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Thursday, 30 October 2014

Hadoop and Big Data: Detailed Information for the Non-Tech Savvy Minds

During the last few years, the surge in the volume of data processed by businesses has gone far beyond the capability of traditional systems. Addressing the ever-increasing concern of efficient data management, Hadoop pioneered a fundamentally new way to store and process data. Today, with the arrival of different versions of Hadoop, it has become for organizations to understand the benefits of big data platform for C-suite executives. The latest Hadoop2.0 has created a completely new framework where big data can be stored, mined and processed with remarkable ease.


Hadoop Software: An Introduction

Hadoop is an open source, Java-based programming system that facilitates seamless processing of large data distributed across the computing environment. Hadoop, a part of apache project, enables parallel processing of both structured and unstructured data through cost-effective and industry-specific servers. It enables servers to store and process data with a provision of limitless scalability. In addition, the software facilitates rapid data transfer rates among thousands of nodes involved and support system to operate uninterrupted in case of any failure.

Hadoop and Big Data: The Advantages

Employing a Hadoop software solution is instrumental in efficiently managing big data. Many prominent organizations such as Google, Yahoo, and IBM leverage Hadoop, particularly for search and advertising applications. In addition, Hadoop has enabled numerous organizations to find value in data, which was previously considered useless.
Some of the key advantages of Hadoop software include:

Scalable

Hadoop system is easily scalable to accommodate changing organizational needs. New nodes can be added as per the need without making any change to data format or how it is loaded, written or applied.

Cost-effective

Hadoop software lends simultaneous computing capability to commodity servers, which results in decrease in the cost per volume of storage, consequently making it a highly cost-effective solution to manage big data. 
Flexible
Hadoop software solution can process almost every type of data – structured and unstructured – and that too from a variety of sources.

Reliable

In case of fault in any of the nodes, the system redirects the work to another location and continues processing, without missing any bit of it. Hence, it promises an exceptionally reliable solution to manage big data.

Conclusion


Employing Hadoop software is an ideal solution for any organization that needs to store and process big data. However, it is important to find a credible provider of Hadoop and big data software that not only promises robust and cost-effective solutions, but also provides round the clock support. 

Sunday, 7 September 2014

Manage Data within Organization with Hadoop

In every organization, irrespective of its popularity and size, it is extremely important to manage data well. Correct data management can make or break every organization and change the level of performance of every employee within the organization. In order to manage organizational data and help them perform well, Hadoop was created. Hadoop architecture is a well-known, open source and respected framework by Apache that guarantees scalability, reliability and offers distributed computing. It is known to break large data clusters into various small data so that it can manage well.

It is a software framework that is created to simplify tasks running on big data clusters. To manage huge data sets with extreme conviction this system requires some top quality ingredients that can help create the desired results. It has a well-structured architecture that comprises a number of elements. At the bottom, it has Hadoop Distributed file system (HDFS) that is known to store files across nodes within the Hadoop cluster. Above Hadoop Distributed file system (HDFS), there is a MapReduce engine that comprises of two basic elements named Task-trackers and Job-trackers.
On the above area, a lot of elements have significant purpose such as a Job-tracker is added in the system to perform better task assignment. On the other hand, Task-tracker is present to perform Hadoop map and reduce tasks, the most acute and significant task in the whole process of data management. During the time of installation, there are three different modes including Local mode, which is also known as Standalone Mode, Fully-Distributed Mode and Pseudo-Distributed Mode. In order to use these software, there is a huge requirement of the software such as Java TM 1.6.x. If would be a great deal if you will use it from the sun.
While installing Hadoop architecture, it is extremely important for everyone to use the correct configuration. If you require to use Hadoop MapReduce model for processing the large amount of data within the organization, it is important for you to understand the software structure and every information about all the elements in detail. Do not miss a single step, otherwise you won’t be able to get desired results.
Although, Hadoop is an open source software framework, Hadoop training is extremely important in order to make the most of this framework. Thanks to the advent of the internet, today, it is not difficult to get Hadoop Map Reduce training online and make the most of this service.

Tuesday, 24 June 2014

Basic Introduction of Hadoop Map Reduce

An open source java implementation of MapReduce framework, Hadoop is introduced by Google. However, the main developer and contributor of Hadoop is said to be Yahoo, which amazed a lot of people because Yahoo, being one of the major competitors of Google released an open source version of a framework that was introduced by it competitor, Google. Nevertheless, Google has granted patent for it.
One of the major reasons why Yahoo could easily use the technology is because the Map and Reduce functions and features have been known and used in the field of functional programming for a lot of years. This is another major reason why Hadoop Map Reduce has gained a higher popularity as part of the Apache project. Today, numerous companies are using this technology as a significant component in their web architecture.
Hadoop Mapreduce
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The technology is used to simplify the process of data management within organizations. Every organization depends upon its data to function and perform better. However, it is seen that large and complicated data present within the organization increases complications and reduce work productivity. In such situations, the used of Hadoop Ecosystem helps organization manage data better by distributing large data clusters into various small parts.
It is the major and most significant framework for data analysis and processing that sometimes can be presented as an alternative to conventional relational databases. However, it is not a real database even if it does offer no SQL one called H Base as one of its major tools because it is a framework for distributing major data processes.
On the other hand, Map Reduce is a basic programming model that is introduced by Google, which is a significant part of Hadoop. It is based on the use of two major functions taken from basic fundamental programming: Map and Reduce, where Map processes a key pair into a list of intermediate key pairs and Reduce takes an intermediate key and the set of values for that particular key. In this process, the user writes both the mapper and the reducer processes. Hadoop Map framework groups together intermediary values linked with the same key to process them to the equivalent Reduce.
If you feel that by including Hadoop framework you save increase your organizational proficiency and manage data within the organization better, you can find this framework for free anywhere on the net. However, in order to excel in the field and make the best use of this framework, Hadoop training is extremely important.

Tuesday, 6 May 2014

Is Hadoop The Future Of Enterprise Data Warehousing?

While the answer to this question may not have been verified yet, what is clear is that Hadoop is proving itself in the world of enterprise data warehousing. Its presence is felt especially in handling execution of embedded advanced analytics and where unstructured content is concerned. This is actually the most dominating role of Hadoop in the production environments. It is true that the traditional Hadoop-less enterprise data warehousing is working effectively from the standpoint of architecture. However, considering the fact that the majority of cutting edge cloud analytics is taking place in Hadoop clusters, in less than one or two years, vendors will be bringing Hadoop distributed file system close to their architectural hearts. For the numerous enterprise data warehouse vendors who are yet to be fully committed to Hadoop, circumstances surrounding the increasing adoption of this open source strategy will actually force them to embrace it.

If studied objectively, it is not impossible to realize that petabyte staging cloud is just an initial footprint of Hadoop. Organizations are quickly moving towards the enterprise data warehousing as the hub for all their advanced analytics. Typically, vendors are expected to incorporate Hadoop technologies such as Hadoop distributed file system, pig, hive and the popular MapReduce in their architectures. Surprisingly, MapReduce is experiencing an impressive growth in the world of enterprise data warehousing.

This impressive growth is expected to compel enterprise data warehousing vendors to maximize their platforms of MapReduce in line with high performance support such as SAS, R, SPSS and other statistical formats and languages. There are a number of factors that are truly a clear indication that this is already happening. For instance, the recent announcement about one of the Hadoop products by EMC Green plum and the emergence of competitors with similar road maps is a clear indication that Hadoop will shape the future of enterprise data warehousing.

Hadoop for the structured data may actually be more relevant for firms that are planning to  push or are already pushing structured data to the cloud; either private or public. It is undeniable that it is actually the core platform as far as big data is concerned. Additionally, it is a core convergence focus for the purpose of enterprise application, in analytics as well as middleware vendors essentially everywhere. This may actually mean that Hadoop could be the bright future that the world of enterprise data warehousing has been waiting for the longest time.