Titlebook: Big Data Infrastructure Technologies for Data Analytics; Scaling Data Science Yuri Demchenko,Juan J. Cuadrado-Gallego,Marharyta Book 2024

接合

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Cloud and Big Data Service Providers and Platforms,r cloud and Big Data service providers, including Amazon Web Services (AWS), Microsoft Azure Cloud (Azure), and Google Cloud Platform (GCP), to understand how the main Cloud Computing properties are implemented and what services are offered..Knowledge of the cloud and Big Data services market is imp

STELL

Big Data Algorithms, MapReduce and Hadoop ecosystem,will look at the MapReduce algorithm for parallel processing of large amounts of data that provides a basis for many other algorithms and applications working with Big Data..The MapReduce programming model and its implementation in Hadoop were invented to address limitations of the traditional high-

NIP

Data Structures for Big Data, Modern Big Data SQL and NoSQL Databases,cludes discussion the ACID properties of the SQL databases, BASE properties of NoSQL databases, and CAP Theorem for distributed systems applied to NoSQL databases and their classification. The chapter describes examples of the NoSQL databases capable of processing Big Data.

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Research Data Management, metadata tools, and registries. Current research data management is built around FAIR data principles: research data must be Findable, Accessible, Interoperable, Reusable. The chapter also reviews Open Science and Open Access principles..Research Data Management has many specifics compared to enter

calorie

Finding Data on the Web, Data Sets, Web Scraping, Web API, dataset collections, data dumps, Web API, and the web itself. Familiarity with this topic and the ability to effectively collect data from the web, also called webscraping, is a demanded competence and skill in Data Science and Big Data job vacancies. Collecting data from external sources and from

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Data Science Projects Management, DataOps, MLOPs, production environment. We discuss the importance of using research methods for effectively planning and managing Data Science projects. We also explain importance of applying best practices in DevOps for software development to Data Science projects that defined as DataOps, primarily focused on da

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