Four Reasons To Choose Big Data Hadoop

Four Reasons To Choose Big Data Hadoop

Big Data Hadoop: Over the past ten years (precisely a few years now), huge companies such as Google, Yahoo!, Amazon and Facebook have effectively collected and connected vast amounts of information ( Big Data Analytics ), creating dissemination tools such as advertising frameworks around the web.

Apache Hadoop, the framework that supports distributed applications with high data access under a free license, is rapidly becoming a benchmark due to the enormous amounts of information it offers to enterprises.

Hadoop is a high-level Apache project built and used by a global community of contributors using the Java programming language.

Yahoo! is the most significant contributor to this project and currently uses it. But Hadoop is also used by:

What does Hadoop do?

To answer this question in general, Hadoop offers many easy-to-use libraries. However, this is only one point in favor; there are other reasons (precisely four!) why today this tool is considered valid by the most prominent organizations on the web.

Below are the four reasons you should know to approach Apache Hadoop optimally.

Reason 1: Explore data with complete sets

R, SAS, Matlab, or Python, typically require a workstation with lots of memory to slice the information and produce models. But when it comes to communication, more than PC memory is needed.

But with Hadoop, you can do a lot of exploratory searches on complete datasets.

Compose a PIG or HIVE, send it directly to Hadoop on the dataset, and retrieve the ideal results for your PC.

Reason 2: Larger mining datasets

In general, tasks perform better when there is more information to process.

However, huge datasets are not accessible or have become excessively expensive to store. Therefore, discovering new approaches to do everything optimally is necessary.

With Hadoop,  on the other hand, you can store information in RAW and use the entire data set to fabricate optimal and more accurate models.

Reason 3: Large-scale preprocessing of raw data

Hadoop is perfect for doing this kind of prep productively, as it can analyze massive datasets using devices like PIG and HIVE and scripting languages ​​like Python.

If your application requires joining large tables with billions of rows to create highlight vectors for each reference question, then HIVE or PIG is perfect.

Reason 4: Data agility

Hadoop is a “pattern on reading,” unlike most RDBMS frameworks.

This allows you to maneuver a lot of data.

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