Big Data Vs Hadoop Vs Data Science

Big Data Vs Hadoop Vs Data Science

The Three Pillars Of Modern Technology

 

These three terms have been doing the tech rounds now for a long time, and most of us think that they are quite similar to each other. However, therein lies the basic difference between these emerging platforms.

Let us understand these platforms better to acknowledge the essential differences and their usage.

 

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Apache Hadoop

Hadoop is an Apache open source framework written in java that allows distributed processing of large datasets across clusters of computers using simple programming models. A Hadoop frame-worked application works in an environment that provides distributed storage and computation across clusters of computers. Hadoop is designed to scale up from single server to thousands of machines, each offering local computation and storage.

 

Big Data

Big data means really a big data, it is a collection of large datasets that cannot be processed using traditional computing techniques. Big data is not merely a data, rather it has become a complete subject, which involves various tools, techniques and frameworks.

 

Data Science

Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems.

At the core is data. Piles of raw information, streaming in and stored in enterprise data warehouses. Much to learn by mining it. Advanced capabilities we can build with it. Data science is ultimately about using this data in creative ways to generate business value.

 

Understanding Big Data

 

Big Data is a huge collection of data sets that can’t be store in a traditional system.

Big data is a complex sets of data. It’s size can be vary up to peta-bytes.

According to Gartner – Big data is huge-volume, fast-velocity, and different variety information assets that demand innovative platform for enhanced insights and decision making.

A Revolution, authors explain it as – Big Data is a way to solve all the unsolved problems related to data management and handling, an earlier industry was used to live with such problems. With Big data analytics, you can also unlock hidden patterns and know the 360-degree view of customers and better understand their needs.

Big data gets generated in multi terabyte quantities. It changes fast and comes in varieties of forms that are difficult to manage and process using RDBMS or other traditional technologies. Big Data solutions provide the tools, methodologies, and technologies that are used to capture, store, search & analyze the data in seconds to find relationships and insights for innovation and competitive gain that were previously unavailable.

80% of the data getting generated today is unstructured and cannot be handled by our traditional technologies. Earlier, an amount of data generated was not that high. We kept archiving the data as there was just need of historical analysis of data. But today data generation is in petabytes that it is not possible to archive the data again and again and retrieve it again when needed as Data scientists need to play with data now and then for predictive analysis unlike historical as used to be done with traditional.

 

Understanding Hadoop

 

Hadoop is an open source, Scalable, and Fault tolerant framework written in Java. It efficiently processes large volumes of data on a cluster of commodity hardware. Hadoop is not only a storage system but is a platform for large data storage as well as processing.

It provides an efficient framework for running jobs on multiple nodes of clusters. Cluster means a group of systems connected via LAN. Apache Hadoop provides parallel processing of data as it works on multiple machines simultaneously.

 

What is Data Science?

Data Science is a field that encompasses related to data cleansing, preparation, and analysis. Data science is an umbrella term in which many scientific methods apply. For example mathematics, statistics, and many other tools scientists apply to data sets. Scientist applies the tools to extract knowledge from data.

It is a tool to tackle Big Data. And then extract information from it. First Data scientist gathers data sets from multi disciplines and compiles it. After that, apply machine learning, predictive and sentiment analysis. Then sharpen it to a point where he can derive something. At last, he extracts the useful information from it.

Data scientist understands data from a business point of view.His work is to give the most accurate prediction. He takes charge of giving his predictions. The prediction of data scientist is very accurate. It prevents a businessman from future loss.

 

Although, these three tech platforms are related, but there is a major difference between them. Understanding them clearly can help us exploit and appreciate them better.