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 DescriptionApache 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.
Getting Started with DuckDB
Getting Started with DuckDB - 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 Getting Started with DuckDB write by Simon Aubury. This book was released on 2024-06-24. Getting Started with DuckDB available in PDF, EPUB and Kindle. Analyze and transform data efficiently with DuckDB, a versatile, modern, in-process SQL database Key Features Use DuckDB to rapidly load, transform, and query data across a range of sources and formats Gain practical experience using SQL, Python, and R to effectively analyze data Learn how open source tools and cloud services in the broader data ecosystem complement DuckDB’s versatile capabilities Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDuckDB is a fast in-process analytical database. Its ease of use, versatile feature set, and powerful analytical capabilities make DuckDB a valuable addition to the data practitioner’s toolkit. Getting Started with DuckDB offers a practical overview of DuckDB’s fundamentals and guidance for effectively using its powerful capabilities. Through extensive hands-on examples, you’ll learn how to use DuckDB to load, transform, and query a variety of data sources and formats, including CSV, JSON, and Parquet files, semi-structured data, remotely-hosted files, and external databases. You'll also find out how to leverage DuckDB's performance optimizations and friendly SQL enhancements. You'll explore how to use DuckDB’s extensions for specialized applications, such as geospatial analysis and text search over document collections. In addition to working through examples in SQL, Python, and R, you’ll also dive into using DuckDB for analyzing public datasets and discover the wider ecosystem of open-source tools and cloud services that supercharge DuckDB-powered workflows and applications. Whether you’re a seasoned data practitioner or new to working with analytical data, this book will rapidly get you up to speed with DuckDB’s versatile and powerful capabilities, enabling you to apply them in your analytical workflows and projects.What you will learn Understand the properties and applications of a columnar in-process database Use SQL to load, transform, and query a range of data formats Discover DuckDB's rich extensions and learn how to apply them Use nested data types to model semi-structured data and extract and model JSON data Integrate DuckDB into your Python and R analytical workflows Effectively leverage DuckDB's convenient SQL enhancements Explore the wider ecosystem and pathways for building DuckDB-powered data applications Who this book is for If you’re interested in expanding your analytical toolkit, this book is for you. It will be particularly valuable for data analysts wanting to rapidly explore and query complex data, data and software engineers looking for a lean and versatile data processing tool, along with data scientists needing a scalable data manipulation library that integrates seamlessly with Python and R. You will get the most from this book if you have some familiarity with SQL and foundational database concepts, as well as exposure to a programming language such as Python or R.
Disruptive Analytics
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.
Kickstart Modern Android Development with Jetpack and Kotlin
Kickstart Modern Android Development with Jetpack and Kotlin - 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 Kickstart Modern Android Development with Jetpack and Kotlin write by Catalin Ghita. This book was released on 2022-05-24. Kickstart Modern Android Development with Jetpack and Kotlin available in PDF, EPUB and Kindle. Explore modern Android development in Kotlin 1.6.10 with this condensed hands-on guide to building reliable apps using libraries such as Compose, ViewModel, Hilt, Retrofit, Flow, and more Key Features Explore Jetpack libraries and other modern technologies for Android development Improve the architectural design of your Android apps Enhance the quality of your Android projects’ code bases and applications using the latest libraries Book DescriptionWith Jetpack libraries, you can build and design high-quality, robust Android apps that have an improved architecture and work consistently across different versions and devices. This book will help you understand how Jetpack allows developers to follow best practices and architectural patterns when building Android apps while also eliminating boilerplate code. Developers working with Android and Kotlin will be able to put their knowledge to work with this condensed practical guide to building apps with the most popular Jetpack libraries, including Jetpack Compose, ViewModel, Hilt, Room, Paging, Lifecycle, and Navigation. You'll get to grips with relevant libraries and architectural patterns, including popular libraries in the Android ecosystem such as Retrofit, Coroutines, and Flow while building modern applications with real-world data. By the end of this Android app development book, you'll have learned how to leverage Jetpack libraries and your knowledge of architectural concepts for building, designing, and testing robust Android applications for various use cases.What you will learn Integrate popular Jetpack libraries such as Compose, ViewModel, Hilt, and Navigation into real Android apps with Kotlin Apply modern app architecture concepts such as MVVM, dependency injection, and clean architecture Explore Android libraries such as Retrofit, Coroutines, and Flow Integrate Compose with the rest of the Jetpack libraries or other popular Android libraries Work with other Jetpack libraries such as Paging and Room while integrating a real REST API that supports pagination Test Compose UI and the application logic through unit tests Who this book is for This book is for junior and intermediate-level Android developers looking to level up their Android development skills to develop high-quality apps using Jetpack libraries and other cutting-edge technologies. Beginners with knowledge of Android development fundamentals will also find this book useful. Familiarity with Kotlin is assumed.
Cleaning Data for Effective Data Science
Cleaning Data for Effective Data Science - 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 Cleaning Data for Effective Data Science write by David Mertz. This book was released on 2021-03-31. Cleaning Data for Effective Data Science available in PDF, EPUB and Kindle. Think about your data intelligently and ask the right questions Key FeaturesMaster data cleaning techniques necessary to perform real-world data science and machine learning tasksSpot common problems with dirty data and develop flexible solutions from first principlesTest and refine your newly acquired skills through detailed exercises at the end of each chapterBook Description Data cleaning is the all-important first step to successful data science, data analysis, and machine learning. If you work with any kind of data, this book is your go-to resource, arming you with the insights and heuristics experienced data scientists had to learn the hard way. In a light-hearted and engaging exploration of different tools, techniques, and datasets real and fictitious, Python veteran David Mertz teaches you the ins and outs of data preparation and the essential questions you should be asking of every piece of data you work with. Using a mixture of Python, R, and common command-line tools, Cleaning Data for Effective Data Science follows the data cleaning pipeline from start to end, focusing on helping you understand the principles underlying each step of the process. You'll look at data ingestion of a vast range of tabular, hierarchical, and other data formats, impute missing values, detect unreliable data and statistical anomalies, and generate synthetic features. The long-form exercises at the end of each chapter let you get hands-on with the skills you've acquired along the way, also providing a valuable resource for academic courses. What you will learnIngest and work with common data formats like JSON, CSV, SQL and NoSQL databases, PDF, and binary serialized data structuresUnderstand how and why we use tools such as pandas, SciPy, scikit-learn, Tidyverse, and BashApply useful rules and heuristics for assessing data quality and detecting bias, like Benford’s law and the 68-95-99.7 ruleIdentify and handle unreliable data and outliers, examining z-score and other statistical propertiesImpute sensible values into missing data and use sampling to fix imbalancesUse dimensionality reduction, quantization, one-hot encoding, and other feature engineering techniques to draw out patterns in your dataWork carefully with time series data, performing de-trending and interpolationWho this book is for This book is designed to benefit software developers, data scientists, aspiring data scientists, teachers, and students who work with data. If you want to improve your rigor in data hygiene or are looking for a refresher, this book is for you. Basic familiarity with statistics, general concepts in machine learning, knowledge of a programming language (Python or R), and some exposure to data science are helpful.