems. The infrastructure systems can be developed by utilizing sensors that include wireless sensor networks as a component of IoT. Wireless sensor networks will warn engineers when the key elements of highways, buildings and bridges are weak so that immediate action could be taken.These sensors will create huge amounts of data.Earlier, organizing huge amounts of engineering data was impossible economically, but today integrating big data with traditional engineering practices can help improve the effectiveness and efficiency of engineering process systems at economical cost.(Source : https://blogs.scientificamerican.com/observations/how-big-data-can-help-rebuild-americas-aging-infrastructure/)

Databricks Cache Boosts Apache Spark Performance. Databricks.com, January 9, 2018.


Databricks announced the availability of Databricks Cache, a runtime feature as a part of its unified analytics platform can enhance the scan speed of Apache Spark workloads by 10x without having to make any application code changes.Databricks cache automatically caches any input data for a particular user and load balances it across a spark cluster. It also makes use of NVMe SSD hardware with columnar compression techniques that help improve interactive and reporting workloads performance by 10 times. This novel runtime feature can cache 30 times more data than Apache Spark’s in-memory cache. (Source : https://databricks.com/blog/2018/01/09/databricks-cache-boosts-apache-spark-performance.html)

Top 5 Mistakes when Writing Spark Applications.InsideBigData.com, January 7, 2018


Apache park has eased development and is helping developers begin writing distributed programs in just few lines of code that can run on multiple machines and generate business value. Despite the fact that Apache Spark program are easy to read and write , it does not mean that users will not come across issues like out of memory error, slow performing jobs and long running jobs. Most of the issues with Apache Spark have nothing to do with spark features but the approach that we follow when using it.Mark Grover, a software engineer at Cloudera goes on to highlight the common mistakes that developers should avoid when writing Apache Spark applications - i) No Spark Shuffle block should be greater than 2GB. ii) Follow appropriate DAG management iii) To avoid exceptions , perform Shading. iv) For every spark application you build, the Number of Executors, Cores of each executor and Memory for each executor must be decided on carefully. v) Follow two stage aggregation using salted and unsalted keys to speed up jobs. (Source : https://insidebigdata.com/2018/01/07/top-5-mistakes-writing-spark-applications/)

Artificial Intelligence Needs Big Data, and Big Data Needs AI. RTInsights.com, December 26, 2017.


Big Data and Artificial Intelligence have formed a symbiotic relationship with each other and they need each other to reap the fruit of what they promise. Mike Manchett, senior analyst with Taneja Group who has been observing the revolution in the AI market said that Apache Spark is the Spark for AI development using big data.Artificial Intelligence is a resource intensive environment and many organizations do not have the infrastructure for this. Under such circumstances, open source tools like Apache Spark make this proposition cost effective and compelling.Apache Spark has gained widespread adoption for its in-memory, real-time processing and fast machine learning at scale.(Source : https://www.rtinsights.com/artificial-intelligence-needs-big-data-and-big-data-needs-ai/)

53% of Companies are adopting Big Data Analytics. Forbes.com, December 24,2017


Big data adoption has reached 53% in 2017 up from 17% in 2015. The leading early adopters of big data include telecom and financial sectors with data warehouse optimization being the top use case for it. The softwares that have gained popularity for big data are Apache Spark, Hadoop MapReduce and YARN. 30% of the organizations surveyed consider Apache Spark a critical component of big data strategies and 20% consider Hadoop MapReduce and YARN a critical component. The big data access methods that are most preferred by organizations include Spark SQL, Hadoop HDFS, Hadoop Hive and Amazon S3 with 73% of the organizations considering Spark SQL as the key component for implementing analytic strategies. (Source : https://www.forbes.com/sites/louiscolumbus/2017/12/24/53-of-companies-are-adopting-big-data-analytics/#449c16a639a1)

Apache Spark Training Jobs

Sr. Big Data ETL Developer

Company Name: DoubleVerify, Inc Careers
Location: New York
Date Posted: 20th Jan, 2018
Description:

Specific Responsibilities:

  • Developing ETL processes that process billions of records a day efficiently
  • Delivering insightful data to clients, partners and internal users in various ways by implementing robust, scalable data delivery applications and APIs.
  • Collaborating and participating in project meetings.
  • Analyzing data to test the correctness and effectiveness of ETL processes.

Big Data Engineer

Company Name: Bose
Location: US, MA - Framingham
Date Posted: 19th Jan, 2018
Description:
  • Design and develop ETL pipelines connected devices, web applications, and mobile applications that support the customer experiences
  • Collaborate with front-end and mobile app development teams on user-facing features and services
  • Work with platform architects on software and system optimizations, helping to identify and remove potential performance bottlenecks
  • Focus on innovating new and better ways to create solutions that add value and amaze the end user, with a penchant for simple elegant design in every aspect from data structures to co...

Data Engineer

Company Name: Mount Sinai Health System
Location: New York
Date Posted: 16th Jan, 2018
Description:

Responsibilities