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Openstack, Hadoop Architect Training - All In 1 Combo Course -iPartner
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Openstack, Hadoop Architect Training - All In 1 Combo Course

  1. Openstack, Hadoop Architect Training - All In 1 Combo Course
/ 345*
(* including all taxes.)


Key Features
SMALL
BATCHES
MENTORING
BY EXPERTS
FLEXIBLE
SCHEDULE
LEARN
BY DOING
GOAL
ORIENTED



Course Agenda


  • What is Big Data?
  • Factors constituting Big Data
  • Hadoop and its 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
  • 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 Big Data Hadoop Ecosystem
  • 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
  • Map Reduce in detail.
  • Comparison b/w YARN and MRV1
  • MapReduce job Execution.
  • MapReduce Combiner.
  • Mapreduce Partitioner.
  • shuffle & Sort Phase.
  • Map reduce job submission flow
  • Job launch process (Job)
  • Job launch Process (task)
  • Job launch process (Task tracker)
  • Job launch process (Task runner)
  • How mapper process with detailed example
    testing module.
  • How to develop Map Reduce Application.
  • writing unit test Best Practices for developing and writing.
  • Debugging Map Reduce applications.
  • 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
  • Complex/Nested Data Types
  • Grouping
  • Iterating Grouped Data
  • Hands-On Exercise: Analyzing Data with Pig
  • Techniques for Combining Data Sets
  • Joining Data Sets in Pig
  • Set Operations
  • Splitting Data Sets
  • Hands-On Exercise
  • What Is Hive?
  • Hive Schema and Data Storage
  • Comparing Hive to Traditional Databases
  • Hive vs. Pig
  • Hive Use Cases
  • Interacting 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
  • 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

  • What is Impala?
  • How Impala Differs from Hive and Pig
  • How Impala Differs from Relational Databases
  • Limitations and Future Directions
  • Using the Impala Shell
  • Data Storage Overview
  • Creating Databases and Tables
  • Loading Data into Tables
  • HCatalog
  • Impala Metadata Caching
  • 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
  • Selecting a File Format
  • Hadoop Tool Support for File Formats
  • Avro Schemas
  • Using Avro with Hive and Sqoop
  • Avro Schema Evolution
  • Compression
  • What is Hbase
  • Where does it fits
  • What is NOSQL
  • 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
  • What is Spark
  • Comparison with Hadoop
  • Components of Spark
  • Apache Spark- Introduction, Consistency, Availability, Partition
  • Unified Stack Spark
  • Spark Components
  • Comparison with Hadoop – Scalding example, mahout, storm, graph
  • 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
  • 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
  • Hadoop Multi Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup
  • Running Map Reduce Jobs on Cluster
  • Hadoop Multi Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup
  • Running Map Reduce Jobs on Cluster
  • Delving Deeper Into The Hadoop API
  • More Advanced Map Reduce Programming, Joining Data Sets in Map Reduce
  • Graph Manipulation in Hadoop

  • 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.
  • li>Hadoop configuration overview and important configuration file
  • Configuration parameters and values
  • HDFS parameters MapReduce parameters
  • Hadoop environment setup
  • ‘Include’ and ‘Exclude’ configuration files
  • 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
  • Best practices of monitoring a Hadoop cluster
  • Using logs and stack traces for monitoring and troubleshooting
  • Using open-source tools to monitor Hadoop cluster
  • How to schedule Hadoop Jobs on the same cluster
  • Default Hadoop FIFO Schedule
  • Fair Scheduler and its configuration
  • Hadoop Multi Node Cluster Setup using Amazon ec2 – Creating 4 node cluster setup
  • Running Map Reduce Jobs on Cluster
  • 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
  • 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
  • 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

Learn & Get

  • Understand Data science Project Life Cycle, Data Acquisition and Data Collection
  • Understand Apache Hadoop 2.7 Framework and Architecture
  • Learn to write complex MapReduce programs in both MRv1 and Mrv2
  • Understand Prediction and Analysis Segmentation through Clustering
  • Learn various advanced modules like advanced modules like Yarn, Flume, Hive, Oozie, Impala, Zookeeper and Hue.
  • Monitor a Hadoop cluster and execute routine administration procedures

Payment Method

PAYMENT METHODS
You need to pay through PayPal. We accept both Debit and Credit Card for transaction.
SCHOLARSHIPS
We subsidize our fees by 10% for military personnel, and college students with exceptional records. To apply for a scholarship, email info@ipartner.ca
FREQUENTLY ASKED QUESTIONS
In our iPartner self-paced training program, you will receive the training assessments, recorded sessions, course materials, Quizzes, related softwares and assignments. The courses are designed in such a way that you will the get real world exposure; the solid understanding of every concept that allows you to get the most from the online training experience and you will be able to apply the information and skills in the workplace. After the successful completion of your training program, you can take quizzes which enable you to check your level of knowledge and also enables you to clear your relevant certification at higher marks/grade where you will be able to work on the technologies independently.
In Self-paced courses, the learners are able to conduct hands-on exercises and produce learning deliverables entirely on their own at any convenient time without a facilitator whereas in the Online training courses, a facilitator will be available for answering queries at a specific time to be dedicated for learning. During your self-paced learning, you can learn more effectively when you interact with the content that is presented and a great way to facilitate this is through review questions and quizzes that strengthen key concepts. In case if you face any unexpected challenges while learning, we will arrange a live class with our trainer.
All Courses from iPartner are highly interactive to provide good exposure to learners and gives them a real time experience. You can learn only at a time where there are no distractions, which leads to effective learning. The costs of self-paced training are 75% cheaper than the online training. You will offer 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.
Please send an email. You can join our Live chat for instant solution.
We will provide you the links of the software to download which are open source and for proprietary tools, we will provide you the trail version if available.
You will have to work on a training project towards the end of the course. This will help you understand how the different components of courses 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.
Yes, we do keep launching multiple offers that best suits your needs. Please email us at: info@ipartner.ca and we will get back to you with exciting offers.
We will help you with the issue and doubts regarding the course. You can attempt the quiz again.
Sure! Your feedbacks are greatly appreciated. Please connect with us on the email support - info@ipartner.ca.