Big Data With Hadoop And Spark Analyze Massive Datasets With Apache Hadoop Spark And Nosql Thompson Carter Isbn 13 979 8346251026
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Big Data with Hadoop and Spark
Author: Thompson Carter
language: en
Publisher: Independently Published
Release Date: 2024-11-11
Dive deep into the world of Big Data with Big Data with Hadoop and Spark. This guide demystifies complex topics, from foundational Hadoop architecture and HDFS to real-time processing with Apache Spark. Explore MapReduce, YARN, and Spark's Resilient Distributed Datasets (RDDs), and gain hands-on knowledge with Spark Streaming, MLlib, and GraphX. Designed for data professionals, engineers, and tech enthusiasts, this book covers data security, troubleshooting, performance optimization, and Big Data strategy, giving readers the skills to handle high-velocity data and transform it into actionable insights. With real-world applications and case studies from retail, social media, healthcare, and finance, this book is the ultimate field guide to Big Data mastery.
Practical Big Data Analytics
Author: Nataraj Dasgupta
language: en
Publisher: Packt Publishing Ltd
Release Date: 2018-01-15
Get command of your organizational Big Data using the power of data science and analytics Key Features A perfect companion to boost your Big Data storing, processing, analyzing skills to help you take informed business decisions Work with the best tools such as Apache Hadoop, R, Python, and Spark for NoSQL platforms to perform massive online analyses Get expert tips on statistical inference, machine learning, mathematical modeling, and data visualization for Big Data Book Description Big Data analytics relates to the strategies used by organizations to collect, organize and analyze large amounts of data to uncover valuable business insights that otherwise cannot be analyzed through traditional systems. Crafting an enterprise-scale cost-efficient Big Data and machine learning solution to uncover insights and value from your organization's data is a challenge. Today, with hundreds of new Big Data systems, machine learning packages and BI Tools, selecting the right combination of technologies is an even greater challenge. This book will help you do that. With the help of this guide, you will be able to bridge the gap between the theoretical world of technology with the practical ground reality of building corporate Big Data and data science platforms. You will get hands-on exposure to Hadoop and Spark, build machine learning dashboards using R and R Shiny, create web-based apps using NoSQL databases such as MongoDB and even learn how to write R code for neural networks. By the end of the book, you will have a very clear and concrete understanding of what Big Data analytics means, how it drives revenues for organizations, and how you can develop your own Big Data analytics solution using different tools and methods articulated in this book. What you will learn - Get a 360-degree view into the world of Big Data, data science and machine learning - Broad range of technical and business Big Data analytics topics that caters to the interests of the technical experts as well as corporate IT executives - Get hands-on experience with industry-standard Big Data and machine learning tools such as Hadoop, Spark, MongoDB, KDB+ and R - Create production-grade machine learning BI Dashboards using R and R Shiny with step-by-step instructions - Learn how to combine open-source Big Data, machine learning and BI Tools to create low-cost business analytics applications - Understand corporate strategies for successful Big Data and data science projects - Go beyond general-purpose analytics to develop cutting-edge Big Data applications using emerging technologies Who this book is for The book is intended for existing and aspiring Big Data professionals who wish to become the go-to person in their organization when it comes to Big Data architecture, analytics, and governance. While no prior knowledge of Big Data or related technologies is assumed, it will be helpful to have some programming experience.
Big Data Made Simple
Author: THOMPSON. CARTER
language: en
Publisher: Independently Published
Release Date: 2025-02-18
Big Data Made Simple: Understanding Hadoop, Spark, and Beyond Unlock the world of Big Data with Big Data Made Simple, the ultimate guide to understanding and utilizing Hadoop, Spark, and other powerful technologies in the big data ecosystem. This book is designed for data engineers, analysts, and developers who want to harness the full potential of big data processing frameworks to analyze massive datasets quickly and efficiently. Whether you're a beginner exploring big data concepts or an experienced professional looking to deepen your expertise, this book will help you navigate and leverage big data technologies for scalable, high-performance data processing. Learn how to process and analyze large-scale datasets using Hadoop, Spark, and other tools, and understand how they fit into the overall big data landscape. With step-by-step tutorials, real-world case studies, and practical tips, this book equips you with the knowledge and skills needed to effectively work with big data platforms. What You'll Learn: ✅ Introduction to Big Data - Understand the foundational concepts of big data and why it requires specialized frameworks like Hadoop and Spark for processing. ✅ Hadoop Fundamentals - Learn about the Hadoop ecosystem, including HDFS (Hadoop Distributed File System), MapReduce, and YARN, and how they enable the processing of large datasets. ✅ Processing Data with Hadoop - Explore how to create, manage, and optimize MapReduce jobs for batch processing and analyzing big data. ✅ Spark Overview - Understand Apache Spark, its architecture, and how it provides fast, in-memory data processing for both batch and real-time workloads. ✅ Distributed Computing with Spark - Learn how to build efficient data processing workflows using Spark RDDs and DataFrames, and scale them across a cluster. ✅ Advanced Spark Techniques - Delve into advanced Spark features like Spark Streaming, MLlib for machine learning, and GraphX for graph processing. ✅ Data Warehousing with Hive and HBase - Use Apache Hive for querying large datasets in Hadoop, and HBase for real-time, random access to big data. ✅ Real-Time Data Processing - Learn how to process streaming data in real-time using Apache Kafka and Spark Streaming for faster insights and decision-making. ✅ Data Security in Big Data - Implement security measures like data encryption, authentication, and access control for Hadoop and Spark clusters. ✅ Optimizing Big Data Pipelines - Explore strategies for optimizing big data jobs for performance and scalability across distributed systems. ✅ Integrating Big Data with Machine Learning - Leverage big data technologies with machine learning tools for predictive analytics and decision-making. ✅ Case Studies and Industry Applications - Study real-world big data applications in industries like finance, healthcare, and e-commerce. ✅ Future Trends in Big Data - Stay up-to-date with the latest advancements in big data processing and how emerging technologies like AI and edge computing are shaping the future. With clear explanations, hands-on examples, and practical exercises, Big Data Made Simple simplifies complex big data concepts and enables you to confidently work with technologies like Hadoop and Spark to solve real-world data challenges.