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Apache Spark Course

4,629 Ratings

Intellipaat’s Apache Spark Course lets you master real-time data processing using Spark Streaming, Spark SQL, RDD, machine learning libraries, etc., to clear Cloudera Spark and Hadoop Developer Certification exam. You will learn how to work on real-life projects in this Apache Spark course.

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Key Highlights

24 Hrs Instructor Led Training
22 Hrs Self-paced Videos
60 Hrs Project & Exercises
Certification
Job Assistance
Flexible Schedule
Lifetime Free Upgrade
Mentor Support
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Spark Training Overview

What will you learn in this Apache Spark Course?

  • Apache Spark and Scala programming
  • Difference between Apache Spark and Hadoop
  • Implementing Spark on a cluster
  • Writing Spark applications using Python, Java and Scala
  • RDD and its operation, along with the implementation of Spark algorithms
  • Defining and explaining Spark streaming
  • Scala classes concept and executing pattern matching
  • Scala–Java interoperability and other Scala operations
  • Working on projects using Scala to run on Spark applications
  • Software Engineers looking to upgrade Big Data skills
  • Data Engineers and ETL Developers
  • Data Scientists and Data Analytics Professionals
  • Graduates who are looking to make a career in Big Data

Spark supports two deployment modes. Both of them have been discussed below:

Deployment Mode Description
Client mode The driver program runs on the same machine where the Spark application is submitted. This mode is typically used for interactive development and debugging, as it does not require any cluster infrastructure.
Cluster mode The driver program runs on a cluster node, and the worker nodes are responsible for executing the tasks. This mode is typically used for production workloads, as it can scale to large datasets and workloads.
  • Spark is an open-source computing framework that is up to 100 times faster than MapReduce
  • Spark is an alternative form of data processing, which is unique in batch processing and streaming
  • This is a comprehensive course for advanced implementation of Scala
  • This course will help you prepare for Cloudera Hadoop Developer and Spark Professional Certification
  • This course will give professional credibility to your resume and will help you get hired faster with a high salary
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Spark and Kafka are powering today's modern data apps - Forbes
Spark can be 100x faster than Hadoop for large scale data processing - Databricks

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Skills Covered

Hadoop

Scala

Python

Java

MLlib

K-means clustering

Kafka

Flume

Hive

Spark SQL

Maven

Scala–Java

Cloudera

ZooKeeper

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Spark Course Fees

Self Paced Training

  • 22 Hrs e-learning videos
  • Flexible Schedule
  • Lifetime Free Upgrade

₹15,048

Corporate Training

  • Customized Learning
  • Enterprise Grade Learning Management System (LMS)
  • 24x7 Support
  • Enterprise Grade Reporting

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Spark Training Curriculum

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Scala Course Content

Module 01 - Introduction to Scala

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1.1 Introducing Scala
1.2 Deployment of Scala for Big Data applications and Apache Spark analytics
1.3 Scala REPL, lazy values, and control structures in Scala
1.4 Directed Acyclic Graph (DAG)
1.5 First Spark application using SBT/Eclipse
1.6 Spark Web UI
1.7 Spark in the Hadoop ecosystem.

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2.1 The importance of Scala
2.2 The concept of REPL (Read Evaluate Print Loop)
2.3 Deep dive into Scala pattern matching
2.4 Type interface, higher-order function, currying, traits, application space and Scala for data analysis

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3.1 Learning about the Scala Interpreter
3.2 Static object timer in Scala and testing string equality in Scala
3.3 Implicit classes in Scala
3.4 The concept of currying in Scala
3.5 Various classes in Scala

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4.1 Learning about the Classes concept
4.2 Understanding the constructor overloading
4.3 Various abstract classes
4.4 The hierarchy types in Scala
4.5 The concept of object equality
4.6 The val and var methods in Scala

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5.1 Understanding sealed traits, wild, constructor, tuple, variable pattern, and constant pattern

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6.1 Understanding traits in Scala
6.2 The advantages of traits
6.3 Linearization of traits
6.4 The Java equivalent
6.5 Avoiding of boilerplate code

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7.1 Implementation of traits in Scala and Java
7.2 Handling of multiple traits extending

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8.1 Introduction to Scala collections
8.2 Classification of collections
8.3 The difference between iterator and iterable in Scala
8.4 Example of list sequence in Scala

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9.1 The two types of collections in Scala
9.2 Mutable and immutable collections
9.3 Understanding lists and arrays in Scala
9.4 The list buffer and array buffer
9.6 Queue in Scala
9.7 Double-ended queue Deque, Stacks, Sets, Maps, and Tuples in Scala

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10.1 Introduction to Scala packages and imports
10.2 The selective imports
10.3 The Scala test classes
10.4 Introduction to JUnit test class
10.5 JUnit interface via JUnit 3 suite for Scala test
10.6 Packaging of Scala applications in the directory structure
10.7 Examples of Spark Split and Spark Scala

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Spark Course Content

11.1 Introduction to Spark
11.2 Spark overcomes the drawbacks of working on MapReduce
11.3 Understanding in-memory MapReduce
11.4 Interactive operations on MapReduce
11.5 Spark stack, fine vs. coarse-grained update,, Spark Hadoop YARN, HDFS Revision, and YARN Revision
11.6 The overview of Spark and how it is better than Hadoop
11.7 Deploying Spark without Hadoop
11.8 Spark history server and Cloudera distribution

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12.1 Spark installation guide
12.2 Spark configuration
12.3 Memory management
12.4 Executor memory vs. driver memory
12.5 Working with Spark Shell
12.6 The concept of resilient distributed datasets (RDD)
12.7 Learning to do functional programming in Spark
12.8 The architecture of Spark

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13.1 Spark RDD
13.2 Creating RDDs
13.3 RDD partitioning
13.4 Operations and transformation in RDD
13.5 Deep dive into Spark RDDs
13.6 The RDD general operations
13.7 Read-only partitioned collection of records
13.8 Using the concept of RDD for faster and efficient data processing
13.9 RDD action for the collect, count, collects map, save-as-text-files, and pair RDD functions

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14.1 Understanding the concept of key-value pair in RDDs
14.2 Learning how Spark makes MapReduce operations faster
14.3 Various operations of RDD
14.4 MapReduce interactive operations
14.5 Fine and coarse-grained update
14.6 Spark stack

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15.1 Comparing the Spark applications with Spark Shell
15.2 Creating a Spark application using Scala or Java
15.3 Deploying a Spark application
15.4 Scala built application
15.5 Creation of the mutable list, set and set operations, list, tuple, and concatenating list
15.6 Creating an application using SBT
15.7 Deploying an application using Maven
15.8 The web user interface of Spark application
15.9 A real-world example of Spark
15.10 Configuring of Spark

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16.1 Learning about Spark parallel processing
16.2 Deploying on a cluster
16.3 Introduction to Spark partitions
16.4 File-based partitioning of RDDs
16.5 Understanding of HDFS and data locality
16.6 Mastering the technique of parallel operations
16.7 Comparing repartition and coalesce
16.8 RDD actions

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17.1 The execution flow in Spark
17.2 Understanding the RDD persistence overview
17.3 Spark execution flow, and Spark terminology
17.4 Distribution shared memory vs. RDD
17.5 RDD limitations
17.6 Spark shell arguments
17.7 Distributed persistence
17.8 RDD lineage
17.9 Key-value pair for sorting implicit conversions like CountByKey, ReduceByKey, SortByKey, and AggregateByKey

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18.1 Introduction to Machine Learning
18.2 Types of Machine Learning
18.3 Introduction to MLlib
18.4 Various ML algorithms supported by MLlib
18.5 Linear regression, logistic regression, decision tree, random forest, and K-means clustering techniques

Hands-on Exercise: 
1. Building a Recommendation Engine

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19.1 Why Kafka and what is Kafka?
19.2 Kafka architecture
19.3 Kafka workflow
19.4 Configuring Kafka cluster
19.5 Operations
19.6 Kafka monitoring tools
19.7 Integrating Apache Flume and Apache Kafka

Hands-on Exercise: 
1. Configuring Single Node Single Broker Cluster
2. Configuring Single Node Multi Broker Cluster
3. Producing and consuming messages
4. Integrating Apache Flume and Apache Kafka

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20.1 Introduction to Spark Streaming
20.2 Features of Spark Streaming
20.3 Spark Streaming workflow
20.4 Initializing StreamingContext, discretized Streams (DStreams), input DStreams and Receivers
20.5 Transformations on DStreams, output operations on DStreams, windowed operators and why it is useful
20.6 Important windowed operators and stateful operators

Hands-on Exercise: 
1. Twitter Sentiment analysis
2. Streaming using Netcat server
3. Kafka–Spark streaming
4. Spark–Flume streaming

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21.1 Introduction to various variables in Spark like shared variables and broadcast variables
21.2 Learning about accumulators
21.3 The common performance issues
21.4 Troubleshooting the performance problems

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22.1 Learning about Spark SQL
22.2 The context of SQL in Spark for providing structured data processing
22.3 JSON support in Spark SQL
22.4 Working with XML data
22.5 Parquet files
22.6 Creating Hive context
22.7 Writing data frame to Hive
22.8 Reading JDBC files
22.9 Understanding the data frames in Spark
22.10 Creating Data Frames
22.11 Manual inferring of schema
22.12 Working with CSV files
22.13 Reading JDBC tables
22.14 Data frame to JDBC
22.15 User-defined functions in Spark SQL
22.16 Shared variables and accumulators
22.17 Learning to query and transform data in data frames
22.18 Data frame provides the benefit of both Spark RDD and Spark SQL
22.19 Deploying Hive on Spark as the execution engine

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23.1 Learning about the scheduling and partitioning in Spark
23.2 Hash partition
23.3 Range partition
23.4 Scheduling within and around applications
23.5 Static partitioning, dynamic sharing, and fair scheduling
23.6 Map partition with index, the Zip, and GroupByKey
23.7 Spark master high availability, standby masters with ZooKeeper, single-node recovery with the local file system and high order functions

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Apache Spark Certification

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This course is designed for clearing the Cloudera Spark and Hadoop Developer Certification (CCA175) exam. Check our Hadoop Training Course for gaining proficiency in the Hadoop component of the CCA175 exam. The
complete course is created by industry experts for professionals to get top jobs in the best organizations. The training includes real-world projects and case studies that are highly valuable.

On the completion of the training course, you will have quizzes that will help you prepare for the CCA175 certification exam and score top marks.

The Intellipaat certification is awarded on the successful completion of the project work and after its review by experts. The Intellipaat certification is recognized in some of the biggest companies such as Cisco, Cognizant, Mu Sigma, TCS, Genpact, Hexaware, Sony, Ericsson, etc.

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FAQs

Why should I learn Spark from Intellipaat?

Intellipaat is a pioneer in Hadoop training in India. It pays to be with a market leader such as Intellipaat to learn Spark and get the best jobs in leading MNCs with competitive salaries. Intellipaat provides the most comprehensive training course that includes real-time projects and assignments, designed by industry experts. The entire course content is fully aligned toward clearing the exam for the Cloudera Spark and Hadoop Developer Certification (CCA175) exam.

Intellipaat offers lifetime access to videos, course material, 24/7 support, and course material upgrades to the latest version at no extra fee. For Hadoop and Spark training, you get the Intellipaat Proprietary Virtual Machine for lifetime and free cloud access for six months for performing training exercises. Hence, it is clearly a one-time investment.

If you are looking for free resources on Spark then read our blogs on Spark tutorial, and Spark Interview Questions.

3 technical 1:1 sessions per month will be allowed.

Intellipaat offers query resolution, and you can raise a ticket with the dedicated support team at any time. You can avail yourself of email support for all your queries. We can also arrange one-on-one sessions with our support team If your query does not get resolved through email. However, 1:1 session support is given for 6 months from the start date of your course.

Intellipaat provides placement assistance to all learners who have completed the training and moved to the placement pool after clearing the PRT (Placement Readiness Test). More than 500+ top MNCs and startups hire Intellipaat learners. Our alumni work with Google, Microsoft, Amazon, Sony, Ericsson, TCS, Mu Sigma, etc.

No, our job assistance is aimed at helping you land your dream job. It offers a potential opportunity for you to explore various competitive openings in the corporate world and find a well-paid job, matching your profile. The final hiring decision will always be based on your performance in the interview and the requirements of the recruiter.

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