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
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
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
Project 1: Movie Recommendation
Topics: This is a project wherein you will gain hands-on experience in deploying Apache Spark for the movie recommendation. You will be introduced to the Spark Machine Learning Library, a guide to MLlib algorithms and coding which is a Machine Learning library. You will understand how to deploy collaborative filtering, clustering, regression and dimensionality reduction in MLlib. Upon the completion of the project, you will gain experience in working with streaming data, sampling, testing and statistics.
Project 2: Twitter API Integration for Tweet Analysis
Topics: With this project, you will learn to integrate Twitter API for analyzing tweets. You will write codes on the server side using any of the scripting languages, like PHP, Ruby or Python, for requesting the Twitter API and get the results in JSON format. You will then read the results and perform various operations like aggregation, filtering and parsing as per the need to come up with tweet analysis.
Project 3: Data Exploration Using Spark SQL – Wikipedia Dataset
Topics: This project lets you work with Spark SQL. You will gain experience in working with Spark SQL for combining it with ETL applications, real-time analysis of data, performing batch analysis, deploying Machine Learning, creating visualizations and processing of graphs.
Project 1: Call Log Analysis Using Trident
Topics: In this project, you will be working on call logs to decipher the data and gather valuable insights using Apache Storm Trident. You will extensively work with data about calls made from one number to another. The aim of this project is to resolve the call log issues with Trident stream processing and low latency distributed querying. You will gain hands-on experience in working with Spouts and Bolts, along with various Trident functions, filters, aggregation, joins and grouping.
Project 2: Twitter Data Analysis Using Trident
Topics: This is a project that involves working with Twitter data and processing it to extract patterns out of it. The Apache Storm Trident is the perfect framework for the real-time analysis of tweets. While working with Trident, you will be able to simplify the task of live Twitter feed analysis. In this project, you will gain real-world experience of working with Spouts, Bolts and Trident filters, joins, aggregation, functions and grouping.
Project 3: The US Presidential Election Results Analysis Using Trident DRPC Query
Topics: This is a project that lets you work on the US presidential election results and predict who is leading and trailing on a real-time basis. For this, you exclusively work with Trident distributed remote procedure call server. After the completion of the project, you will learn how to access data residing in a remote computer or network and deploy it for real-time processing, analysis and prediction.
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.
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.
A Senior Software Architect at NextGen Healthcare who has previously worked with IBM Corporation, Suresh Paritala has worked on Big Data, Data Science, Advanced Analytics, Internet of Things and Azure, along with AI domains like Machine Learning and Deep Learning. He has successfully implemented high-impact projects in major corporations around the world.
An experienced Blockchain Professional who has been bringing integrated Blockchain, particularly Hyperledger and Ethereum, and Big Data solutions to the cloud, David Callaghan has previously worked on Hadoop, AWS Cloud, Big Data and Pentaho projects that have had major impact on revenues of marquee brands around the world.
We provide 24X7 support by email for issues or doubts clearance for Self-paced training.
In online Instructor led training, trainer will be available to help you out with your queries regarding the course. If required, the support team can also provide you live support by accessing your machine remotely. This ensures that all your doubts and problems faced during labs and project work are clarified round the clock.