About Hadoop Online Training:
Hadoop Training Course is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.Audience:
This tutorial has been prepared for professionals aspiring to learn the basics of Big Data Analytics using Hadoop Framework and become a Developer. Software Professionals, Analytics Professionals, and ETL developers are the key beneficiaries of this course.Prerequisites:
Before you start proceeding with the Hadoop Training in Hyderabad, we assume that you have prior exposure to Core Java, database concepts, and any of the Linux operating system flavors.Who Uses Hadoop? A wide variety of companies and organizations use Hadoop for both research and production What is Big Data?
Big Data Training is a collection of large datasets that cannot be processed using traditional computing techniques. It is not a single technique or a tool, rather it involves many areas of business and technology.What is Hadoop Training
Hadoop Course is an Apache open source framework written in Java that allows distributed processing of large datasets across clusters of computers using simple programming models. Its framework application works in an an environment that provides distributed storage and computation across clusters of computers. It is designed to scale up from a single server to thousands of machines, each offering local computation and storage.Hadoop Architecture: Hadoop has two major layers namely: (a)Processing/Computation layer (MapReduce), and (b)Storage layer (Hadoop Distributed File System). a) MapReduce
MapReduce is a parallel programming model for writing distributed applications devised at Google for efficient processing of large amounts of data (multi terabyte datasets), on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner. The MapReduce program runs on Hadoop which is an Apache open-source framework.b) Hadoop Distributed File System
The Hadoop Distributed File System (HDFS) is based on the Google File System (GFS) and provides a distributed file system that is designed to run on commodity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant. It is highly fault-tolerant and is designed to be deployed on low-cost hardware. It provides high throughput access to application data and is suitable for applications having large datasets.How Does Hadoop Work? Hadoop runs code across a cluster of computers. This process includes the following core tasks that Hadoop performs:
- . Data is initially divided into directories and files. Files are divided into
- . These files are then distributed across various cluster nodes for further processing.
- . HDFS, being on top of the local file system, supervises the processing.
- . Blocks are replicated for handling hardware failure.
- . Checking that the code was executed successfully.
- Hadoop does not rely on hardware to provide fault-tolerance and high availability (FTHA), rather it library itself has been designed to detect and handle failures at the application layer. Servers can be added or removed from the cluster dynamically and
- Hadoop continues to operate without interruption.
- Another big advantage of Hadoop is that apart from being open source, it is compatible with all the platforms since it is Java based.
Hadoop/Bigdata Course Contentcourse Objective Summary During this course, you will learn: Introduction to Big Data and Analytics Introduction to Hadoop Hadoop ecosystem - Concepts Hadoop Map-reduce concepts and features Developing the map-reduce Applications Pig concepts Hive concepts Sqoop concepts Flume Concepts Oozie workflow concepts Impala Concepts Hue Concepts HBASE Concepts ZooKeeper Concepts Real Life Use Cases Reporting Tool Tableau 1.Virtualbox/VM Ware Basics Installations Backups Snapshots 2.Linux Basics Installations Commands 3.Hadoop Why Hadoop? Scaling Distributed Framework Hadoop v/s RDBMS Brief history of Hadoop 4.Setup hadoop Pseudo mode Cluster mode Ipv6 Ssh Installation of Java, Hadoop Configurations of Hadoop Hadoop Processes ( NN, SNN, JT, DN, TT) Temporary directory UI Common errors when running Hadoop cluster, solutions 5.HDFS- Hadoop Distributed File System HDFS Design and Architecture HDFS Concepts Interacting HDFS using command line Interacting HDFS using Java APIs Dataflow Blocks Replica 6.Hadoop Processes Name node Secondary name node Job tracker Task tracker Data node 7.Map Reduce Developing Map Reduce Application Phases in Map Reduce Framework Map Reduce Input and Output Formats Advanced Concepts Sample Applications Combiner 8.Joining datasets in MapReduce jobs Map-side join Reduce-Side join 9.Map-reduce – customization Custom Input format class Hash Partitioner Custom Partitioner Sorting techniques Custom Output format class 10. Hadoop Programming Languages :- I.HIVE Introduction Installation and Configuration Interacting HDFS using HIVE Map Reduce Programs through HIVE HIVE Commands Loading, Filtering, Grouping…. Data types, Operators….. Joins, Groups…. Sample programs in HIVE II. PIG Basics Installation and Configurations Commands…. OVERVIEW HADOOP DEVELOPER 11.Introduction 12.The Motivation for Hadoop Problems with traditional large-scale systems Requirements for a new approach 13.Hadoop: Basic Concepts An Overview of Hadoop The Hadoop Distributed File System Hands-On Exercise How MapReduce Works Hands-On Exercise Anatomy of a Hadoop Cluster Other Hadoop Ecosystem Components 14.Writing a MapReduce Program The MapReduce Flow Examining a Sample MapReduce Program Basic MapReduce API Concepts The Driver Code The Mapper The Reducer Hadoop's Streaming API Using Eclipse for Rapid Development Hands-on exercise The New MapReduce API 15.Common MapReduce Algorithms Sorting and Searching Indexing Machine Learning With Mahout Term Frequency – Inverse Document Frequency Word Co-Occurrence Hands-On Exercise. 16.PIG Concepts.. Data loading in PIG. Data Extraction in PIG. Data Transformation in PIG. Hands on exercise on PIG. 17. Hive Concepts. Hive Query Language. Alter and Delete in Hive. Partition in Hive. Indexing. Joins in Hive.Unions in hive. Industry specific configuration of hive parameters. Authentication & Authorization. Statistics with Hive. Archiving in Hive. Hands-on exercise 18. Working with Sqoop Introduction. Import Data. Export Data. Sqoop Syntaxs. Databases connection. Hands-on exercise 19. Working with Flume Introduction. Configuration and Setup. Flume Sink with example. Channel. Flume Source with example. Complex flume architecture. 20. OOZIE Concepts 21. IMPALA Concepts 22. HUE Concepts 23. HBASE Concepts 24. ZooKeeper concepts Reporting Tool.. Tableau
This course is designed for the beginner to intermediate-level Tableau user. It is for anyone who works with data – regardless of technical or analytical background. This course is designed to help you understand the important concepts and techniques used in Tableau to move from simple to complex visualizations and learn how to combine them in interactive dashboards.Course Topics Overview What is visual analysis? strengths/weakness of the visual system. Laying the Groundwork for Visual Analysis Analytical Process Preparing for analysis Getting, Cleaning and Classifying Your Data Cleaning, formatting and reshaping. Using additional data to support your analysis. Data classification Visual Mapping Techniques Visual Variables : Basic Units of Data Visualization Working with Color Marks in action: Common chart types Solving Real-World Problems with Visual Analysis Getting a Feel for the Data- Exploratory Analysis. Making comparisons Looking at (co-)Relationships. Checking progress. Spatial Relationships. Try, try again. Communicating Your Findings Fine-tuning for more effective visualization Storytelling and guided analytics Dashboards
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- The course is based totally live Project-Based Learning Approach.
- Recorded periods of all the instructions can be supplied to you.
- There can be Module-smart assignments and coding assignments to re-enforce your know- how.
- After completion, of Course, certification and profession steerage will be furnished to every pupil.
- instructor led Interactive on-line training and undertaking guidance
- Assignments at quit of every magnificence to boost the standards.
- We offer entire palms-on enjoy to each pupil on implementation of stay undertaking
- 24x7 online help crew available to assist participants with any technical queries they'll have during the course.
LEOtrainings are best the area in which u can change your future in your brilliant career with our Hadoop/Big Data online Training and certification in Hyderabad. After of entirety of the path you turn into the nicely certified expert within the marketplace. There are a lot of students are trained with the aid of LEOtrainings.
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