Apache Hadoop Ozone

Apache Hadoop Ozone

Ozone is a scalable, distributed object store for Hadoop. Applications like Apache Spark, Hive and YARN, can run against Ozone without any modifications. Ozone comes with a Java client library and a command line interface which makes it easy to use Ozone. This client library supports both RPC and REST protocols.

Ozone consists of volumes, buckets, and Keys.

• Volumes are similar to user accounts. Only administrators can create or delete volumes.
• Buckets are similar to directories. A bucket can contain any number of keys, but buckets cannot contain other buckets.
• Keys are similar to files. A bucket can contain any number of keys.

Apache Hadoop Ozone 0.3.0-alpha is released.

• A new S3 compatible rest server is added. Ozone can be used from any S3 compatible tools (docs, HDDS-434)
• Ozone Hadoop file system URL prefix is renamed from o3:// to o3fs:// (HDDS-651)
• Extensive testing and stability improvements of OzoneFs.
• Spark, YARN and Hive support and stability improvements.
• Improved Pipeline handling and recovery.
• Separated/dedicated classpath definitions for all the Ozone components. (HDDS-447)

This is an alpha release.

Hadoop Vendors List

Big Data Hadoop

Apache Hadoop is an abstraction or concept, which needs some implementation. We have many vendors who provides this implementation.

The top 6 vendors offering Big Data Hadoop solution are:

• Cloudera.
• HortonWorks.
• Amazon Web Services Elastic MapReduce Hadoop Distribution.
• Microsoft.
• MapR.
• IBM InfoSphere Insights.

BigData Hadoop News

Introduction

Just Like Scala News  post, in this post I'm going to update all news regarding BigData, Apache Hadoop and Hadoop Ecosystem.

I will also list all free and best Haoop tutorials here.

Please check this post regularly to know more news.

NOTE:- Please read this post news from bottom to top.

BigData Hadoop Up-to-date News:

• Good BigData Tutorial at 

            https://github.com/haifengl/bigdata

• Apache Hadoop 2.7.2  is relaease on January 25, 2016. It is the latest available stable version.

• Apache Hadoop 2.7 is release on June 2015

• Apache Hadoop 2.6.4 is released on 11 February, 2016

• Apache Hadoop Website

          http://hadoop.apache.org/

• Apache Hadoop FAQs at

          https://wiki.apache.org/hadoop/FAQ

• Apache Hadoop 2 is release on 23 May, 2012

• Apache Hadoop 1.0.0 was release on 27 December, 2011

• Hadoop First version 0.1.0 was release on April 2006

• Google has release its GFS(Google File System) paper on October 2003

Thank you for reading my posts.

Please drop me a comment if you like my tutorials or have any issues/suggestions.

How MapReduce Algorithm Works

Introduction

In this post, I will discuss about MapReduce Algorithm in general. We don't develop any MapReduce programs in this post.

If you want to learn and develop MapReduce programs, please go through my other posts on MapReduce.

MapReduce Algorithm

As a BigData Hadoop Developer, we should have very good knowledge about MapReduce Algorithm.

One of the main components of Apache Hadoop is MapReduce.

This MapReduce Component was implemented by following MapReduce Algorithm.

I have already delivered the following two topics through JournalDev website:

1. How MapReduce Algorithm Works
2. MapReduce Algorithm with WordCounting Example.

Please click here "MapReduce" to get a tutorial on those two topics.

Please find direct link to access this tutorial below:
http://www.journaldev.com/8848/how-mapreduce-algorithm-works

Please drop me a comment if you like my post or have any issues/suggestions.

Happy Learning.

Difference between Hadoop 1.x and Hadoop 2.x

I have already delivered some Hadoop Basics in my previous posts and also discussed about Hadoop 1.x & Hadoop 2.x Architecture.

In this post, we are going to discuss the following important contents:

1. Differences between Hadoop 1.x and Hadoop 2.x

2. Hadoop 1.x Limitations

3. How Hadoop 2.x overcomes Hadoop 1.x Limitations

4. Hadoop 2.x YARN Benefits

Please access the below link to answer the above questions

• Hadoop 1.x Vs Hadoop 2.x

Please drop me a comment if you like my tutorials.

Complete BigData Hadoop With Cloud Tutorial

I'm going to deliver a series of Tutorials on the following concepts one by one through www.journaldev.com website:

1. BigData
2. Hadoop
3. Hadoop Ecosystem
4. Cloud
5. Amazon Web Services
6. Google Cloud Platform
7. Microsoft Azure
8. BigData with Cloud
9. Spring Cloud – Cloud Foundry
10. Spring Hadoop Module
11. Spark With Hadoop

First we will start with BigData Basics, then move to Hadoop to Cloud then finally we will discuss about “How to use BigData Solutions with Cloud Platforms”. We will discuss different BigData and Cloud Platforms Solutions available in the current market like Amazon Web Services (AWS), Google Cloud Platform, Microsoft Azure, IBM Bluemix, Pivotal Cloud Foundry, Yahoo Cloud Platform etc.

Finally we will discuss how to develop applications using Spring Cloud and Spring Hadoop Modules. We feel that these two are really Big subjects: BigData and Cloud so it may take more time to discuss all these concepts in-detail with Real-time examples. Please bare with us.

Sl No	Post Title	Post URL
1	Introduction To BigData	http://www.journaldev.com/8734/introduction-to-bigdata
2	Introduction to Hadoop, BigData Life-Cycle Management	http://www.journaldev.com/8795/introduction-to-hadoop
3	Hadoop 1.x: Architecture, Major Components and How HDFS and MapReduce Works	http://www.journaldev.com/8808/hadoop1-architecture-and-how-major-components-works
4	Hadoop 2.x: Architecture, Major Components and How YARN, HDFS and MapReduce Works	http://www.journaldev.com/8800/hadoop2-architecture-and-how-major-components-works
5	CloudEra Hadoop VMWare Single Node Environment Setup	http://www.journaldev.com/8811/cloudera-hadoop-vmware-singlenode-setup
6	Differences between Hadoop 1.x and Hadoop 2.x
7	How MapReduce Algorithm Works With Simple Example
8	MapReduce Word Counting Example
9	Some More MapReduce Examples

I will update this post as and when I deliver new posts.

Please drop me a comment if you like my posts or have any issues/suggestions.

Happy BigData Hadoop with Cloud Learning!