All Eyez On Data

Hadoop Big Data

Hadoop is an open-source platform for managing distributed storage and processing of enormous sets of data. It is software that runs on clusters of computers instead of just one PC. Distributed storage is one of the most important things Hadoop provides.

A lot of people do not want to be limited by a hard drive. Companies that deal with Big Data have terabytes coming into their databases on a daily basis. This is where Hadoop comes in. the nice thing about distributed storage is that you are able to add even more computers to the cluster and their hard drives will become a part of your data storage.

Hadoop gives you the ability to view the data distributed across all of the hard drives in your cluster as one single file system.

Hadoop is a system that gives an answer to two challenges that arise with traditional databases.

The first one is capacity. As Hadoop uses a distributed file system, it enables the data to be split. It splits the data into pieces and chunks and makes it possible to save it across all of the clusters of commodity servers. These commodity servers are made of simple hardware configurations that’s makes them economical and easily scalable.

The second problem is speed. As data is sent to the database, it is split into pieces and tasks and thus it drastically improves the processing speed.

Hadoop Big Data

Hadoop is made up of four modules that are used in analyzing Big Data. These are: distributed file system, MapReduce, Hadoop Common and Yarn.

Distributed File System: This lets data to be kept in such a way that it is easy to get to, even when it is crossways a large number of linked devices.

MapReduce: MapReduce interprets data from the database and then puts it in format that is readable and that can be used for later analysis.

Hadoop Common: This provides tools required to analyze the data stored in Hadoop file.

Yarn: It manages the resources and runs the analysis

Components of the Hadoop System

The Hadoop system is made up of components that are extensively used. They are all supplementary by nature.

Hive – this is a data warehousing system. It helps to query enormous sets of data in the HDFS. Before Hive was used, many developers had problems creating complex MapReduce jobs. These jobs were to query the Hadoop data. This system uses Hive Query Language (HQL). It is a language that is similar to the syntax of SQL. It is easier to understand and work with as many developers used SQL foundations for their systems. Hive comes with many advantages.

Pig – is a warehouse system that has been develpooed by Yahoo. It has a lot of similarities with Hive. Like Hive, it eliminates and gets rid of the need to make MapReduce functions. Just lie HQL, the language that is used in this warehouse system is called “Pig Latin”. It is a language that is a lot closer to SQL. It is a high-level data flow language.

There are a lot of advantages that come with Hadoop. They are:

1. Faster storage and processing of vast amounts of data

2. Flexibility

3. Reduced cost

4. Processing power

5. Fault tolerance

6. Scalability

Hadoop is a tool that has many applications in the real world. For example, it is used to understand customer requirements. A lot of the international companies and enterprises especially in the financial industry use this technology to better understand the needs of their customers. They do so by analyzing the data that comes from their activity. Then, that data is analyzed and used to improve the user experience.

The use of Hadoop doesn’t end there, though. A lot of institutions that are related to the medical community and industry use their storage systems for monitoring. They use to take a closer look to the data that is connected to a lot of medical issues and treatment results. For example, scientists, researches and medical staff are able to analyze this data. Once they do that, they have an easier time identifying medical conditions, to predict better medications and opt for better treatment plans.

Overall, this is a warehouse system that is efficient in managing and handling Big Data. If it is implemented with other effective steps, it is able to overcome big challenges. It is an adaptable technology that companies use to deal with gigantic tons of data.