Data science is a blend of various algorithms, tools, and machine learning principles that operate with the goal of discovering hidden patterns from raw data. It is used to make decisions and predictions by using prescriptive analysis, predictive causal analysis, and machine learning. It is used to scope out the right questions from the dataset. It is a multidisciplinary field that works at the raw level of data (structured, unstructured, or both) to make predictions, identify patterns and trends, build data models, and create more efficient machine learning algorithms. Data science experts work in the realm of the unknown. Some of the data science techniques are regression analysis, classification analysis, clustering analysis, association analysis, and anomaly detection.
In this article, we will analyze the importance of Hadoop for the field of data science.
A Brief Introduction To Hadoop
In today’s context, data is increasing at an exponential rate. There is a huge demand for processing massive volumes of data. The one such technology responsible for processing large datasets is Hadoop. Hadoop has evolved as a comprehensive data science platform. It is used for storing colossal amounts of data, owing to its notable features such as fault tolerance and scalability. It uses tools such as Pig and Hive to facilitate a comprehensive analytical platform.
Apache Hadoop is an open-source framework that facilitates a network of computers to solve problems requiring massive computational power and datasets. It processes the datasets across clusters of computers with the help of simple programming models. It aids in scaling from single servers to thousands of machines. Though Hadoop is written in Java, it can be programmed in other languages such as C++, Ruby, Python, Perl, etc.
Three Main Components of Hadoop
Let us now understand the three major components of Hadoop.
- Map-Reduce– This component is responsible for high-level data processing. It helps process a large amount of data over the cluster of nodes.
- Hadoop Distributed Filesystem– This is the storage component of Hadoop, which is a collection of master-slave networks. A Hadoop Distributed FileSystem has two daemons such as name node and data node running on the master and slave nodes, respectively.
- YARN– This component is used for resource management and job scheduling. It is difficult to allocate, manage, and release resources in a multi-node cluster. Hadoop YARN helps manage and control these resources efficiently.
Role of Hadoop in Data Science
We will now list down the various areas in which Hadoop plays a significant role in data science.
1. Hadoop for Data Exploration
A data scientist spends 80% of his time in data preparation and data exploration. Hadoop is good at data exploration as it helps data scientists discover the complexities present in data, even if they cannot make sense of it. It allows data scientists to store data as it is, and that is the whole idea of data exploration. It does not need a data scientist to understand the data when they deal with large volumes of data.
2. Hadoop for Data Sampling
It is not possible for a data scientist to build a model by taking just the first 1000 records from the dataset because of the way in which the data is usually written- grouping similar kinds of records together. A data scientist cannot get a good view of what is there in the data as a whole, without sampling the data. Using Hadoop for data sampling gives the data scientist a fair idea of the approaches that might work and those that might not work for data modelling. The cool keyword utility ‘Sample’ of Hadoop lets users down the number of records.
3. Hadoop for Summarization
Summarizing the data as a whole with Hadoop MapReduce will help data scientists get a bird’s eye view of better data building models. Hadoop MapReduce is used for summarization of data. Mappers get the data and reducers summarize the data. Hadoop is also used in a significant part of the data science process, which is data preparation. It is both important and useful for a data scientist to familiarize himself with concepts like Hadoop MapReduce, Hive, and Pig.
Conclusion
Hadoop is a great tool for data science. The easiest way to leverage it for big data and data science is to use it to store information in a structured manner. Using Hadoop as file storage for a relational database management system offers data scientists advantages such as unlimited storage, high performance, and the ease of using fast and familiar tools such as R and SQL.
Learning Hadoop would certainly prove to be useful for a data scientist as it helps speed up the learning process. Hadoop will let data scientists look for novel ways to leverage the big data of organizations.
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