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- Title: Engineering of Big Data Processing
- Author(s) Piotr Fulmański
- Publisher: Fulmanski (2022)
- Hardcover/Paperback: N/A
- eBook: PDF
- Language: English
- ASIN: N/A
- ISBN-10: N/A
- ISBN-13: N/A
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This book is addressed to all the people who want to understand how Big Data differs from Data and why they should be treated different way. It may be good both for someone with no computer scientist background and for those who have some IT experience but want to put in correct order the whole dispersed knowledge one may have.
About the Authors- N/A
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Big Data Processing with Apache Spark (Srini Penchikala)
Learn about the Apache Spark framework and develop Spark programs for use cases in big-data analysis. It covers all the libraries that are part of Spark ecosystem, which includes Spark Core, Spark SQL, Spark Streaming, Spark MLlib, and Spark GraphX.
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Algorithms for Big Data (Hannah Bast, et al)
This open access book surveys the progress in addressing selected challenges related to the growth of big data in combination with increasingly complicated hardware. Tackles problems such as transportation systems, energy supply, medicine.
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Engineering Agile Big-Data Systems (Kevin Feeney, et al)
This book outlines an approach to dealing with problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals.
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Knowledge Graphs and Big Data Processing (Valentina Janev, et al)
Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions.
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Big Data in Context: Legal, Social and Technological Insights
This book sheds new light on a selection of big data scenarios from an interdisciplinary perspective. it provides a comprehensive overview of and introduction to the emerging challenges regarding big data.
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Modelling and Simulation for Big Data Applications
Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations.
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The Promise and Peril of Big Data (David Bollier)
This book explores the positive aspects and the social perils that arise when the ever-rising floods of data being generated by mobile networking, cloud computing and other new technologies meets continued innovations in advanced correlation techniques.
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Understanding Big Data: Analytics for Hadoop and Streaming Data
In this book, the three defining characteristics of Big Data - volume, variety, and velocity, are discussed. Industry use cases are also included in this practical guide, to deliver a robust, secure, highly available, enterprise-class Big Data platform.
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Kafka: The Definitive Guide: Real-Time Data and Stream Processing
Through detailed examples, you'll learn Kafka's design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer.
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Designing Event-Driven Systems (Ben Stopford)
Concepts and Patterns for Streaming Services with Apache Kafka: this book explains how service-based architectures and stream processing tools such as Apache Kafka can help you build business-critical systems.
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Making Sense of Stream Processing: Behind Apache Kafka
This book shows you how stream processing can make your data storage and processing systems more flexible and less complex. It explains how these projects can help you reorient your database architecture around streams and materialized views.
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The Internals of Apache Spark (Jacek Laskowski)
This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala.
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The Data Engineer's Guide to Apache Spark (Databricks)
This book is for data engineers looking to leverage the immense growth of Apache Spark to build faster and more reliable data pipelines. It leverages Spark's amazing speed, scalability, simplicity, and versatility to build practical Big Data solutions.
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