This is default featured slide 1 title

Go to Blogger edit html and find these sentences.Now replace these sentences with your own descriptions.

This is default featured slide 2 title

Go to Blogger edit html and find these sentences.Now replace these sentences with your own descriptions.

This is default featured slide 3 title

Go to Blogger edit html and find these sentences.Now replace these sentences with your own descriptions.

This is default featured slide 4 title

Go to Blogger edit html and find these sentences.Now replace these sentences with your own descriptions.

This is default featured slide 5 title

Go to Blogger edit html and find these sentences.Now replace these sentences with your own descriptions.

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
Add caption
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.

Wednesday, 9 April 2014

Embracing SQL-MR to Handle Advanced (Analytical) Queries

The story behind the development of the SQL-mr function, as far as the world of enterprise data warehouse is concerned, is quite a funny one. Simply told, this resourceful function actually started out just as a simple expression evaluator, which is add, multiply, subtract and divide. It is from this humble beginning that this function grew into a full fledged programming language. With so much programming in the world, it is not impossible to wonder whether there aren’t enough of them yet. What is more, one cannot help but wonder what makes a new programming language more special than the earlier ones. Well, there are obviously convincing responses to your questions. 

This programming language usually runs in and as a SQL-mr function. The user passes on the program he or she wishes to run at the command line and done, it executes the code. Specifically, reading in normally record from the functions ON clause and passing then records back to your database. If you were already wondering if it can handle multi functions, then the answer is yes. What is more, it usually supports JDBC. This means that you can read from through a cursor variable, you can update, delete, insert records and even execute arbitrary SQL using a JDBC connection.

Another great thing about this function is that it bears the capacity to execute programs that were previously stored as enterprise data warehouse via the install command. This explains why it is considered an effectively kept procedure language.

There is actually a lot more of good things as far as this function is considered. Typically, SQL map reduce is a solution specifically designed for handling advanced analytical queries. Generally, the presence of more complex queries and increased data demands a more powerful enterprise data warehouse platform. A good number of database vendors have actually implemented SQL MapReduce. Better explained, it is a combination of the popular database language, SQL and a programming model developed by Google known as MapReduce.

Advantages of SQL MapReduce

  • Map reduce is usually implemented as a set of SQL table of functions. Despite being extremely sophisticated on the inside, these tables resemble the ones supported by SQL.
  • Individuals developing a report have to learn neither a new language nor a new set of statements. In any case, they just have to study the specific parameters of the MR functions.
  • Any existing reporting as well as an analytical tool that usually supports SQL can work effectively with SQL MapReduce.
  • SQL MapReduce is as storage independent and declarative as the SQL
  • With SQL MapReduce, developers have the liberty of writing their personal analytical functions and can use the language they consider comfortable to them.