In-Memory Analytics with Apache Arrow

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Release : 2022-06-24
Genre : Computers
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Book Rating : 430/5 ( reviews)

In-Memory Analytics with Apache Arrow - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook In-Memory Analytics with Apache Arrow write by Matthew Topol. This book was released on 2022-06-24. In-Memory Analytics with Apache Arrow available in PDF, EPUB and Kindle. Process tabular data and build high-performance query engines on modern CPUs and GPUs using Apache Arrow, a standardized language-independent memory format, for optimal performance Key Features • Learn about Apache Arrow's data types and interoperability with pandas and Parquet • Work with Apache Arrow Flight RPC, Compute, and Dataset APIs to produce and consume tabular data • Reviewed, contributed, and supported by Dremio, the co-creator of Apache Arrow Book Description Apache Arrow is designed to accelerate analytics and allow the exchange of data across big data systems easily. In-Memory Analytics with Apache Arrow begins with a quick overview of the Apache Arrow format, before moving on to helping you to understand Arrow's versatility and benefits as you walk through a variety of real-world use cases. You'll cover key tasks such as enhancing data science workflows with Arrow, using Arrow and Apache Parquet with Apache Spark and Jupyter for better performance and hassle-free data translation, as well as working with Perspective, an open source interactive graphical and tabular analysis tool for browsers. As you advance, you'll explore the different data interchange and storage formats and become well-versed with the relationships between Arrow, Parquet, Feather, Protobuf, Flatbuffers, JSON, and CSV. In addition to understanding the basic structure of the Arrow Flight and Flight SQL protocols, you'll learn about Dremio's usage of Apache Arrow to enhance SQL analytics and discover how Arrow can be used in web-based browser apps. Finally, you'll get to grips with the upcoming features of Arrow to help you stay ahead of the curve. By the end of this book, you will have all the building blocks to create useful, efficient, and powerful analytical services and utilities with Apache Arrow. What you will learn • Use Apache Arrow libraries to access data files both locally and in the cloud • Understand the zero-copy elements of the Apache Arrow format • Improve read performance by memory-mapping files with Apache Arrow • Produce or consume Apache Arrow data efficiently using a C API • Use the Apache Arrow Compute APIs to perform complex operations • Create Arrow Flight servers and clients for transferring data quickly • Build the Arrow libraries locally and contribute back to the community Who this book is for This book is for developers, data analysts, and data scientists looking to explore the capabilities of Apache Arrow from the ground up. This book will also be useful for any engineers who are working on building utilities for data analytics and query engines, or otherwise working with tabular data, regardless of the programming language. Some familiarity with basic concepts of data analysis will help you to get the most out of this book but isn't required. Code examples are provided in the C++, Go, and Python programming languages.

Mastering Spark with R

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Release : 2019-10-07
Genre : Computers
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Book Rating : 329/5 ( reviews)

Mastering Spark with R - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Mastering Spark with R write by Javier Luraschi. This book was released on 2019-10-07. Mastering Spark with R available in PDF, EPUB and Kindle. If you’re like most R users, you have deep knowledge and love for statistics. But as your organization continues to collect huge amounts of data, adding tools such as Apache Spark makes a lot of sense. With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems. Authors Javier Luraschi, Kevin Kuo, and Edgar Ruiz show you how to use R with Spark to solve different data analysis problems. This book covers relevant data science topics, cluster computing, and issues that should interest even the most advanced users. Analyze, explore, transform, and visualize data in Apache Spark with R Create statistical models to extract information and predict outcomes; automate the process in production-ready workflows Perform analysis and modeling across many machines using distributed computing techniques Use large-scale data from multiple sources and different formats with ease from within Spark Learn about alternative modeling frameworks for graph processing, geospatial analysis, and genomics at scale Dive into advanced topics including custom transformations, real-time data processing, and creating custom Spark extensions

Disruptive Analytics

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Release : 2016-08-27
Genre : Computers
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Book Rating : 114/5 ( reviews)

Disruptive Analytics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Disruptive Analytics write by Thomas W. Dinsmore. This book was released on 2016-08-27. Disruptive Analytics available in PDF, EPUB and Kindle. Learn all you need to know about seven key innovations disrupting business analytics today. These innovations—the open source business model, cloud analytics, the Hadoop ecosystem, Spark and in-memory analytics, streaming analytics, Deep Learning, and self-service analytics—are radically changing how businesses use data for competitive advantage. Taken together, they are disrupting the business analytics value chain, creating new opportunities. Enterprises who seize the opportunity will thrive and prosper, while others struggle and decline: disrupt or be disrupted. Disruptive Business Analytics provides strategies to profit from disruption. It shows you how to organize for insight, build and provision an open source stack, how to practice lean data warehousing, and how to assimilate disruptive innovations into an organization. Through a short history of business analytics and a detailed survey of products and services, analytics authority Thomas W. Dinsmore provides a practical explanation of the most compelling innovations available today. What You'll Learn Discover how the open source business model works and how to make it work for you See how cloud computing completely changes the economics of analytics Harness the power of Hadoop and its ecosystem Find out why Apache Spark is everywhere Discover the potential of streaming and real-time analytics Learn what Deep Learning can do and why it matters See how self-service analytics can change the way organizations do business Who This Book Is For Corporate actors at all levels of responsibility for analytics: analysts, CIOs, CTOs, strategic decision makers, managers, systems architects, technical marketers, product developers, IT personnel, and consultants.

Practical Machine Learning with Spark

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Release : 2022-04-28
Genre : Computers
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Book Rating : 083/5 ( reviews)

Practical Machine Learning with Spark - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Practical Machine Learning with Spark write by Gourav Gupta. This book was released on 2022-04-28. Practical Machine Learning with Spark available in PDF, EPUB and Kindle. Explore the cosmic secrets of Distributed Processing for Deep Learning applications KEY FEATURES ● In-depth practical demonstration of ML/DL concepts using Distributed Framework. ● Covers graphical illustrations and visual explanations for ML/DL pipelines. ● Includes live codebase for each of NLP, computer vision and machine learning applications. DESCRIPTION This book provides the reader with an up-to-date explanation of Machine Learning and an in-depth, comprehensive, and straightforward understanding of the architectural techniques used to evaluate and anticipate the futuristic insights of data using Apache Spark. The book walks readers by setting up Hadoop and Spark installations on-premises, Docker, and AWS. Readers will learn about Spark MLib and how to utilize it in supervised and unsupervised machine learning scenarios. With the help of Spark, some of the most prominent technologies, such as natural language processing and computer vision, are evaluated and demonstrated in a realistic setting. Using the capabilities of Apache Spark, this book discusses the fundamental components that underlie each of these natural language processing, computer vision, and machine learning technologies, as well as how you can incorporate these technologies into your business processes. Towards the end of the book, readers will learn about several deep learning frameworks, such as TensorFlow and PyTorch. Readers will also learn to execute distributed processing of deep learning problems using the Spark programming language WHAT YOU WILL LEARN ●Learn how to get started with machine learning projects using Spark. ● Witness how to use Spark MLib's design for machine learning and deep learning operations. ● Use Spark in tasks involving NLP, unsupervised learning, and computer vision. ● Experiment with Spark in a cloud environment and with AI pipeline workflows. ● Run deep learning applications on a distributed network. WHO THIS BOOK IS FOR This book is valuable for data engineers, machine learning engineers, data scientists, data architects, business analysts, and technical consultants worldwide. It would be beneficial to have some familiarity with the fundamentals of Hadoop and Python. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Apache Spark Environment Setup and Configuration 3. Apache Spark 4. Apache Spark MLlib 5. Supervised Learning with Spark 6. Un-Supervised Learning with Apache Spark 7. Natural Language Processing with Apache Spark 8. Recommendation Engine with Distributed Framework 9. Deep Learning with Spark 10. Computer Vision with Apache Spark

New Trends and Challenges in Open Data

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Release : 2023-10-04
Genre : Computers
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Book Rating : 92X/5 ( reviews)

New Trends and Challenges in Open Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook New Trends and Challenges in Open Data write by Vijayalakshmi Kakulapati. This book was released on 2023-10-04. New Trends and Challenges in Open Data available in PDF, EPUB and Kindle. Data is often open to all users and sharers. Governments provide data on publicly available websites and this data may pertain to specific regions or be aggregate data on national or international issues. Data that is in the public domain but not in a machine-readable format is considered public data and may only be accessible via a right-of-access request. Maintaining accuracy and management is a major obstacle when it comes to data systems and solutions. Data governance describes the rules, procedures, and responsibilities that outline the data's acquisition, storage, retrieval and use. Data security and privacy refer to safeguards put in place to protect information from being seen, copied, distributed, altered, or destroyed without permission. Data integration and interoperability involve combining and exchanging data from many sources, systems, and formats, as well as facilitating data sharing and collaboration across various platforms, apps, and organizations. Defining data standards, implementing data quality checks, assigning data ownership and responsibility, and monitoring data performance and utilization are all important steps toward resolving the data quality problem. This book contains two sections. “Trends and Challenges of Open Data” and “Case Studies”. Each section contains three chapters.