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Advantages of Using Hadoop

Ipsita Pattnaik May 19, 2017

Big Data Hadoop Apache Hadoop


Organizations that are majorly data driven and process large datasets are increasingly adopting Apache Hadoop as a potential tool. This is because of its ability to process, store and manage vast amounts of structured, unstructured or semi-structured data. Basically, Hadoop is a distributed data storage and processing platform with three core components that are the HDFS distributed file system, the MapReduce distributed processing engine running on top and the YARN (Yet Another Resource Negotiator).

Apache Hadoop uses parallel processing techniques to distribute processing across multiple nodes for rapidity. It can also process data where it is stored instead of needing to transport it across a network.

Besides the widely known advantages, Hadoop has numerous other benefits that aren’t always as obvious. Let us look at a few of them.

It is scalable

Increase in the creation and collection of data is often seen as bottlenecks for Big Data analysis. Many enterprises face the challenge of keeping data on platform which gives them a single consistent view. Hadoop clusters provide a highly scalable storage platform. It can store and distribute datasets across hundreds of inexpensive servers. It also gives the possibility of scaling the cluster by adding extra nodes. This allows enterprises to run applications on thousands of nodes and deal with thousands of terabytes of data.

It is cost effective

Hadoop clusters have proven to be a very cost-effective solution for expanding datasets. It is designed to scale-out architecture that can affordably store all of a company’s data for use sometime later. This saves a lot of cost and improves the storage capability tremendously.

It is resilient and highly available

Hadoop is a highly fault tolerant platform. HDFS makes 3 copies of the entire file across three computer nodes so that if a node becomes offline, it has more two copies.

The HA (high availability) configuration protects the cluster during planned and unplanned downtime. It can protect against the single-point failure of a master node (the NameNode or JobTracker or ResourceManager).

It is flexible

The built-in failure protection of Hadoop combined with its use of commodity hardware makes it very attractive. It allows the enterprises to store and capture multiple data types that include images, videos and documents etc. It also makes them easily ready for processing and analysis. This flexibility allows businesses to expand and adjust their data analysis operations.

It enhances speed

Hadoop is based on HDFS and MapReduce. HDFS stores data and MapReduce is used for processing data in parallel. The storage method is based on a distributed file system which maps data stored on the cluster. MapReduce us also generally stored in the same servers that enhances the faster processing of data.

If your business is dealing with large volumes of unstructured data, Hadoop us able to process hundreds of terabytes of data in a few minutes efficiently.

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