Each human or organization doing various activities are constantly generating massive amounts of data. In real life, when you visit supermarkets, doctors, institutions, log into a bank account, visit webpages, spend time in social networks, buy online – you leave a footprint of data. The data of your past carries lots of interesting information about your human habits, interests, behavior patterns. If we scale up to organizations, where every process and decision play a significant role in business success, data becomes a valuable asset. Collected and properly mined historical data may help make critical decisions for the future, optimize the structure, and even see the business trends.
Hadoop machine learning tools
Big Data is everywhere and, so storing analyzing it becomes a challenge. No human can handle and effectively analyze vast amounts of data. This is where machine learning and distributed storage comes in handy. Hadoop machine learning is an excellent concept for dealing with large amounts of data. The Apache-based Hadoop platform is based on open source tools and utilities that use a network of lots of computers to store and process large amounts of data more efficiently.
Hadoop machine learning has joined the concept of different tools. Hadoop is a platform for storing, running, and processing Big Data. It is a distributed network of multiple computers, clusters that can be accessed and administered using master nodes –servers that link used to the network. Hadoop provides a software framework for distributed storage and processing. The structure allows implementing different data processing and machine learning algorithms that enable clustering, classification, trend analysis, pattern recognition, and other knowledge extraction.
An increasing need for rapid decision-making is a crucial value of Hadoop machine learning. Both Hadoop and machine learning are in demand as it provides fast tools for Big Data processing and mining.
Why use Hadoop?
Hadoop is an excellent tool in the right hands. It may help organizations collect a massive amount of data, which can be analyzed by statistical, machine learning, and even deep-learning tools. For instance, in the financial sector, a Big Data analysis may help detect frauds, evaluate credit scoring, generate new pricing strategies, and create real-time targeted ads on websites. Hadoop is a set of tools that require specialization in IT skills. Different requirements and tools may need a broad spectrum of skills to generate value for the business. This is a job for data scientists who have to learn new skills continually depending on organization needs. Hadoop machine learning may be a great tool in any organization, but only if there are enough skills for extracting value from stored data. Mastering Hadoop and running the right machine learning tools usually require a broad spectrum of specialized skills, and sometimes a team with different specialties is capable of running it effectively.
Once mastered, Big Data analysis with Hadoop machine learning collection gives an advantage of processing large volumes of various types and complexity of data with high speed.