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Apache Hadoop is quickly becoming the technology of choice for organizations investing in big data, powering their next generation data architecture. With Hadoop serving as both a scalable data platform and computational engine, data science is re-emerging as a centerpiece of enterprise innovation, with applied data solutions such as online product recommendation, automated fraud detection and customer sentiment analysis.
In this article, we provide an overview of data science and how to take advantage of Hadoop for large scale data science projects.
Hadoop is a boon to data scientists. Let’s look at how Hadoop helps in boosting productivity of Data Scientists. Hadoop has a unique capability where all the data can be stored and retrieved from a single place. Through this manner, the following can be achieved:
Reason#1: Explore Large Datasets
The First and foremost reason being one can Explore Large Datasets directly with Hadoop by integrating Hadoop in the Data Analysis flow.
This is achieved by utilizing simple statistics like:
One can also use Ad-hoc Sampling /filtering to achieve Random: with or without Replacement, Sample by unique Key and K-fold Cross-validation.
Reason#2: Ability to Mine Large Datasets
Learning algorithms with large datasets has its own challenges. The challenges being:
When using Hadoop one can perform functions like distribute data across nodes in the Hadoop cluster and implement a distributed/parallel algorithm. For recommendations, one can Alternate Least Square algorithm and for clustering K-Means can be used.
Reason#3: Large Scale Data Preparation
We all know 80% of Data Science Work involves ‘Data Preparation’. Hadoop is ideal for batch preparation and cleanup of large Datasets.
Reason#4: Accelerate Data Driven Innovation:
Traditional data architectures have barriers to speed. RDBMS uses schema on Write and therefore change is expensive. It’s also a high barrier for data-driven innovation.
Hadoop uses “Schema on Read” which means faster time to Innovation and thus adds a low barrier on data driven innovation.
Therefore to summarize the four main reasons why we need Hadoop with Data Science would be:
We therefore see that Organizations can leverage Hadoop to their advantage for mining data and gathering useful results from it.
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