A comprehensive practical guide that walks you through the multiple stages of data management in enterprise and gives you numerous design patterns with appropriate code examples to solve frequent problems in each of these stages. The chapters are organized to mimick the sequential data flow evidenced in Analytics platforms, but they can also be read independently to solve a particular group of problems in the Big Data life cycle.
If you are an experienced developer who is already familiar with Pig and is looking for a use case standpoint where they can relate to the problems of data ingestion, profiling, cleansing, transforming, and egressing data encountered in the enterprises. Knowledge of Hadoop and Pig is necessary for readers to grasp the intricacies of Pig design patterns better.
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Pradeep Pasupuleti has over 17 years of experience in architecting and developing distributed and real-time data-driven systems. Currently, he focuses on developing robust data platforms and data products that are fuelled by scalable machine-learning algorithms, and delivering value to customers in addressing business problems by applying his deep technical insights.Pradeep founded Datatma expressly to humanize Big Data, simplify it, and unravel new value on a previously unimaginable scale in economy and scope. He has created COE (Centers of Excellence) to provide quick wins with data products that analyze high-dimensional multistructured data using scalable natural language processing and deep learning techniques. He has performed roles in technology consulting and advising Fortune 500 companies.Beulah Salome Purra has over 11 years of experience and specializes in building large-scale distributed systems. Her core expertise lies in working on Big Data Analytics. In her current role at ATMECS, her focus is on building robust and scalable data products that extract value from the organization's huge data assets.
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