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Sollers Blog

Hadoop: An Essential Tool for Data Scientists

Posted by Doctor Dan on Aug 24, 2016 9:06:01 AM

Hadoop is an open-source software framework for handling very large data sets for storing data and running applications on clusters. Apache Hadoop has a storage part which provides massive storage for any kind of data. The processing part provides enormous processing power and can handle virtually limitless tasks.


 The Utilities:

 1.Hadoop can quickly store and process huge amount of any kind of data

2. Hadoop protects data and application processing against hardware failure. If a node goes down, jobs are automatically redirected to other nodes.

 3. It’s very flexible to use. It can store as much data as needed and use it later. That includes text, images and videos.

 4. This open-source framework is free; it uses commodity hardware to store large quantities of data. Therefore it is very cost-effective.

 5. Large data management is possible here just by adding nodes. Little administration is required.

 Why Hadoop is Important for Data Science?

 Apache Hadoop is a prevalent technology and is an essential skill for a data scientist. The importance of Hadoop lies in the following aspects:

 1. It helps to glean valuable insights from the data irrespective of the sources. Hadoop is the technology that actually stores large volumes of data. Data Scientists can first load the data into Hadoop and then ask questions. They don’t need to do any transformations to get the data into the cluster.

 2. A Data Scientist needs not to be a master to work with Hadoop. Without handling inter-process communication, message-passing and network programming a Data Scientist can just write java based code and use other big data tools on top of Hadoop.

 3. When the data volume is too large for system memory or data distribution is needed across multiple servers, Hadoop becomes a savior. In these cases, Hadoop helps transport data to different nodes on a system quickly.

 4. It is essential for data exploration, data filtering, data sampling and summarization.

Topics: Data Science