Big data analytics and meaningful insights have transformed the businesses and driving growth. According to a survey, there is more than 3 million jobs are predicted by the end of 2020.

With the warn adoption of Hadoop platform, it is expected that more than 50 percent of data will be processed by big data platform by the end of the year 2020. So, demand for Hadoop professionals is actually very high, you can think of.

Data governance becomes very important in such scenarios. What’s data governance? It’s the complete administration of usability, integrity, availability, and data security in an organization.

It has created the need for a more organized file system for storage and processing of data, when it is observed a sudden increase in the volume of data from the order of gigabytes to zettabytes.


Once in a while, there may come a feeling that you are stuck in the same job profile and living a monotonous professional life. This generally leads to the realization that a change in your profile is much needed.

The reason that folks such as chief financial officers are excited by the thought of using Hadoop is that it lets us store massive amounts of data across a cluster of low cost commodity servers — that’s music to the ears of financially minded people.

Now we are very well familiar with the power packed characteristics and nature of Hbase.

All scripts are run on a single machine without requiring Hadoop MapReduce and HDFS. This can be useful for developing and testing Pig logic.

Moving data and running different kinds of applications in Hadoop is great stuff, but it’s only half the battle.

Unlike the supervised learning method described earlier for Mahout’s recommendation engine feature, clustering is a kind of unsupervised learning — where the data labels points are not known ahead of time and should be inferred from the data without

Big data is all about applying analytics to more data, for more people.

Although the mapper and reducer implementations are all we need to perform the MapReduce job, there is one more piece of code necessary in MapReduce:

We have already seen the Pig architecture and Pig Latin Application flow. We also learn the Pig Design principle in the previous post.

Pig Latin is the programing platform which provides a language for Pig programs. Pig helps to convert the Pig Latin script into MapReduce tasks that can be run within Hadoop cluster.

Java MapReduce programs and the Hadoop Distributed File System (HDFS) provide us with a powerful distributed computing framework, but they come with one major drawback

The most key component of YARN is the Resource Manager, which governs and maintains all the data processing resources in the Hadoop cluster.

In my previous post, I have explained various Hadoop file system commands, in which I also explained about the “ls command”.

The core concept of HDFS is that it can be made up of dozens, hundreds, or even thousands of individual computers, where the system’s files are stored in directly attached disk drives.

Hadoop was originally designed with an intention to store petabyte data at the scale, with any Potential limitations to scaling out are minimized.

Hadoop is primarily structured and designed to be deployed on a massive cluster of networked systems or nodes,

After we have stored piles and piles of data in HDFS (a distributed storage system spread over an expandable cluster of individual slave nodes),

For creating new files in HDFS, a set of process would have to take place (refer to adjoining figure to see the components involved

Before Hadoop 2 comes to the picture, Hadoop clusters were living with the fact that Name Node has placed limits on the degree to which they could scale.

Here we enlist and identify some common codecs that are supported by the Hadoop framework.

Just to be clear, storing data in HDFS is not entirely the same as saving files on your personal computer.


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