Big Data Hadoop
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Big Data with Hadoop
What is Hadoop?
Hadoop is an open source, Java based framework used for storing and processing big data. The data is stored on inexpensive commodity servers that run as clusters. Its distributed file system enables concurrent processing and fault tolerance. Developed by Doug Cutting and Michael J. Cafarella, Hadoop uses the MapReduce programming model for faster storage and retrieval of data from its nodes. The framework is managed by Apache Software Foundation and is licensed under the Apache License 2.0.
Who Can Learn Bigdata hadoop
How Hadoop Improves on Traditional Databases?
Hadoop solves two key challenges with traditional databases:
1. Capacity: Hadoop stores large volumes of data.
By using a distributed file system called an HDFS (Hadoop Distributed File System), the data is split into chunks and saved across clusters of commodity servers. As these commodity servers are built with simple hardware configurations, these are economical and easily scalable as the data grows.
2. Speed: Hadoop stores and retrieves data faster.
Hadoop uses the MapReduce functional programming model to perform parallel processing across data sets. So, when a query is sent to the database, instead of handling data sequentially, tasks are split and concurrently run across distributed servers. Finally, the output of all tasks is collated and sent back to the application, drastically improving the processing speed.
Benefits of Hadoop for Big Data
For big data and analytics, Hadoop is a life saver. Data gathered about people, processes, objects, tools, etc. is useful only when meaningful patterns emerge that, in-turn, result in better decisions. Hadoop helps overcome the challenge of the vastness of big data:
1. Resilience — Data stored in any node is also replicated in other nodes of the cluster. This ensures fault tolerance. If one node goes down, there is always a backup of the data available in the cluster.
2. Scalability — unlike traditional systems that have a limitation on data storage, Hadoop is scalable because it operates in a distributed environment. As the need arises, the setup can be easily expanded to include more servers that can store up to multiple petabytes of data.
3. Low cost — As Hadoop is an open-source framework, with no license to be procured; the costs are significantly lower compared to relational database systems. The use of inexpensive commodity hardware also works in its favor to keep the solution economical.
4. Speed — Hadoop's distributed file system, concurrent processing, and the MapReduce model enable running complex queries in a matter of seconds.
5. Data diversity — HDFS has the capability to store different data formats such as unstructured (e.g. videos), semi-structured (e.g. XML files), and structured. While storing data, it is not required to validate against a predefined schema. Rather, the data can be dumped in any format. Later, when retrieved, data is parsed and fitted into any schema as needed. This gives the flexibility to derive different insights using the same data.
Exclusive Key factors with Redprism?
Redprism is a given corporate trainings to different reputed companies. In Hadoop training all sessions are teaching with examples and with real time scenarios. We are helping in real time how approach job market, Hadoop Resume preparation, BigData Hadoop Interview point of preparation, how to solve problem in Hadoop projects in job environment, information about job market etc. Redprism provides classroom Training in Noidaand online from anywhere. We provide all recordings for classes, materials, sample resumes, and other important stuff. Hadoop Online Training We provide Hadoop online training through worldwide like India, USA, Japan, UK, Malaysia, Singapore, Australia, Sweden, South Africa, and etc. Redprism providing Hadoop Corporate Training worldwide depending on Company requirements with well experience real time experts.
Prime Features why to Join Red Prism?-
· Industry Expert Trainers with 10-15 years of experience.
· Course content is curated by best Subject Matter Experts.
· Practical Assignments.
· Real Time Projects.
· Video recording of each and every session.
· Yours doubts are clarified with 24*7 assistance by our experts.
· We conduct regular Mock tests and certifications at the end of course.
· Certification Guidance.
· Recognized training complete certificate.
· 100% Placement Assistance.
· Less fees as compared to other institutes.
· Flexi payment options
· Scholarship Available
- Overview of Big Data Technologies and Big Data Challenges
- How Hadoop solves Big Data problem
- Hadoop and its features
- Hadoop 2.x Cluster Architecture
- Federation and High Availability Architecture
- Typical Production Hadoop Cluster
- Hadoop Cluster Modes
- Common Hadoop Shell Commands
- Hadoop 2.x Configuration Files
- Single Node Cluster & Multi-Node Cluster set up
- Basic Hadoop Administration
- Introduction to UNIX shell.
- Basic Commands of UNIX
- Basic of JAVA Programming Language
- OOP's Concept in Java
- String Classes/Array/Exception Handling
- Using Collection Classes
- Explaining Various file systems
- HDFS, GFS, POSIX, GPFS
- Explain clustering methodology
- Master Nodes and slave nodes
- Starting and Stopping HDFS & YARN daemon services
- Formatting NameNode
- Exploring important configuration files
- Exploring HDFS File System Commands
- Data Loading in Hadoop
- Copying files from DFS to LFS
- Copying files from LFS to DFS
- Exploring Hadoop Admin Commands
- Understanding Hadoop Safe Mode – Maintenance state of NameNode
- Exploring YARN Commands
- Executing YARN Jobs
- Monitoring YARN Jobs
- Monitoring different -appTypes
- Killing YARN Jobs
- Exploring NameNode UI
- Exploring ResourceManager UI
- Introduction to HBase and its History
- Hbase Common Use Cases
- Understanding Hbase Client –Shell
- Hbase Architecture
- Hbase Building Components
- Log Structured Merge Trees, B+Trees
- Read/Write Path
- Region Life Cycle
- Introduction to Hbase Schema
- Column Family, Row, cells, cell stamps.
- Hbase Java API usage
- Scan API, Filters, Counters
- Hbase Map Reduce, Hbase Bulk Load
- Introduction to MapReduce programming
- Understanding different phases of MapReduce programs
§ Understanding Key/Value pair
§ What it means?
§ Why key/value data?
- Flow of Operations in MapReduce
- Hadoop Data Types
- Writing MapReduce programs using Java
§ Creating Mapper class
§ Creating Reducer class
§ Creating Driver program
- Deploying MapReduce programs in the cluster
- Understanding and Implementing Combiner
- Exploring HashPartitioner
- Understanding and implementing Partitioner.
- Setting up MapReduce to accept command line arguments
- The Tool, ToolRunner and GenericOptionsParser
- A Walkthrough of Hive Architecture
- Understanding Hive Query Patterns
- Configuring default Hive Metastore
- Exploring Hive table types
§ Internal tables
§ External tables
- Different ways to describe Hive tables
- Use of different types of tables.
- Data loading techniques.
§ Loading data from Local File System to Hive Tables.
§ Loading data from HDFS to Hive Tables.
- Hive Complex Data types
- Exploring Hive built-in Functions
- Introduction of PIG Architecture
- Requirement of Pig
- How it is useful over Map Reduce
- Working with pig Script
- Running and managing Pig Script
- Perform Streaming Data Analytics through PIG
- Pig Latin Data Types
- Cheat Sheet.
- Different Expressions and Commands
- Cogroup and Different Joins
- Limit, Sample, Parallel
- Spark Introduction
- Functional Programming
- Spark Streaming
- Spark SQL
- Spark MLLib
- Basic Data types used in Scala
- Operators/Methods/Classes in Scala
- Control Structures/Collections/Libraries of Scala
- Importance of RDD
- Creating an RDD
- Operations and Methods
- Understand with an example
- Sqoop Overview
- Sqoop JDBC Driver and Connectors
- Sqoop Importing Data
- Various Options to Import Data
- Understanding Sqoop Jobs
- Table Import
- Filtering Import
- Incremental Imports using Sqoop
- Oozie Preview
- Oozie Components
- Oozie Workflow
- Scheduling Jobs with Oozie Scheduler
- Demo of Oozie Workflow
- Oozie Coordinator Preview
- Oozie Commands
- Oozie Web Console
- Oozie for Map Reduce
- Combining flow of Map Reduce Jobs
- Hive in Oozie
- Hadoop Project Demo
- Hadoop Talend Integration
- Understanding Scheduling Framework
- Role of Scheduling Framework in Hadoop
- Quartz Job Scheduling Library
- Using Quartz API
- Scheduling Hive Jobs using Quartz scheduler
- Sample Dataset Description
- Analysis of Social Media channels
- Practical of Pig, Hive, Flume and MapReduce
- Airline Data Analysis
- Introduction to Recommendation Systems
- Types of Recommendation systems
- Recommendation system evaluation
- Architecture of recommendation systems