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Data science for Business Intelligence with the help of Hadoop With the help of data science, an organization can enhance its business by being data-driven. Data science implementations gives a high return on investment with the help of data insight guidance and development of data product. There are not enough people available for the position of the data scientist because of the lack of the desired skills. This increases the pay of a data scientist. Hence, to achieve the position of a data scientist, a person must possess all the desired skills and qualification. Data science and Business Intelligence Business intelligence analyses the previous data of any company or organization. Meaningful insights are extracted with the help of this data to describe business trends. The source of the previous data can be external or internal. This data is prepared, queried and stored. Then dashboards are created to cater to the business needs. These dashboards answers questions like risk analysis, business problems, and revenue analysis. Business intelligence also studies the impact of events on businesses. This helps the organization to take corrective actions with respect to their business. Data is the more enhanced and forward-looking approach to BI. It explores the previous as well as current data, and using it, data science tries to predict the outcome of certain events. Data science answers openended questions like how and what. Data science idea emerged from business intelligence itself. Business intelligence uses structures data whereas data science uses both structured and unstructured data. Business Intelligence uses statistics and visualization approach, whereas data science uses statistics, machine learning, data mining, graph analysis, and Neuro-linguistic programming approach. Business intelligence focuses on past and present data, whereas data science focuses on present and future data. Tools used by business Intelligence is Microsoft, R, and Pentaho. Data science uses tools such as RapidMiner, R, Weka. Hadoop for data science Hadoop is an open source framework that uses a simple programming model to process large data sets distributes across a set of computers. It helps to scale from a single server to several distributed servers. It is based on the concept of clusters. Clusters of servers are created which processes and stores data across different machines. Hadoop came out from a distributed storage software which is from the opensource search engine, Nutch. The basic idea was to distribute data across servers so that the data is fetched faster. Today, Hadoop is maintained and managed by Apache Software Foundation. Features and advantages of Hadoop include scalability, cost-effective, flexible, fast, and resilient to failure. Elements of Hadoop Ecosystem Hadoop ecosystem has many elements that help in different phases and applications of data science in many ways. Some of these elements are: Pig It is a high-level data flow language for parallel computation. It helps to perform data extraction, transformation, and basic analysis. Hive It is a data warehouse. It provides data summarization. It also facilitates adhoc querying.


HBase It is a distributed and scalable database that stores data in a structured or relational form. Ambari It is a web-based user interface for managing and monitoring Hadoop services and components. Resource box If you are keen about the Hadoop software framework and interested in learning the implementation of the major elements of the Hadoop ecosystem, data science certification is the best option for you. It covers the use of Hadoop in data science field as Hadoop is one of the essential tools for data science.

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Data science and its different processes  

Data science is a field which uses different algorithms and methods to extract information and insights from different structured and unstru...

Data science and its different processes  

Data science is a field which uses different algorithms and methods to extract information and insights from different structured and unstru...

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