Hadoop and Data Science- A Perfect Partnership
Apache Hadoop is quickly becoming the niche technology option for companies wanting to invest in big data and empowering their next generation big data architecture. With Hadoop now emerging as the scalable data platform as well as computational engine, data science has now regained the center stage of business innovations with applied data solutions including online product recommendations, automated fraud detections, consumer sentiment analysis and the like. This article would brief you on Data Science and would make you understand how Hadoop benefits large scale Data Science projects.
Understanding How Useful is Hadoop to Data Science
Hadoop is a virtual miracle to Data Science as it comes with the unique facility of both storing as well as retrieving data from a single source that assists in:
The capability to store data in its RAW format
A data Silo convergence
Data scientists will now find novel ways of combined data assets
The Significance of Hadoop’s Power
Increasingly cost effective- Hadoop helps in greatly reducing both the time and cost of building large scale data sets
Computation is co designed to work with Data- computation and data system are so designed to work together.
Hadoop is designed for one write and multiple reads- there are no random Writes and is so Optimized for maximum seek on hard drives
Why Should Hadoop Combine with Data Science
To Explore Large Datasets
This is primarily done so that one would be able to Explore Large Datasets directly with Hadoop by integrating Hadoop within the Data Analysis flow. This can be accomplished by with the help of simple statistics which would be:
- Pre-processing grep, regex
Learning Algorithms with huge datasets comes with its own challenges that would include:
- Data not fixing into the memory
- Learning taking a far longer time
A large Scale preparation
Hadoop works like magic for both batch preparations as well as a thorough clean- up of large Datasets.
Augmenting the Pace for Data Driven Innovations
Traditional architectures have a block to speed as RDBMS use Schema on Write which makes change an expensive proposition. It is here that Hadoop uses Schema on Read that gives a faster time to Innovation and thus a lower barrier on data driven innovation.
Let us now encapsulate the 4 primary reasons why we need Hadoop with Big Data:
- Mine larger Databases
- Data Exploration with full datasets
- Pre-processing at scale
- A Faster Driven Cycle
It is now an understood fact that organizations can leverage Hadoop to suit their business propositions to mine their data as well as gain useful insights out of it.