Mastering analytics on Hadoop machine learning through programming not only requires a very broad yet specialized skill set, but also means repetitively solving many tasks that are a necessity of the technology rather than part of the actual analytics initiative.
This makes creating predictive analytics on Hadoop a challenging and expensive task. You will discover more if you keep reading.
Table of Contents
What is Hadoop?
It takes a wide range of specialized IT and analytics skills to use Hadoop, which is a collection of technologies and open source initiatives that creates an ecosystem for storage and processing. Organizations spend their time on the architecture rather than generating business value because integrating these various Hadoop technologies is frequently difficult and time-consuming.
Instead of doing actual data science, data scientists spend the majority of their time learning the numerous skills needed to extract value from the Hadoop stack.
HDFS (file system), Hive (data access and manipulation), Spark (parallel job management), Yarn (job scheduling), and other software programs make up the Hadoop ecosystem. In addition to the more than 20 integrated packages like those listed above that are present in all of the major Hadoop distributions, there are dozens more.
Simply keeping track of these packages requires a lot of work; learning the Hadoop ecosystem requires time and specialized knowledge.
Why is Hadoop Important?
For businesses hoping to use data science to their advantage, Hadoop holds out a lot of promise.
Hadoop enables businesses to gather enormous amounts of data that can then be used to derive insights with enormous business value for use cases like fraud detection, sentiment analysis, risk assessment, predictive maintenance, churn analysis, user segmentation, and many others.
Gaining the insights is challenging because Hadoop deployment can be incredibly complicated and time-consuming.
Final Words on Hadoop Machine Learning
The promise of computer scientists when they first developed computer software programming was machine learning.
Natural language processing, search engines, recommendation engines, bio-informatics, image processing, text analytics, and many other applications are heavily reliant on machine learning in the medical field.
Read More:
- GPU Machine Learning
- SAP Machine Learning
- Stacking Machine Learning
- Bayesian Machine Learning
- Regularization In Machine Learning
FAQs
Is Hadoop Used in Machine Learning?
Hadoop is used for some advanced level of analytics, which includes Machine Learning and data mining.
Why Hadoop is Used in Machine Learning?
Organizations can gather enormous amounts of data using Hadoop, which can then be used to derive insights with enormous business value for use cases like fraud detection, sentiment analysis, risk assessment, predictive maintenance, churn analysis, user segmentation, and many others.
Which Component is Used for Machine Learning in Hadoop?
Mahout was developed to implement distributed algorithmic learning techniques. It can store and process large amounts of data using straightforward programming models in a distributed setting across a cluster.
