Big Data and Hadoop
Other,
Updated On 02 Feb, 19
Other,
Updated On 02 Feb, 19
Introduction to Big Data & Hadoop - Big Data Learning Paths - Hadoop Tutorial for Beginners - Hadoop MapReduce Tutorial - Big Data - Hadoop - Introduction to MapReduce - MapReduce Programming Tutorial - Fundamentals of Hadoop MapReduce - Understanding Hadoop MapReduce - Hadoop Tutorial - What is Big Data - Big Data Explained - Hadoop & MapReduce - MapReduce Tutorial - Hadoop distributed cache - Advanced mapreduce - What is Big Data? - Why Learn Hadoop ? - Hadoop 2.0 Features - What is Hadoop - Hadoop Tutorial for Beginners - Hadoop Tutorial - What is Hadoop - Hadoop Basics - Hadoop for Beginners - Hadoop for Data Warehousing Professional - De-Identify Mapreduce Code - Mapreduce Tutorial - What is Hive - Why Hive - Apache Hive Tutorial - Understanding Concepts of Advanced Hive - Hive Scripting - Apache Hive Tutorial - Introduction to Apache Hive - Understanding Hive Commands in Apache Hadoop - Hive Data Model - Introduction to Sqoop - Demo on Hadoop Flume - Intro To Hadoop Developer Training - Cloudera - Introduction to Hadoop Zookeeper - Introduction to HBase - Intro To Hadoop Developer Training - Hadoop installation on ubuntu - TDD Using Pig Unit - Demo - Hadoop Pig Tutorial - What is Pig Unit & Why Unit Test Pig - Hadoop Pig Tutorial - What is Big data - Why learn Hadoop - Big data and Hadoop Tutorial - Learn Hadoop - What is HBase - Why Hbase - Hbase Tutorial - What is Oozie in Hadoop - Introduction to Advanced MapReduce - Advanced MapReduce Programming Tutorial - Hadoop Distributed Cache - Advanced Mapreduce Tutorial - Combiner & Partitioner in Hadoop - Hadoop Mapreduce Tutorial - Learn Hadoop - Debugging using MRUnit Testig Framework - Hadoop Tutorial for Beginners - Joins in Hadoop Mapreduce - Mapside Joins - Reduce Side Joins - Hadoop Mapreduce - Tutorial - Setup Pig on cloudera - Pig Installation Tutorial - What is Pig and Why Pig | Pig Explained - Hadoop Pig Tutorial - Demonstrating Word Count Example Using Pig Grunt Shell - Hadoop Pig Tutorial - Hbase Architecture - Pig vs Hive ? - Hadoop Vs Traditional Database Systems - Hadoop Data Warehouse - Hadoop and ETL - Hadoop Data Mining - Big Data Tutorial - Hadoop Training - Big Data Training - What is Hadoop? - Hadoop 1.x Arch Challenges - Hadoop 2.0 Architecture - YARN - Hadoop Jobs - Introduction to Hadoop Ecosystem - Introduction to Hdfs - Demo on big data applications - Demo on Hadoop Technology
4.1 ( 11 )
Upcoming Batches www.edureka.cobig-data-and-hadoop
The question is everyones mind these days is What is Hadoop?
This 1 hour session introduces participants to Hadoop Architecture. We understand the Hadoop Eco-System and opportunities for professionals in Hadoop.
We then look at the challenges in Hadoop 1.x Architecture and the need for introducing Hadoop 2.0.
Hadoop 2.0 features such as HDFS Federation, Namenode High Availability, YARN, Multi-Tenacy are introduced.
The viewer will be able to get a very good overview of Hadoop Architecture and Hadoop 2.0 architecture advancements.
Agenda
- Introduction to Big Data
- What is Hadoop?
- What is Hadoop for
Hadoop opportunities for Java Professionals
Hadoop opportunities for ETL and Data Warehousing Professionals
Hadoop opportunities for Administrators and DBAs
Hadoop Testing Opportunities
- Hadoop 1 Architecture
- Challenges in Hadoop 1 architecture
- NameNode being a single point of failure
- Horizontal Scalability issues on scaling beyond 4000 nodes in hadoop 1.0
- Jobtracker becoming overburned since it has to both act as scheduler and has to monitor the jobs running on Task Tracker
- No support for Non-Mapreduce jobs
- No Support for Multi-Tenacy - the ability to run other than map reduce workloads in parallel
- What is Hadoop 2.0 Acrhitecture
- How does NameNode Federation help in scaling beyond 4000 data nodes
- How does the stand by Namenode work and how does failover mechanism work in case of NameNode failure
- What is YARN.
- How does YARN resource management help in reducing the burden on Job Tracker by introducing the components such as Resource Manager, Scheduler, Application Master, Node Manager, Container
- We also talk about how do non-Mapreduce jobs such as Storm (Real Time Big Data Analytics), Spark, Interactive, Online work with YARN
- We also discuss the capacity scheduler and how it helps hadoop acheive multi-tenacy using the concept of dedicated queues for different work loads.
We end with a quiz to reinforce the understanding of participants and finally take up questions from participants.
Happy Learning
Team Edureka
Sam
Sep 12, 2018
Excellent course helped me understand topic that i couldn't while attendinfg my college.
Dembe
March 29, 2019
Great course. Thank you very much.