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Big Data Architect???s Handbook

Big Data Architect???s Handbook

By : Akhtar
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Big Data Architect???s Handbook

Big Data Architect???s Handbook

3 (2)
By: Akhtar

Overview of this book

The big data architects are the “masters” of data, and hold high value in today’s market. Handling big data, be it of good or bad quality, is not an easy task. The prime job for any big data architect is to build an end-to-end big data solution that integrates data from different sources and analyzes it to find useful, hidden insights. Big Data Architect’s Handbook takes you through developing a complete, end-to-end big data pipeline, which will lay the foundation for you and provide the necessary knowledge required to be an architect in big data. Right from understanding the design considerations to implementing a solid, efficient, and scalable data pipeline, this book walks you through all the essential aspects of big data. It also gives you an overview of how you can leverage the power of various big data tools such as Apache Hadoop and ElasticSearch in order to bring them together and build an efficient big data solution. By the end of this book, you will be able to build your own design system which integrates, maintains, visualizes, and monitors your data. In addition, you will have a smooth design flow in each process, putting insights in action.
Table of Contents (21 chapters)
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10
Frontend Architecture
17
Data Visualization

Supervised learning


In this type of learning, computers learn from a predefined data set with data labels and features. Its aim is to predict an output based on the given input variables using the defined data point and label from the learned dataset. The most important thing you need in supervised learning is data with labels. By providing data labels, we teach and train our model for accuracy. The more accurate your data is, the closer your prediction will be. Some of the ways of getting data include from a historical source, or by doing experiments.

Now let's take an example of supervised learning. In your mailbox, you have a folder for junk emails; ever wondered how it automatically identifies emails as spam? It is actually based on the trained model, which looks for certain things before marking it as junk, including the source from where the email was generated, the intended audience (whether it is directly targeting the recipient), whether the email body contains marketing or spam...

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Tech Concepts
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Big Data Architect???s Handbook
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