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Hadoop is the popular open source implementation of MapReduce, a powerful tool designed for deep analysis and transformation of very large data sets. Hadoop enables you to explore complex data, using custom analyses tailored to your information and questions. Hadoop is the system that allows unstructured data to be distributed across hundreds or thousands of machines forming shared nothing clusters, and the execution of Map/Reduce routines to run on the data in that cluster. Hadoop has its own filesystem which replicates data to multiple nodes to ensure if one node holding data goes down, there are at least 2 other nodes from which to retrieve that piece of information. This protects the data availability from node failure, something which is critical when there are many nodes in a cluster (aka RAID at a server level).
Hadoop has its origins in Apache Nutch, an open source web searchengine, itself a part of the Lucene project. Building a web search engine from scratch was an ambitious goal, for not only is the software required to crawl and index websites complex to write, but it is also a challenge to run without a dedicated operations team, since there are so many moving parts. It's expensive too: Mike Cafarella and Doug Cutting estimated a system supporting a 1-billion-page index would cost around half a million dollars in hardware, with a monthly running cost of $30,000
Introduction of Hadoop
In a Hadoop cluster, data is distributed to all the nodes of the cluster as it is being loaded in. The Hadoop Distributed File System (HDFS) will split large data files into chunks which are managed by different nodes in the cluster. In addition to this each chunk is replicated across several machines, so that a single machine failure does not result in any data being unavailable. An active monitoring system then re-replicates the data in response to system failures which can result in partial storage. Even though the file chunks are replicated and distributed across several machines, they form a single namespace, so their contents are universally accessible.
Hadoop-only deployments using the open-source version from Apache. The open-source version of Hadoop is being used for an amazing array of applications
Setting up hadoop System Architecture
Each cluster has one ?master node? with multiple slave nodes.
The master node runs NameNode and JobTracker functions and
coordinates with the slave nodes to get the job done. The slaves
run TaskTracker, HDFS to store data, and map and reduce functions
for data computation. The basic stack includes? Pig and JAVA for
language and compilers.
Cluster? hardware setup:
-Number of nodes in cluster. Need One master node and number of slave nodes based on? project .
Configuration requirement :
New requirements for this project:
. Will only one or a few source systems load Hadoop, so that you can just use the HDFS API, or
will there be a significant number of source systems?
Dedicate TOR switches to hadoop
Use dedicated core switching blades or switches
Ensure application servers are ?close? to hadoop
Ethernet bounding for increasing capacity
Basic hardware recommendation:
Namenode/ JobTracker (2*1GB/s Ethernet, 16 GB of ram, 4xCPU, 100 GB disk)
Datanode ( 2x1Gb/s Ethenet, * GB of ram, $xCPU , Multiple diswks with total amount of 500+GB).
Do not mix up hardware when building up Hadoop cluster.
so to avoid this, somebody needs to know where Data Nodes are located in the network topology= give it a name.
Understanding? the scope?
Hadoop integrated with traditional databases. Organizations with traditional data warehousing and analytics in place have the option of extending their existing platform to include an integrated Hadoop implementation. Connecting existing data management resources to Hadoop provides an opportunity to tap both structured and unstructured data for insights.
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