Learn to play with data via various platforms and techniques. Be the expert in Hadoop Architecture, Apache Storm and Apache Spark using Scala Programming
Key Features
This is a combo course including:
1. Hadoop Developer Training
2. Hadoop Analyst Training
3. Hadoop Administration Training
4. Hadoop Testing Training
5. Apache Storm
6. Apache Spark
7. Scala programming
- 102 hours of High-Quality in-depth Video E-Learning Sessions
- 154 hours of Lab Exercises
- Intellipaat Proprietary VM and free cloud instance for 6 months for performing exercises
- 70% of extensive learning through Hands-on exercises, Project Work, Assignments and Quizzes
- The training will prepare you for Cloudera Certified Developer for Apache Hadoop (CCDH), Cloudera Certified Administrator for Apache Hadoop (CCAH) , Apache Storm and Apache Spark Certification
- You will also learn how to work with Hortonworks and MapR Distributions
- 24X7 Lifetime Support with Rapid Problem Resolution Guaranteed
- Lifetime Access to Videos, Tutorials and Course Material
- Guidance to Resume Preparation and Job Assistance
- Step -by- step Installation of Software
- Course Completion Certificate from Intellipaat
About Hadoop all in 1, Spark, Storm, Scala Training Course
It is an all-in-one course designed to give a 360 degree overview of Hadoop Architecture using real-time projects along with real-time processing of unbound data streams using Apache Storm and creating applications in Spark with Scala programming. The major topics include Hadoop and its Ecosystem, core concepts of MapReduce and HDFS, Introduction to HBase Architecture, Hadoop Cluster Setup, Hadoop Administration and Maintenance. The course further trains you on the concepts of Big Data world, Batch Analysis, Types of Analytics and usage of Apache Storm for real-time Big Data Analytics, Comparison between Spark and Hadoop and Techniques to increase your application performance, enabling high-speed processing.
Learning Objectives:
After completion of this Hadoop all-in-one course, you will be able to:
- Excel in the concepts of Hadoop Distributed File System (HDFS)
- Implement HBase and MapReduce Integration
- Understand Data science Project Life Cycle, Data Acquisition and Data Collection
- Execute various Machine Learning Algorithms
- Understand Apache Hadoop2.0 Framework and Architecture
- Learn to write complex MapReduce programs in both MRv1 and Mrv2
- Design and develop applications involving large data using Hadoop Ecosystem
- Understand Prediction and Analysis Segmentation through Clustering
- Learn the basics of Big Data and ways to integrate R with Hadoop
- Learn various advanced modules like advanced modules like Yarn, Flume, Hive, Oozie, Impala, Zookeeper and Hue.
- Set up Hadoop infrastructure with single and multi-node clusters using Amazon ec2 (CDH4)
- Monitor a Hadoop cluster and execute routine administration procedures
- Understand basic distributed concepts and Storm Architecture
- Learn Hadoop Distributed Computing, Big Data features, Legacy architecture of Real-time System
- Know the Logic Dynamics, Components and Topology in Storm
- Understand the difference between Apache Spark and Hadoop
- Learn Scala and its programming implementation
- Implement Spark on a cluster
- Write Spark Applications using Python, Java and Scala
- Get deep insights into the functioning of Scala
- Implement Trident Spouts and understand Trident Filter, Function and Aggregator
- Learn Twitter Boot Stripping
- Work on Real-life Storm Project
- Work on Minor and Major Projects applying programming techniques of Scala to run on Spark applications
Recommended Audience
- Programming Developers , System Administrators and ETL Developers
- Project Managers eager to learn new techniques of maintaining large data
- Experienced working professionals aiming to become Big Data Analysts
- Professionals aiming to build career in real-time Data Analytics with Apache Storm techniques and Hadoop Computing
- Professionals aspiring to be a ‘Data Scientist’
- Information Architects to gain expertise in Predictive Analytics domain
- Mainframe Professionals, Architects & Testing Professionals
Pre-Requisites:
- Some prior experience in Core Java and good analytical skills
- Basic knowledge of Unix , sql scripting
- Prior knowledge of Apache Hadoop is not required.
Why Take Hadoop all in 1, Spark, Storm, Scala Training Course?
- This course provides an exploratory data analysis approach using concepts of R Programming and Hadoop.
- This course offers a reliable and faster means of real-time data processing for huge data streams.
- This course gives the complete study of effective data handling, amazing graphical facilities for data analytics and user-friendly ways to create top-notch graphics
- Big, multinational companies like Google, Yahoo, Apple, eBay, Facebook and many others are hiring skilled professionals capable of handling Big Data using Hadoop and Data Science techniques.
- The training certifies you for biggest, top-paid job opportunities in top MNCs working on Big Data, Hadoop, Apache Spark and Storm.
Module 1 – Introduction to Hadoop and its Ecosystem, Map Reduce and HDFS
- Big Data, Factors constituting Big Data
- Hadoop and Hadoop Ecosystem
- Map Reduce -Concepts of Map, Reduce, Ordering, Concurrency, Shuffle, Reducing, Concurrency
- Hadoop Distributed File System (HDFS) Concepts and its Importance
- Deep Dive in Map Reduce – Execution Framework, Partitioner, Combiner, Data Types, Key pairs
- HDFS Deep Dive – Architecture, Data Replication, Name Node, Data Node, Data Flow
- Parallel Copying with DISTCP, Hadoop Archives
Assignment – 1
Module 2 – Hands on Exercises
- Installing Hadoop in Pseudo Distributed Mode, Understanding Important configuration files, their Properties and Demon Threads
- Accessing HDFS from Command Line
- Map Reduce – Basic Exercises
- Understanding Hadoop Eco-system
- Introduction to Sqoop, use cases and Installation
- Introduction to Hive, use cases and Installation
- Introduction to Pig, use cases and Installation
- Introduction to Oozie, use cases and Installation
- Introduction to Flume, use cases and Installation
- Introduction to Yarn
Assignment -2 and 3
Mini Project – Importing Mysql Data using Sqoop and Querying it using Hive
Module 3 – Deep Dive in Map Reduce and Yarn
- How to develop Map Reduce Application, writing unit test
- Best Practices for developing and writing, Debugging Map Reduce applications
- Joining Data sets in Map Reduce
- Hadoop API’s
- Introduction to Hadoop Yarn
- Difference between Hadoop 1.0 and 2.0
Module 3.1
- Project 1- Hands on exercise – end to end PoC using Yarn or Hadoop 2.
- Real World Transactions handling of Bank
- Moving data using Sqoop to HDFS
- Incremental update of data to HDFS
- Running Map Reduce Program
- Running Hive queries for data analytics
- Project 2- Hands on exercise – end to end PoC using Yarn or Hadoop 2.0
Running Map Reduce Code for Movie Rating and finding their fans and average rating
Assignment -4 and 5
Module 4 – Deep Dive in Pig
1. Introduction to Pig
- What Is Pig?
- Pig’s Features
- Pig Use Cases
- Interacting with Pig
2. Basic Data Analysis with Pig
- Pig Latin Syntax
- Loading Data
- Simple Data Types
- Field Definitions
- Data Output
- Viewing the Schema
- Filtering and Sorting Data
- Commonly-Used Functions
- Hands-On Exercise: Using Pig for ETL Processing
3. Processing Complex Data with Pig
- Complex/Nested Data Types
- Grouping
- Iterating Grouped Data
- Hands-On Exercise: Analyzing Data with Pig
4. Multi-Dataset Operations with Pig
- Techniques for Combining Data Sets
- Joining Data Sets in Pig
- Set Operations
- Splitting Data Sets
- Hands-On Exercise
5. Extending Pig
- Macros and Imports
- UDFs
- Using Other Languages to Process Data with Pig
- Hands-On Exercise: Extending Pig with Streaming and UDFs
6. Pig Jobs
Case studies of Fortune 500 companies which are Electronic Arts and Walmart with real data sets.
Assignment – 6
Module 5 – Deep Dive in Hive
1. Introduction to Hive
- What Is Hive?
- Hive Schema and Data Storage
- Comparing Hive to Traditional Databases
- Hive vs. Pig
- Hive Use Cases
- Interacting with Hive
2. Relational Data Analysis with Hive
- Hive Databases and Tables
- Basic HiveQL Syntax
- Data Types
- Joining Data Sets
- Common Built-in Functions
- Hands-On Exercise: Running Hive Queries on the Shell, Scripts, and Hue
3. Hive Data Management
- Hive Data Formats
- Creating Databases and Hive-Managed Tables
- Loading Data into Hive
- Altering Databases and Tables
- Self-Managed Tables
- Simplifying Queries with Views
- Storing Query Results
- Controlling Access to Data
- Hands-On Exercise: Data Management with Hive
4. Hive Optimization
- Understanding Query Performance
- Partitioning
- Bucketing
- Indexing Data
5. Extending Hive
- User-Defined Functions
6. Hands on Exercises – Playing with huge data and Querying extensively.
7. User defined Functions, Optimizing Queries, Tips and Tricks for performance tuning
Assignment – 7
Module 6 – Introduction to Hbase architecture
- What is Hbase
- Where does it fits
- What is NOSQL
Assignment -8
Module 7 – Hadoop Cluster Setup and Running Map Reduce Jobs
- Hadoop Multi Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup
- Running Map Reduce Jobs on Cluster
Module 8 – Major Project – Putting it all together and Connecting Dots
- Putting it all together and Connecting Dots
- Working with Large data sets, Steps involved in analyzing large data
Assignment – 9, 10
Module 9 – Advance Mapreduce
- Delving Deeper Into The Hadoop API
- More Advanced Map Reduce Programming, Joining Data Sets in Map Reduce
- Graph Manipulation in Hadoop
Assignment – 11, 12
Module 10 – Impala
1. Introduction to Impala
- What is Impala?
- How Impala Differs from Hive and Pig
- How Impala Differs from Relational Databases
- Limitations and Future Directions
- Using the Impala Shell
2. Choosing the Best (Hive, Pig, Impala)
Module 11 – ETL Connectivity with Hadoop Ecosystem
- How ETL tools work in Big data Industry
- Connecting to HDFS from ETL tool and moving data from Local system to HDFS
- Moving Data from DBMS to HDFS
- Working with Hive with ETL Tool
- Creating Map Reduce job in ETL tool
- End to End ETL PoC showing Hadoop integration with ETL tool.
Module 12 – Hadoop Cluster Configuration
- Hadoop configuration overview and important configuration file
- Configuration parameters and values
- HDFS parameters MapReduce parameters
- Hadoop environment setup
- ‘Include’ and ‘Exclude’ configuration files
Lab: MapReduce Performance Tuning
Module 13 – Hadoop Administration and Maintenance
- Namenode/Datanode directory structures and files
- File system image and Edit log
- The Checkpoint Procedure
- Namenode failure and recovery procedure
- Safe Mode
- Metadata and Data backup
- Potential problems and solutions / what to look for
- Adding and removing nodes
Lab: MapReduce File system Recovery
Module 14 – Hadoop Monitoring and Troubleshooting
- Best practices of monitoring a Hadoop cluster
- Using logs and stack traces for monitoring and troubleshooting
- Using open-source tools to monitor Hadoop cluster
Module 15 – Job Scheduling
- How to schedule Hadoop Jobs on the same cluster
- Default Hadoop FIFO Schedule
- Fair Scheduler and its configuration
Module 16 – Hadoop Multi Node Cluster Setup and Running Map Reduce Jobs on Amazon Ec2
- Hadoop Multi Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup
- Running Map Reduce Jobs on Cluster
Module 17 – ZOOKEEPER
- ZOOKEEPER Introduction
- ZOOKEEPER use cases
- ZOOKEEPER Services
- ZOOKEEPER data Model
- Znodes and its types
- Znodes operations
- Znodes watches
- Znodes reads and writes
- Consistency Guarantees
- Cluster management
- Leader Election
- Distributed Exclusive Lock
- Important points
Module 18 – Advance Oozie
- Why Oozie?
- Installing Oozie
- Running an example
- Oozie- workflow engine
- Example M/R action
- Word count example
- Workflow application
- Workflow submission
- Workflow state transitions
- Oozie job processing
- Oozie- HADOOP security
- Why Oozie security?
- Job submission to hadoop
- Multi tenancy and scalability
- Time line of Oozie job
- Coordinator
- Bundle
- Layers of abstraction
- Architecture
- Use Case 1: time triggers
- Use Case 2: data and time triggers
- Use Case 3: rolling window
Module 19 – Advance Flume
- Apache Flume
- Big data ecosystem
- Physically distributed Data sources
- Changing structure of Data
- Closer look
- Anatomy of Flume
- Core concepts
- Event
- Clients
- Agents
- Source
- Channels
- Sinks
- Interceptors
- Channel selector
- Sink processor
- Data ingest
- Agent pipeline
- Transactional data exchange
- Routing and replicating
- Why channels?
- Use case- Log aggregation
- Adding flume agent
- Handling a server farm
- Data volume per agent
- Example describing a single node flume deployment
Module 20 – Advance HUE
- HUE introduction
- HUE ecosystem
- What is HUE?
- HUE real world view
- Advantages of HUE
- How to upload data in File Browser?
- View the content
- Integrating users
- Integrating HDFS
- Fundamentals of HUE FRONTEND
Module 21 – Advance Impala
- IMPALA Overview: Goals
- User view of Impala: Overview
- User view of Impala: SQL
- User view of Impala: Apache HBase
- Impala architecture
- Impala state store
- Impala catalogue service
- Query execution phases
- Comparing Impala to Hive
Testing
Module 22 – Hadoop Stack Integration Testing
- Why Hadoop testing is important
- Unit testing
- Integration testing
- Performance testing
- Diagnostics
- Nightly QA test
- Benchmark and end to end tests
- Functional testing
- Release certification testing
- Security testing
- Scalability Testing
- Commissioning and Decommissioning of Data Nodes Testing
- Reliability testing
- Release testing
Module 23 – Roles and Responsibilities of Hadoop Testing
- Understanding the Requirement, preparation of the Testing Estimation, Test Cases, Test Data, Test bed creation, Test Execution, Defect Reporting, Defect Retest, Daily Status report delivery, Test completion.
- ETL testing at every stage (HDFS, HIVE, HBASE) while loading the input (logs/files/records etc) using sqoop/flume which includes but not limited to data verification, Reconciliation.
- User Authorization and Authentication testing (Groups, Users, Privileges etc)
- Report defects to the development team or manager and driving them to closure.
- Consolidate all the defects and create defect reports.
- Validating new feature and issues in Core Hadoop.
Module 24 – Framework called MR Unit for Testing of Map-Reduce Programs
- Report defects to the development team or manager and driving them to closure.
- Consolidate all the defects and create defect reports.
- Validating new feature and issues in Core Hadoop
- Responsible for creating a testing Framework called MR Unit for testing of Map-Reduce programs.
Module 25 – Unit Testing
- Automation testing using the OOZIE.
- Data validation using the query surge tool.
Module 26 – Test Execution of Hadoop _customized
- Test plan for HDFS upgrade
- Test automation and result
Module 27 – Test Plan Strategy Test Cases of Hadoop Testing
- How to test install and configure
Module 28 – High Availability Federation, Yarn and Security
Module 29 – Job and Certification Support
- Major Project, Hadoop Development, cloudera Certification Tips and Guidance and Mock Interview Preparation, Practical Development Tips and Techniques, certification preparation
Apache Spark
Module 1-Why Spark? Explain Spark and Hadoop Distributed File System
- What is Spark
- Comparison with Hadoop
- Components of Spark
Module 2-Spark Components, Common Spark Algorithms-Iterative Algorithms, Graph Analysis, Machine Learning
- Apache Spark- Introduction, Consistency, Availability, Partition
- Unified Stack Spark
- Spark Components
- Comparison with Hadoop – Scalding example, mahout, storm, graph
Module 3-Running Spark on a Cluster, Writing Spark Applications using Python, Java, Scala
- Explain python example
- Show installing a spark
- Explain driver program
- Explaining spark context with example
- Define weakly typed variable
- Combine scala and java seamlessly.
- Explain concurrency and distribution.
- Explain what is trait.
- Explain higher order function with example.
- Define OFI scheduler.
- Advantages of Spark
- Example of Lamda using spark
- Explain Mapreduce with example
Module 4-RDD and its operation
- Difference between RISC and CISC
- Define Apache Mesos
- Cartesian product between two RDD
- Define count
- Define Filter
- Define Fold
- Define API Operations
- Define Factors
Module 5-Spark, Hadoop, and the Enterprise Data Centre, Common Spark Algorithms
- How hadoop cluster is different from spark
- Define writing data
- Explain sequence file and its usefulness
- Define protocol buffers
- Define text file, CSV, Object Files and File System
- Define sparse metrics
- Explain RDD and Compression
- Explain data stores and its usefulness
Module 6-Spark Streaming
- Define Elastic Search
- Explain Streaming and its usefulness
- Apache bookeeper
- Define Dstream
- Define mapreduce word count
- Explain Paraquet
- Scala ORM
- Define Mlib
- Explain multi graphix and its usefulness
- Define property graph
Module 7-Spark Persistence in Spark
- Persistence
- Motivation
- Example
- Transformation
- Scala and Python
- Examples – K-means
- Latent Dirichlet Allocation (LDA)
Module 8-Broadcast and accumulator
- Motivation
- Broadcast Variables
- Example: Join
- Alternative if one table is small
- Better version with broadcast
- How to create a Broadcast
- Accumulators motivation
- Example: Join
- Accumulator Rules
- Custom accumulators
- Another common use
- Creating an accumulator using spark context object
Module 9-Spark SQL and RDD
- Introduction
- Spark SQL main capabilities
- Spark SQL usage diagram
- Spark SQL
- Important topics in Spark SQL- Data frames
- Twitter language analysis
Mini Projects
Project 1. List the items
Project 2. Sorting of Records
Project 3. Show a histogram of date vs users created. Optionally, use a rich visualization like
Project 4. Prepare a map of tags vs # of questions in each tag and display it.
Major Projects
Project 1 Movie Recommendation
Project 2 Twitter API Integration for tweet Analysis
Project 3 Data Exploration Using Spark SQL – Wikipedia dataset
Storm
Module 1 – Understanding Architecture of Storm
- Bayesian Law
- Hadoop Distributed Computing
- Big Data features
- Legacy Architecture of Real Time System
- Storm vs. Hadoop
- Logical Dynamic and Components in Storm
- Storm Topology
- Execution Components in Storm
- Stream Grouping
- Tuple
- Spout
- Bolt-normalization bolt
Module 2 – Installation of Apache storm
- Installing Apache Storm
Module 3 – Grouping
- Different types of Grouping
- Reliable and unreliable messaging
- Fetching data – Direct connection and En-queued message
- Bolt Lifecycle
Module 4 – Overview of Trident
- Trident Spouts and its types
- Components and Interface of Trident spout
- Trident Function, Filter & Aggregator
Module 5 – Boot Stripping
- Twitter Boot Stripping
- Detailed learning on Boot Stripping
- Concepts of Storm
- Storm Development Environment
Projects
Real-time Project on Storm
The Project Bolt Blue Print
Scala
Module 1-Introduction of Scala
- Scala Overview
Module 2-Pattern Matching
- Advantages of Scala
- REPL (Read Evaluate print loop)
- Language Features
- Type Interface
- Higher order function
- Option
- Pattern Matching
- Collection
- Currying
- Traits
- Application Space
Module 3-Executing the Scala code
- Uses of scala interpreter
- Example of static object timer in scala
- Testing of String equality in scala
- Implicit classes in scala with examples.
- Recursion in scala
- Currying in scala with examples.
- Classes in scala
Module 4-Classes concept in Scala
- Constructor
- Constructor overloading
- Properties
- Abstract classes
- Type hierarchy in Scala
- Object equality
- Val and var methods
Module 5-Case classes and pattern matching
- Sealed traits
- Case classes
- Constant pattern in case classes
- Wild card pattern
- Variable pattern
- Constructor pattern
- Tuple pattern
Module 6-Concepts of traits with example
- Java equivalents
- Advantages of traits
- Avoiding boilerplate code
- Linearization of traits
- Modelling a real world example
Module 7-Scala java Interoperability
- How traits are implemented in scala and java
- How extending multiple traits is handled
Module 8-Scala collections
- Classification of scala collections
- Iterable
- Iterator and iterable
- List sequence example in scala
Module 9-Mutable collections vs. Immutable collections
- Array in scala
- List in scala
- Difference between list and list buffer
- Array buffer
- Queue in scala
- Dequeue in scala
- Mutable queue in scala
- Stacks in scala
- Sets and maps in scala
- Tuples
Module 10-Use Case bobsrockets package
- Different import types
- Selective imports
- Testing-Assertions
- Scala test case- scala test fun. Suite
- Junit test in scala
- Interface for Junit via Junit 3 suite in scala test
- SBT
- Directory structure for packaging scala application
WHAT ARE VARIOUS BIG DATA HADOOP PROFESSIONAL TITLES?
Hadoop Architect: Hadoop Architect is a professional who organizes, manages and governs Hadoop on a very large cluster. The most important thing Hadoop Architect must have is rich experience in Hive, HBase, MapReduce, PIG and so on. Hadoop Developers: Hadoop Developer is a person who just loves programming and he must have knowledge about Core, Java, SQL and other languages along with remarkable skills. Hadoop QA Professional : Hadoop QA professional is a person who tests and rectify glitches in Hadoop Hadoop Administrator: Hadoop Administrator is a person who admins Hadoop and its Data base system. He has a well and good understanding of Hadoop principles and its hardware systems. Others: There can be some other jobs which could be assigned to some other professional as well. For example there can be a Hadoop trainer, Hadoop consultant, Hadoop engineers & also senior Hadoop engineers, big data Engineers, Hadoop developers and also Java Engineers (DSE Team).
Java 1.6.x or higher, preferably from Sun -see HadoopJavaVersions Linux and Windows are the supported operating systems, but BSD, Mac OS/X, and Open Solaris are known to work.
Now with Storm and MapReduce running together in Hadoop on YARN, a Hadoop cluster can efficiently process a full range of workloads from real-time to interactive to batch. Storm is simple and developers can write Storm topologies using any programming language. Five characteristics make Storm ideal for real-time data processing workloads. Storm is: Fast – benchmarked as processing one million 100 byte messages per second per node Scalable – with parallel calculations that run across a cluster of machines Fault-tolerant – when workers die, Storm will automatically restart them. If a node dies, the worker will be restarted on another node. Reliable – Storm guarantees that each unit of data (tuple) will be processed at least once or exactly once. Messages are only replayed when there are failures. Easy to operate – standard configurations are suitable for production on day one. Once deployed, Storm is easy to operate.
Telecommunication – Silent Roamers Detection Banking – Fraud Transaction Detection Retail – Popular Retailers like Sears and Walmart Social Networking –Twitter
Speed is crucial for maintaining a competitive edge. Cassandra, with its in-memory database option, can handle very high volumes of data at high velocity. This fits perfectly with Spark’s in-memory data analysis to provide the combination necessary to translate data into information in real time.
The Spark supported open source integration provides developers an easy path for their applications to run on top of. Also, both Spark and Cassandra allow developers to quickly write applications in languages such as Python, Java and others with updated drivers and support.
Cluster managers Hardware & configuration Linking with Spark Monitoring and measuring
In Intellipaat self-paced training program you will receive recorded sessions, course material, Quiz, related software’s and assignments.The courses are designed such that you will get real world exposure and focused on clearing relevant certification exam. After completion of training you can take quiz which enable you to check your knowledge and enables you to clear relevant certification at higher marks/grade also you will be able to work on the technology independently.
In Self-paced courses trainer is not available whereas in Online training trainer will be available for answering queries at the same time. In self-paced course we provide email support for doubt clearance or any query related to training also if you face some unexpected challenges we will arrange live class with trainer.
All Courses are highly interactive to provide good exposure. You can learn at your own place and at your leisure time. Prices of self-paced is training is 75% cheaper than online training. You will have lifetime access hence you can refer it anytime during your project work or job.
Yes, at the top of the page of course details you can see sample videos.
As soon as you enroll to the course, your LMS (The Learning Management System) Access will be Functional. You will immediately get access to our course content in the form of a complete set of previous class recordings, PPTs, PDFs, assignments and access to our 24×7 support team. You can start learning right away.
24/7 access to video tutorials and Email Support along with online interactive session support with trainer for issue resolving.
Yes, You can pay difference amount between Online training and Self-paced course and you can be enrolled in next online training batch.
Yes, we will provide you the links of the software to download which are open source and for proprietary tools we will provide you trail version if available.
Please send an email . You can also chat with us to get an instant solution.
Intellipaat verified certificates will be awarded based on successful completion of course projects. There are set of quizzes after each couse module that you need to go through . After successful submission, official Intellipaat verified certificate will be given to you.
Towards the end of the Course, you will have to work on a Training project. This will help you understand how the different components of course are related to each other.
Classes are conducted via LIVE Video Streaming, where you get a chance to meet the instructor by speaking, chatting and sharing your screen. You will always have the access to videos and PPT. This would give you a clear insight about how the classes are conducted, quality of instructors and the level of Interaction in the Class.
We will help you with the issue and doubts regarding the course. You can attempt the quiz again.
High quality interactive e-learning sessions for Self paced course. For online instructor led training, total course will be divided into sessions.
HANDS ON EXERCISE AND PROJECT WORK: 166 HRS
Each module will be followed by practical assignments and lab exercises. Towards the end of the course, you will be working on a project where would be expected to complete a project based on your learning. Our support team is available to help through email, phone or Live Support for any help required.
You will get Lifetime access to high quality interactive e-Learning Management System . Life time access to Virtual Machine and Course Material. There will be 24/7 access to video tutorials along with online interactive sessions support with trainer for issue resolving.
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.
This course is designed for clearing Cloudera Certified Developer for Apache Hadoop (CCDH). At the end of the course there will be a quiz and project assignments once you complete them you will be awarded with Intellipaat Course Completion certificate.
This course is designed for clearing Cloudera Certified Administrator for Apache Hadoop (CCAH). At the end of the course there will be a quiz and project assignments once you complete them you will be awarded with Intellipaat Course Completion certificate.
This course is designed for clearing any Apache Strom training organized by any reputed agency like HortonWorks. At the end of the course there will be a quiz and project assignments once you complete them you will be awarded with Intellipaat Course Completion certificate.
This course is designed for clearing Apache Spark Certification examination of any reputed company. At the end of the course there will be a quiz and project assignments once you complete them you will be awarded with Intellipaat Course Completion certificate.
Intellipaat enjoys strong relationship with multiple staffing companies in US, UK and have +60 clients across the globe. If you are looking out for exploring job opportunities, you can pass your resumes once you complete the course and we will help you with job assistance. We don’t charge any extra fees for passing the resume to our partners and clients
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