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Apache Spark, Scala and Storm Training

4,598 Ratings

Intellipaat Apache Storm, Spark and Scala certification master's program lets you gain proficiency in real-time data analytics and high-speed processing. You will work on real-world projects in Spark RDD, Scala programming, Storm topology, logic dynamics, Trident filters and spouts.

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40 Hrs Self-paced Videos
80 Hrs Project & Exercises
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Flexible Schedule
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Apache Storm, Spark and Scala Certification Overview

What will you learn in this training course?

  1. Spark and programming in Scala
  2. Comparison between Spark and Hadoop
  3. Deploying high-speed processing on Big Data
  4. Cluster deployment of Apache Spark
  5. Deploying Python, Java and Scala applications in Apache Spark
  6. Concepts of distributed processing and Storm architecture
  7. Storm topology, logic dynamics and components
  8. Trident filter, spouts and functions
  9. Using Storm for real-time analytics
  10. Types of analyses including batch analysis
  • Big Data Professionals, Data Scientists and Software Engineers
  • ETL Developers, Data Analysts and Project Managers
  • Those looking for a Big Data career

Anybody can take up this training course regardless of their skills. A basic knowledge of Java can help, though.

The amount of Big Data that is processed today points to the fact that there is an urgent need for faster and more efficient way of processing data. Learning Spark and Storm puts you at an advantage, since there is a huge demand for professionals in this domain. Learning Scala which is the language of choice for writing Spark applications is also hugely beneficial. Above all, this combo course can help you grab some of the best jobs in the industry.

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Course Fees

Self Paced Training

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

₹13,110

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  • Enterprise Grade Learning Management System (LMS)
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Apache Spark and Storm Course Curriculum

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Scala Course Content

Introduction to Scala

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Introduction and deployment of Scala for Big Data applications and Apache Spark analytics, Scala REPL, Lazy Values, Control Structures in Scala, Directed Acyclic Graph (DAG), first Spark application using SBT/Eclipse, Spark Web UI and Spark in Hadoop Ecosystem.

The importance of Scala, the concept of REPL (Read Evaluate Print Loop), deep dive into Scala pattern matching, type interface, higher-order function, currying, traits, application space and Scala for data analytics

Learning about the Scala Interpreter, static object timer in Scala and testing string equality in Scala, implicit classes in Scala, the concept of currying in Scala and various classes in Scala

Learning about the Classes concept, understanding the constructor overloading, various abstract classes, the hierarchy types in Scala, the concept of object equality and the val and var methods in Scala

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

Understanding traits in Scala, the advantages of traits, linearization of traits, the Java equivalent and avoiding of boilerplate code

Implementation of traits in Scala and Java and handling of multiple traits extending

Introduction to Scala collections, classification of collections, the difference between Iterator and Iterable in Scala and an example of list sequence in Scala

Two types of collections in Scala, Mutable and Immutable collections, understanding lists and arrays in Scala, the list buffer and array buffer, queue in Scala and double-ended queue Deque, Stacks, Sets, Maps and Tuples in Scala

Introduction to Scala packages and imports, the selective imports, the Scala test classes, introduction to JUnit test class, JUnit interface via JUnit 3 suite for Scala test, packaging of Scala applications in Directory Structure and examples of Spark Split and Spark Scala

Spark Course Content

Introduction to Spark, how Spark overcomes the drawbacks of working on MapReduce, understanding in-memory MapReduce, interactive operations on MapReduce, Spark stack, fine vs. coarse-grained update, Spark stack, Spark Hadoop YARN, HDFS Revision, YARN Revision, the overview of Spark and how it is better than Hadoop, deploying Spark without Hadoop, Spark history server and Cloudera distribution

Spark installation guide, Spark configuration, memory management, executor memory vs. driver memory, working with Spark Shell, the concept of resilient distributed datasets (RDD), learning to do functional programming in Spark and the architecture of Spark

Spark RDD, creating RDDs, RDD partitioning, operations, and transformation in RDD, deep dive into Spark RDDs, the RDD general operations, a read-only partitioned collection of records, using the concept of RDD for faster and efficient data processing, RDD action for collect, count, collects map, save-as-text-files and pair RDD functions

Understanding the concept of Key-Value pair in RDDs, learning how Spark makes MapReduce operations faster, various operations of RDD, MapReduce interactive operations, fine and coarse-grained update and Spark stack

Comparing the Spark applications with Spark Shell, creating a Spark application using Scala or Java, deploying a Spark application, Scala built application, creation of mutable list, set and set operations, list, tuple, concatenating list, creating application using SBT, deploying application using Maven, the web user interface of Spark application, a real-world example of Spark and configuring of Spark

Learning about Spark parallel processing, deploying on a cluster, introduction to Spark partitions, file-based partitioning of RDDs, understanding of HDFS and data locality, mastering the technique of parallel operations, comparing repartition and coalesce and RDD actions

The execution flow in Spark, understanding the RDD persistence overview, Spark execution flow and Spark terminology, distribution shared memory vs. RDD, RDD limitations, Spark shell arguments, distributed persistence, RDD lineage, Key-Value pair for sorting implicit conversions like CountByKey, ReduceByKey, SortByKey and AggregateByKey

Introduction to Machine Learning, types of Machine Learning, introduction to MLlib, various ML algorithms supported by MLlib, Linear Regression, Logistic Regression, Decision Tree, Random Forest, K-means clustering techniques and building a Recommendation Engine

Hands-on Exercise:  Building a Recommendation Engine

Why Kafka, what is Kafka, Kafka architecture, Kafka workflow, configuring Kafka cluster, basic operations, Kafka monitoring tools and integrating Apache Flume and Apache Kafka

Hands-on Exercise: Configuring Single Node Single Broker Cluster, Configuring Single Node Multi Broker Cluster, Producing and consuming messages and integrating Apache Flume and Apache Kafka

Introduction to Spark Streaming, features of Spark Streaming, Spark Streaming workflow, initializing StreamingContext, Discretized Streams (DStreams), Input DStreams and Receivers, transformations on DStreams, Output Operations on DStreams, Windowed Operators and why it is useful, important Windowed Operators and Stateful Operators

Hands-on Exercise:  Twitter Sentiment Analysis, streaming using netcat server, Kafka–Spark Streaming and Spark–Flume Streaming

Introduction to various variables in Spark like shared variables and broadcast variables, learning about accumulators, the common performance issues and troubleshooting the performance problems

Learning about Spark SQL, the context of SQL in Spark for providing structured data processing, JSON support in Spark SQL, working with XML data, parquet files, creating Hive context, writing Data Frame to Hive, reading JDBC files, understanding the Data Frames in Spark, creating Data Frames, manual inferring of schema, working with CSV files, reading JDBC tables, Data Frame to JDBC, user-defined functions in Spark SQL, shared variables and accumulators, learning to query and transform data in Data Frames, how Data Frame provides the benefit of both Spark RDD and Spark SQL and deploying Hive on Spark as the execution engine

Learning about the scheduling and partitioning in Spark, hash partition, range partition, scheduling within and around applications, static partitioning, dynamic sharing, fair scheduling, Map partition with index, the Zip, GroupByKey, Spark master high availability, standby masters with ZooKeeper, Single-node Recovery with Local File System and High Order Functions

Apache Storm Course Content

Big Data characteristics, understanding Hadoop distributed computing, the Bayesian Law, deploying Storm for real-time analytics, Apache Storm features, comparing Storm with Hadoop, Storm execution and learning about Tuple, Spout and Bolt

Installing Apache Storm and various types of run modes of Storm

Understanding Apache Storm and the data model

Installation of Apache Kafka and its configuration

Understanding advanced Storm topics like Spouts, Bolts, Stream Groupings and Topology and its life cycle and learning about guaranteed message processing

Various grouping types in Storm, reliable and unreliable messages, Bolt structure and life cycle, understanding Trident topology for failure handling, process and call log analysis topology for analyzing call logs for calls made from one number to another

Understanding of Trident spouts and its different types, various Trident spout interface and components, familiarizing with Trident filter, aggregator and functions and a practical and hands-on use case on solving call log problem using Storm Trident

Various components, classes and interfaces in Storm like, Base Rich Bolt Class, i RichBolt Interface, i RichSpout Interface and Base Rich Spout class and the various methodologies of working with them

Understanding Cassandra, its core concepts and its strengths and deployment

Twitter Boot Stripping, detailed understanding of Boot Stripping, concepts of Storm and Storm development environment

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Apache Storm, Spark and Scala Projects

Apache Storm, Spark and Scala Certification

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This training course is designed to help you clear the Apache Spark component of 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 entire training course content is in line with the certification program and helps you clear the certification exam with ease and get best jobs in the top MNCs.

As part of this training, you will be working on real-time projects and assignments that have immense implications in the real-world industry scenarios, thus helping you fast-track your career effortlessly.

At the end of this training program, there will be a quiz that perfectly reflects the type of questions asked in the certification exam and helps you score better marks.

Intellipaat Storm Certification and Intellipaat Course Completion Certificate will be awarded upon the completion of the project work (after expert review) and upon scoring at least 60% marks in the quiz. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.

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FAQs on Apache Storm, Spark and Scala

Why should I learn Apache Spark, Storm and Scala from Intellipaat?

This Intellipaat all-in-one training course lets you master various computational tools to work on Big Data like Apache Spark and Storm, along with Scala programming. You will gain full proficiency in processing Big Data, work on real-time analytics, perform batch processing and increase the performance of the Hadoop framework.

The course content is fully in line with clearing the Spark component of the Cloudera Spark and Hadoop Developer Certification (CCA175).   

This is a completely career-oriented course designed by industry experts. Your training program includes real-time projects and step-by-step assignments to evaluate your progress and specially designed quizzes for clearing the requisite certification exams.

Intellipaat also offers lifetime access to videos, course materials, 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 6 months for performing training exercises. All-in-one, it is a one-time investment to become a successful Data Scientist and grab the best jobs at the best salaries in top MNCs around the world.

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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