Analiza danych
Geospatial Data Analytics on AWS. Discover how to manage and analyze geospatial data in the cloud
Scott Bateman, Janahan Gnanachandran, Jeff DeMuth
Managing geospatial data and building location-based applications in the cloud can be a daunting task. This comprehensive guide helps you overcome this challenge by presenting the concept of working with geospatial data in the cloud in an easy-to-understand way, along with teaching you how to design and build data lake architecture in AWS for geospatial data.You’ll begin by exploring the use of AWS databases like Redshift and Aurora PostgreSQL for storing and analyzing geospatial data. Next, you’ll leverage services such as DynamoDB and Athena, which offer powerful built-in geospatial functions for indexing and querying geospatial data. The book is filled with practical examples to illustrate the benefits of managing geospatial data in the cloud. As you advance, you’ll discover how to analyze and visualize data using Python and R, and utilize QuickSight to share derived insights. The concluding chapters explore the integration of commonly used platforms like Open Data on AWS, OpenStreetMap, and ArcGIS with AWS to enable you to optimize efficiency and provide a supportive community for continuous learning.By the end of this book, you’ll have the necessary tools and expertise to build and manage your own geospatial data lake on AWS, along with the knowledge needed to tackle geospatial data management challenges and make the most of AWS services.
Abdishakur Hassan, Jayakrishnan Vijayaraghavan
Data scientists, who have access to vast data streams, are a bit myopic when it comes to intrinsic and extrinsic location-based data and are missing out on the intelligence it can provide to their models. This book demonstrates effective techniques for using the power of data science and geospatial intelligence to build effective, intelligent data models that make use of location-based data to give useful predictions and analyses.This book begins with a quick overview of the fundamentals of location-based data and how techniques such as Exploratory Data Analysis can be applied to it. We then delve into spatial operations such as computing distances, areas, extents, centroids, buffer polygons, intersecting geometries, geocoding, and more, which adds additional context to location data. Moving ahead, you will learn how to quickly build and deploy a geo-fencing system using Python. Lastly, you will learn how to leverage geospatial analysis techniques in popular recommendation systems such as collaborative filtering and location-based recommendations, and more.By the end of the book, you will be a rockstar when it comes to performing geospatial analysis with ease.
Tom Hombergs
We would all like to build software architecture that yields adaptable and flexible software with low development costs. But, unreasonable deadlines and shortcuts make it very hard to create such an architecture.Get Your Hands Dirty on Clean Architecture starts with a discussion about the conventional layered architecture style and its disadvantages. It also talks about the advantages of the domain-centric architecture styles of Robert C. Martin's Clean Architecture and Alistair Cockburn's Hexagonal Architecture. Then, the book dives into hands-on chapters that show you how to manifest a hexagonal architecture in actual code. You'll learn in detail about different mapping strategies between the layers of a hexagonal architecture and see how to assemble the architecture elements into an application. The later chapters demonstrate how to enforce architecture boundaries. You'll also learn what shortcuts produce what types of technical debt and how, sometimes, it is a good idea to willingly take on those debts.After reading this book, you'll have all the knowledge you need to create applications using the hexagonal architecture style of web development.
Michael Hsieh
Amazon SageMaker Studio is the first integrated development environment (IDE) for machine learning (ML) and is designed to integrate ML workflows: data preparation, feature engineering, statistical bias detection, automated machine learning (AutoML), training, hosting, ML explainability, monitoring, and MLOps in one environment.In this book, you'll start by exploring the features available in Amazon SageMaker Studio to analyze data, develop ML models, and productionize models to meet your goals. As you progress, you will learn how these features work together to address common challenges when building ML models in production. After that, you'll understand how to effectively scale and operationalize the ML life cycle using SageMaker Studio.By the end of this book, you'll have learned ML best practices regarding Amazon SageMaker Studio, as well as being able to improve productivity in the ML development life cycle and build and deploy models easily for your ML use cases.
Simon Aubury, Ned Letcher, Kris Jenkins
DuckDB is a fast in-process analytical database. Getting Started with DuckDB offers a practical overview of its usage. You'll learn to load, transform, and query various data formats, including CSV, JSON, and Parquet. The book covers DuckDB's optimizations, SQL enhancements, and extensions for specialized applications. Working with examples in SQL, Python, and R, you'll explore analyzing public datasets and discover tools enhancing DuckDB workflows. This guide suits both experienced and new data practitioners, quickly equipping you to apply DuckDB's capabilities in analytical projects. You'll gain proficiency in using DuckDB for diverse tasks, enabling effective integration into your data workflows.
Asjad Athick
The Elastic Stack helps you work with massive volumes of data to power use cases in the search, observability, and security solution areas.This three-part book starts with an introduction to the Elastic Stack with high-level commentary on the solutions the stack can be leveraged for. The second section focuses on each core component, giving you a detailed understanding of the component and the role it plays. You’ll start by working with Elasticsearch to ingest, search, analyze, and store data for your use cases. Next, you’ll look at Logstash, Beats, and Elastic Agent as components that can collect, transform, and load data. Later chapters help you use Kibana as an interface to consume Elastic solutions and interact with data on Elasticsearch. The last section explores the three main use cases offered on top of the Elastic Stack. You’ll start with a full-text search and look at real-world outcomes powered by search capabilities. Furthermore, you’ll learn how the stack can be used to monitor and observe large and complex IT environments. Finally, you’ll understand how to detect, prevent, and respond to security threats across your environment. The book ends by highlighting architecture best practices for successful Elastic Stack deployments.By the end of this book, you’ll be able to implement the Elastic Stack and derive value from it.
Bhanu Birani
Analytics help you to understand the way your users are interacting with your mobile application. Flurry provides you with a strong analytics tool to keep track of your application and to analyze your application data without much hassle. It provides an SDK for almost all platforms to gather data from all platforms of mobile and web applications. The enormous amount of data Flurry handles directly translates into unique, powerful insights into your applications.Getting Started with Flurry Analytics is a practical, hands-on guide to instantly getting started with Flurry analytics using step-by-step exercises. You will take advantage of the real power of Flurry analytics and provides you with an example-based learning approach to help you master it quickly and efficiently.This book starts by discussing how to set up and integrate Flurry analytics and ends by making you aware of how to track your application using Parse. You will also learn about tracking your application data using Flurry analytics to strengthen your strategies and to improve the application monetization process. You will also learn about configuration and implementation of Flurry analytics and how to make the application track all the necessary details to empower management decisions.
James Church
Every business and organization that collects data is capable of tapping into its own data to gain insights how to improve. Haskell is a purely functional and lazy programming language, well-suited to handling large data analysis problems. This book will take you through the more difficult problems of data analysis in a hands-on manner.This book will help you get up-to-speed with the basics of data analysis and approaches in the Haskell language. You'll learn about statistical computing, file formats (CSV and SQLite3), descriptive statistics, charts, and progress to more advanced concepts such as understanding the importance of normal distribution. While mathematics is a big part of data analysis, we've tried to keep this course simple and approachable so that you can apply what you learn to the real world.By the end of this book, you will have a thorough understanding of data analysis, and the different ways of analyzing data. You will have a mastery of all the tools and techniques in Haskell for effective data analysis.