Buy this product. K educators : This link is for individuals purchasing with credit cards or PayPal only. Pearson offers affordable and accessible purchase options to meet the needs of your students. Connect with us to learn more. Prior to Hortonworks, Casey was an architect at Explorys, which was a medical informatics startup spun out of the Cleveland Clinic. Douglas Eadline, PhD, began his career as analytical chemist with an interest in computer methods.
Starting with the first Beowulf how-to document, Doug has written hundreds of articles, white papers, and instructional documents covering many aspects of HPC and Hadoop computing. Prior to starting and editing the popular ClusterMonkey.
He has practical hands-on experience in many aspects of HPC and Apache Hadoop, including hardware and software design, benchmarking, storage, GPU, cloud computing, and parallel computing. We're sorry! We don't recognize your username or password. Drawing on immense experience with Hadoop and big data, three leading experts bring together everything you need: high-level concepts, deep-dive techniques, real-world use cases, practical applications, and hands-on tutorials.
The authors introduce the essentials of data science and the modern Hadoop ecosystem, explaining how Hadoop and Spark have evolved into an effective platform for solving data science problems at scale. In addition to comprehensive application coverage, the authors also provide useful guidance on the important steps of data ingestion, data munging, and visualization. Once the groundwork is in place, the authors focus on specific applications, including machine learning, predictive modeling for sentiment analysis, clustering for document analysis, anomaly detection, and natural language processing NLP.
This guide provides a strong technical foundation for those who want to do practical data science, and also presents business-driven guidance on how to apply Hadoop and Spark to optimize ROI of data science initiatives. Explore big data concepts, platforms, analytics, and their applications using the power of Hadoop 3.
Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples.
You will then move on to learning how to integrate Hadoop with the open source tools, such as Python and R, to analyze and visualize data and perform statistical computing on big data. As you get acquainted with all this, you will explore how to use Hadoop 3 with Apache Spark and Apache Flink for real-time data analytics and stream processing. In addition to this, you will understand how to use Hadoop to build analytics solutions on the cloud and an end-to-end pipeline to perform big data analysis using practical use cases.
By the end of this book, you will be well-versed with the analytical capabilities of the Hadoop ecosystem.
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