Advanced Data Analytics with AWS - Search

nexusstc/Serverless ETL and Analytics with AWS Glue: Your comprehensive reference guide to learning about AWS Glue and its features/f393807d5cdb0ed9d2ef4709bbf8b225.epub

Serverless ETL and Analytics with AWS Glue: Your comprehensive reference guide to learning about AWS Glue and its features Vishal Pathak, Subramanya Vajiraya, Noritaka Sekiyama, Tomohiro Tanaka, Albert Quiroga Bertolin, Ishan Gaur Packt Publishing - ebooks Account, 1, 2022

Build efficient data lakes that can scale to virtually unlimited size using AWS Glue Key Features Learn to work with AWS Glue to overcome typical implementation challenges in data lakes Create and manage serverless ETL pipelines that can scale to manage big data Written by AWS Glue community members, this practical guide shows you how to implement AWS Glue in no time Book Description Organizations these days have gravitated toward services such as AWS Glue that undertake undifferentiated heavy lifting and provide serverless Spark, enabling you to create and manage data lakes in a serverless fashion. This guide shows you how AWS Glue can be used to solve real-world problems along with helping you learn about data processing, data integration, and building data lakes. Beginning with AWS Glue basics, this book teaches you how to perform various aspects of data analysis such as ad hoc queries, data visualization, and real-time analysis using this service. It also provides a walk-through of CI/CD for AWS Glue and how to shift left on quality using automated regression tests. You'll find out how data security aspects such as access control, encryption, auditing, and networking are implemented, as well as getting to grips with useful techniques such as picking the right file format, compression, partitioning, and bucketing. As you advance, you'll discover AWS Glue features such as crawlers, Lake Formation, governed tables, lineage, DataBrew, Glue Studio, and custom connectors. The concluding chapters help you to understand various performance tuning, troubleshooting, and monitoring options. By the end of this AWS book, you'll be able to create, manage, troubleshoot, and deploy ETL pipelines using AWS Glue. What you will learn Apply various AWS Glue features to manage and create data lakes Use Glue DataBrew and Glue Studio for data preparation Optimize data layout in cloud storage to accelerate analytics workloads Manage metadata including database, table, and schema definitions Secure your data during access control, encryption, auditing, and networking Monitor AWS Glue jobs to detect delays and loss of data Integrate Spark ML and SageMaker with AWS Glue to create machine learning models Who this book is for This book is for ETL developers, data engineers, and data analysts who want to understand how AWS Glue can help you solve your business problems. Basic knowledge of AWS data services is assumed. Table of Contents Data Management – Introduction and Concepts Introduction to Important AWS Glue Features Data Ingestion Data Preparation Designing Data Layouts Data Management Metadata Management Data Security Data Sharing Data Pipeline Management Monitoring Tuning, Debugging, and Troubleshooting Data Analysis Machine Learning Integration Architecting Data Lakes for Real-World Scenarios and Edge Cases

English [en] · EPUB · 13.8MB · 2022 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

nexusstc/AWS Certified Data Analytics - Specialty: Study Guide With Practice Questions & Labs: First Edition - 2022/110546be888cab72bb58cdddf3a5fbff.pdf

AWS Certified Data Analytics - Specialty: Study Guide With Practice Questions & Labs: First Edition - 2022 IP Specialist IP Specialist

MASTER THE AWS CERTIFIED DATA ANALYTICS SPECIALTY EXAM OF AWS IN 7 DAYS!This thorough study guide will assist you in evaluating your technical abilities and preparing for the new AWS Certified Data Analytics test. This AWS certification validates your ability to build and implement AWS services to extract value from data. Business analysts and IT professionals who undertake complex Big Data analysis should take the AWS Certified Data Analytics Study Guide: Specialty (DAS-C01) Exam.With expert information, real-world expertise, important exam ideas, and topic reviews, this AWS Specialty Exam guide prepares you for certification testing. Study the topic areas and work through the practice questions to gain confidence. It is a perfect resource to pass the AWS Certified Data Analytics Specialty (DAS-C01) exam on the first attempt. The latest edition includes-Covers complete exam blueprint-Practice Questions.-Mind-maps-Hand-on practice labs.-Real-world examples-Exam tipsThis exam measures your ability to describe the following concepts-Collection-Storage and Data Management-Processing-Analysis and Visualization-Security

English [en] · PDF · 62.1MB · 📘 Book (non-fiction) · 🚀/nexusstc/zlib · Save

lgli/Georgio Daccache [Daccache, Georgio] - AWS Certified Advanced Networking - Specialty (ANS-C00) - Complete Preparation: Get Certified With Our AWS Certified Advanced Networking (ANS-C00) Practice Tests. Pass the Exam With Confident ! (2021, Independently published).epub

AWS Certified Advanced Networking - Specialty (ANS-C00) - Complete Preparation: Get Certified With Our AWS Certified Advanced Networking (ANS-C00) Practice Tests. Pass the Exam With Confident ! Georgio Daccache [Daccache, Georgio] Independently published, 2021

Want to maximize your chances of passing your AWS Certified Advanced Networking - Specialty (ANS-C00) exam first time? Then these brand-new practice exam Book is for you! Our team and I are excited to bring you this Book to help you pass AWS Certified Advanced Networking - Specialty (ANS-C00) exam. We gave a considerable amount of effort to create and publish this Book Which contain the Latest Questions & Detailed Explanation, including the laborious task of checking each item for any errors. We are confident that this will significantly help you pass your AWS Certified Advanced Networking - Specialty (ANS-C00) exam.We provides certification training services and training materials to thousand of student and corporations in both online and offline. We also develop supplemental materials to support your certification preparation especially prepare for CODING INTERVIEW.Just give it a Try and Test your skill and potentially make an awesome career for yourself.Welcome!

English [en] · EPUB · 0.2MB · 2021 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

nexusstc/Serverless ETL and Analytics with AWS Glue: Your comprehensive reference guide to learning about AWS Glue and its features/7deaaedb68e4cd3a5edf40e15dd97046.pdf

Serverless ETL and Analytics with AWS Glue: Your comprehensive reference guide to learning about AWS Glue and its features Vishal Pathak, Subramanya Vajiraya, Noritaka Sekiyama, Tomohiro Tanaka, Albert Quiroga Bertolin, Ishan Gaur Packt Publishing - ebooks Account, 1st, 2022

Build efficient data lakes that can scale to virtually unlimited size using AWS Glue Key Features: Learn to work with AWS Glue to overcome typical implementation challenges in data lakes Create and manage serverless ETL pipelines that can scale to manage big data Written by AWS Glue community members, this practical guide shows you how to implement AWS Glue in no time Book Description: Organizations these days have gravitated toward services such as AWS Glue that undertake undifferentiated heavy lifting and provide serverless Spark, enabling you to create and manage data lakes in a serverless fashion. This guide shows you how AWS Glue can be used to solve real-world problems along with helping you learn about data processing, data integration, and building data lakes. Beginning with AWS Glue basics, this book teaches you how to perform various aspects of data analysis such as ad hoc queries, data visualization, and real-time analysis using this service. It also provides a walk-through of CI/CD for AWS Glue and how to shift left on quality using automated regression tests. You'll find out how data security aspects such as access control, encryption, auditing, and networking are implemented, as well as getting to grips with useful techniques such as picking the right file format, compression, partitioning, and bucketing. As you advance, you'll discover AWS Glue features such as crawlers, Lake Formation, governed tables, lineage, DataBrew, Glue Studio, and custom connectors. The concluding chapters help you to understand various performance tuning, troubleshooting, and monitoring options. By the end of this AWS book, you'll be able to create, manage, troubleshoot, and deploy ETL pipelines using AWS Glue. What You Will Learn: Apply various AWS Glue features to manage and create data lakes Use Glue DataBrew and Glue Studio for data preparation Optimize data layout in cloud storage to accelerate analytics workloads Manage metadata including database, table, and schema definitions Secure your data during access control, encryption, auditing, and networking Monitor AWS Glue jobs to detect delays and loss of data Integrate Spark ML and SageMaker with AWS Glue to create machine learning models Who this book is for: This book is for ETL developers, data engineers, and data analysts who want to understand how AWS Glue can help you solve your business problems. Basic knowledge of AWS data services is assumed.

English [en] · PDF · 26.5MB · 2022 · 📘 Book (non-fiction) · 🚀/nexusstc/zlib · Save

Data Engineering with AWS - 2nd Edition (Early Access) Gareth Eagar Packt Publishing Limited, 2nd, 2023

Looking to revolutionize your data transformation game with AWS? Look no further! From strong foundations to hands-on building of data engineering pipelines, our expert-led manual has got you covered.Key FeaturesDelve into robust AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelinesStay up to date with a comprehensive revised chapter on Data GovernanceBuild modern data platforms with a new section covering transactional data lakes and data meshBook DescriptionThis book, authored by a seasoned Senior Data Architect with 25 years of experience, aims to help you achieve proficiency in using the AWS ecosystem for data engineering. This revised edition provides updates in every chapter to cover the latest AWS services and features, takes a refreshed look at data governance, and includes a brand-new section on building modern data platforms which covers; implementing a data mesh approach, open-table formats (such as Apache Iceberg), and using DataOps for automation and observability. You'll begin by reviewing the key concepts and essential AWS tools in a data engineer's toolkit and getting acquainted with modern data management approaches. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how that transformed data is used by various data consumers. You'll learn how to ensure strong data governance, and about populating data marts and data warehouses along with how a data lakehouse fits into the picture. After that, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. Then, you'll explore how the power of machine learning and artificial intelligence can be used to draw new insights from data. In the final chapters, you'll discover transactional data lakes, data meshes, and how to build a cutting-edge data platform on AWS. By the end of this AWS book, you'll be able to execute data engineering tasks and implement a data pipeline on AWS like a pro!What you will learnSeamlessly ingest streaming data with Amazon Kinesis Data FirehoseOptimize, denormalize, and join datasets with AWS Glue StudioUse Amazon S3 events to trigger a Lambda process to transform a fileLoad data into a Redshift data warehouse and run queries with easeVisualize and explore data using Amazon QuickSightExtract sentiment data from a dataset using Amazon ComprehendBuild transactional data lakes using Apache Iceberg with Amazon AthenaLearn how a data mesh approach can be implemented on AWSWho this book is forThis book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts, while gaining practical experience with common data engineering services on AWS, will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book, but it's not a prerequisite. Familiarity with the AWS console and core services will also help you follow along.

English [en] · EPUB · 12.9MB · 2023 · 📘 Book (non-fiction) · 🚀/zlib · Save

upload/newsarch_ebooks/2023/04/04/extracted__Data_Analytics_in_the_AWS_Cloud_Building_a_Data_Platform_for_BI_and_Predictive_Analytics_on_AWS.zip/Data Analytics in the AWS Cloud Building a Data Platform for BI and Predictive Analytics on AWS/Data Analytics in the AWS Cloud Building a Data Platform for BI and Predictive Analytics on AWS.pdf

Data analytics in the AWS cloud : building a data platform for BI and predictive analytics on AWS Cork, Ireland, Joe Minichino WILEY-SYBEX, 1, DE, 2023

A comprehensive and accessible roadmap to performing data analytics in the AWS cloud In Data Analytics in the AWS Cloud: Building a Data Platform for BI and Predictive Analytics on AWS , accomplished software engineer and data architect Joe Minichino delivers an expert blueprint to storing, processing, analyzing data on the Amazon Web Services cloud platform. In the book, you’ll explore every relevant aspect of data analytics―from data engineering to analysis, business intelligence, DevOps, and MLOps―as you discover how to integrate machine learning predictions with analytics engines and visualization tools. You’ll also find: Real-world use cases of AWS architectures that demystify the applications of data analytics Accessible introductions to data acquisition, importation, storage, visualization, and reporting Expert insights into serverless data engineering and how to use it to reduce overhead and costs, improve stability, and simplify maintenance A can't-miss for data architects, analysts, engineers and technical professionals, Data Analytics in the AWS Cloud will also earn a place on the bookshelves of business leaders seeking a better understanding of data analytics on the AWS cloud platform.

English [en] · PDF · 12.5MB · 2023 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

upload/newsarch_ebooks_2025_10/2023/06/23/Data Analytics in the AWS Cloud.mobi

Data analytics in the AWS cloud : building a data platform for BI and predictive analytics on AWS Joe Minichino Sybex, Hoboken, NJ, 2023

A comprehensive and accessible roadmap to performing data analytics in the AWS cloud In Data Analytics in the AWS Cloud: Building a Data Platform for BI and Predictive Analytics on AWS , accomplished software engineer and data architect Joe Minichino delivers an expert blueprint to storing, processing, analyzing data on the Amazon Web Services cloud platform. In the book, you'll explore every relevant aspect of data analytics—from data engineering to analysis, business intelligence, DevOps, and MLOps—as you discover how to integrate machine learning predictions with analytics engines and visualization tools. You'll also find: Real-world use cases of AWS architectures that demystify the applications of data analytics Accessible introductions to data acquisition, importation, storage, visualization, and reporting Expert insights into serverless data engineering and how to use it to reduce overhead and costs, improve stability, and simplify maintenance A can't-miss for data architects, analysts, engineers and technical professionals, Data Analytics in the AWS Cloud will also earn a place on the bookshelves of business leaders seeking a better understanding of data analytics on the AWS cloud platform.

English [en] · MOBI · 15.3MB · 2023 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

nexusstc/Geospatial Data Analytics on AWS: Discover how to manage and analyze geospatial data in the cloud/705d53da91581ddf8b5714b980697706.rar

Geospatial Data Analytics on AWS : Discover How to Manage and Analyze Geospatial Data in the Cloud Scott Bateman, Janahan Gnanachandran, Jeff DeMuth Packt Publishing, Limited, Packt Publishing, Birmingham, 2023

Build an end-to-end geospatial data lake in AWS using popular AWS services such as RDS, Redshift, DynamoDB, and Athena to manage geodata Purchase of the print or Kindle book includes a free PDF eBook. Key Features Explore the architecture and different use cases to build and manage geospatial data lakes in AWS Discover how to leverage AWS purpose-built databases to store and analyze geospatial data Learn how to recognize which anti-patterns to avoid when managing geospatial data in the cloud Book Description 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. What you will learn Discover how to optimize the cloud to store your geospatial data Explore management strategies for your data repository using AWS Single Sign-On and IAM Create effective SQL queries against your geospatial data using Athena Validate postal addresses using Amazon Location services Process structured and unstructured geospatial data efficiently using R Use Amazon SageMaker to enable machine learning features in your application Explore the free and subscription satellite imagery data available for use in your GIS Who this book is for If you understand the importance of accurate coordinates, but not necessarily the cloud, then this book is for you. This book is best suited for GIS developers, GIS analysts, data analysts, and data scientists looking to enhance their solutions with geospatial data for cloud-centric applications. A basic understanding of geographic concepts is suggested, but no experience with the cloud is necessary for understanding the concepts in this book. Table of Contents Introduction to Geospatial Data in the Cloud Quality and Temporal Geospatial Data Concepts Geospatial Data Lake Architecture Using Geospatial Data with Amazon Redshift Using Geospatial Data with Amazon Aurora PostgreSQL Serverless Geospatial Querying Geospatial Data with Amazon Athena Geospatial Containers on AWS Geospatial Data with Amazon EMR Geospatial Data Analysis using Python on AWS Cloud9 Geospatial Data Analysis using SageMaker Using Amazon QuickSight to Visualize Geospatial Data Open Data on AWS Leveraging OpenStreetMap on AWS Map and Feature Services on AWS Satellite Imagery on AWS

English [en] · RAR · 40.9MB · 2023 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

nexusstc/Data Analytics in the AWS Cloud: Building a Data Platform for BI and Predictive Analytics on AWS/a25705535ec7849405376f462f6d464d.rar

Data analytics in the AWS cloud : building a data platform for BI and predictive analytics on AWS Cork, Ireland, Joe Minichino WILEY-SYBEX, 1, DE, 2023

A comprehensive and accessible roadmap to performing data analytics in the AWS cloud In Data Analytics in the AWS Cloud: Building a Data Platform for BI and Predictive Analytics on AWS , accomplished software engineer and data architect Joe Minichino delivers an expert blueprint to storing, processing, analyzing data on the Amazon Web Services cloud platform. In the book, you’ll explore every relevant aspect of data analytics―from data engineering to analysis, business intelligence, DevOps, and MLOps―as you discover how to integrate machine learning predictions with analytics engines and visualization tools. You’ll also find: Real-world use cases of AWS architectures that demystify the applications of data analytics Accessible introductions to data acquisition, importation, storage, visualization, and reporting Expert insights into serverless data engineering and how to use it to reduce overhead and costs, improve stability, and simplify maintenance A can't-miss for data architects, analysts, engineers and technical professionals, Data Analytics in the AWS Cloud will also earn a place on the bookshelves of business leaders seeking a better understanding of data analytics on the AWS cloud platform.

English [en] · RAR · 37.5MB · 2023 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc · Save

upload/newsarch_ebooks_2025_10/2023/06/23/Data Analytics in the AWS Cloud.epub

Data analytics in the AWS cloud : building a data platform for BI and predictive analytics on AWS Cork, Ireland, Joe Minichino John Wiley & Sons, Incorporated, 1, DE, 2023

A comprehensive and accessible roadmap to performing data analytics in the AWS cloud In Data Analytics in the AWS Cloud: Building a Data Platform for BI and Predictive Analytics on AWS , accomplished software engineer and data architect Joe Minichino delivers an expert blueprint to storing, processing, analyzing data on the Amazon Web Services cloud platform. In the book, you’ll explore every relevant aspect of data analytics―from data engineering to analysis, business intelligence, DevOps, and MLOps―as you discover how to integrate machine learning predictions with analytics engines and visualization tools. You’ll also find: Real-world use cases of AWS architectures that demystify the applications of data analytics Accessible introductions to data acquisition, importation, storage, visualization, and reporting Expert insights into serverless data engineering and how to use it to reduce overhead and costs, improve stability, and simplify maintenance A can't-miss for data architects, analysts, engineers and technical professionals, Data Analytics in the AWS Cloud will also earn a place on the bookshelves of business leaders seeking a better understanding of data analytics on the AWS cloud platform.

English [en] · EPUB · 23.5MB · 2023 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

lgli/Extending Excel with Python and R Unlock the potential of analytics languages for advanced data manipulation and visualization.pdf

Extending Excel with Python and R: Unlock the potential of analytics languages for advanced data manipulation and visualization STEVEN P.. KUN SANDERSON (DAVID.) Packt Publishing Pvt Ltd, 1, 2024

Seamlessly integrate the Python and R programming languages with spreadsheet-based data analysis to maximize productivity Key Features Perform advanced data analysis and visualization techniques with R and Python on Excel data Use exploratory data analysis and pivot table analysis for deeper insights into your data Integrate R and Python code directly into Excel using VBA or API endpoints Purchase of the print or Kindle book includes a free PDF eBook Book Description For businesses, data analysis and visualization are crucial for informed decision-making; however, Excel's limitations can make these tasks time-consuming and challenging. Extending Excel with Python and R is a game changer resource written by experts Steven Sanderson, the author of the healthyverse suite of R packages, and David Kun, co-founder of Functional Analytics, the company behind the ownR platform engineering solution for R, Python, and other data science languages. This comprehensive guide transforms the way you work with spreadsheet-based data by integrating Python and R with Excel to automate tasks, execute statistical analysis, and create powerful visualizations. Working through the chapters, you'll find out how to perform exploratory data analysis, time series analysis, and even integrate APIs for maximum efficiency. Whether you're a beginner or an expert, this book has everything you need to unlock Excel's full potential and take your data analysis skills to the next level. By the end of this book, you'll be able to import data from Excel, manipulate it in R or Python, and perform the data analysis tasks in your preferred framework while pushing the results back to Excel for sharing with others as needed. What you will learn Read and write Excel files with R and Python libraries Automate Excel tasks with R and Python scripts Use R and Python to execute Excel VBA macros Format Excel sheets using R and Python packages Create graphs with ggplot2 and Matplotlib in Excel Analyze Excel data with statistical methods and time series analysis Explore various methods to call R and Python functions from Excel Who this book is for If you're a data analyst or data scientist, or a quants, actuaries, or data practitioner looking to enhance your Excel skills and expand your data analysis capabilities with R and Python, this book is for you. It provides a comprehensive introduction to the topics covered, making it suitable for both beginners and intermediate learners. A basic understanding of Excel, Python, and R is all you need to get started.

English [en] · PDF · 12.5MB · 2024 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/zlib · Save

Data Engineering with AWS : Acquire the Skills to Design and Build AWS-based Data Transformation Pipelines Like a Pro Eagar, Gareth Packt Publishing Limited, 2, 2024

Looking to revolutionize your data transformation game with AWS? Look no further! From strong foundations to hands-on building of data engineering pipelines, our expert-led manual has got you covered.Key FeaturesDelve into robust AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelinesStay up to date with a comprehensive revised chapter on Data GovernanceBuild modern data platforms with a new section covering transactional data lakes and data meshBook DescriptionThis book, authored by a seasoned Senior Data Architect with 25 years of experience, aims to help you achieve proficiency in using the AWS ecosystem for data engineering. This revised edition provides updates in every chapter to cover the latest AWS services and features, takes a refreshed look at data governance, and includes a brand-new section on building modern data platforms which covers; implementing a data mesh approach, open-table formats (such as Apache Iceberg), and using DataOps for automation and observability. You'll begin by reviewing the key concepts and essential AWS tools in a data engineer's toolkit and getting acquainted with modern data management approaches. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how that transformed data is used by various data consumers. You'll learn how to ensure strong data governance, and about populating data marts and data warehouses along with how a data lakehouse fits into the picture. After that, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. Then, you'll explore how the power of machine learning and artificial intelligence can be used to draw new insights from data. In the final chapters, you'll discover transactional data lakes, data meshes, and how to build a cutting-edge data platform on AWS. By the end of this AWS book, you'll be able to execute data engineering tasks and implement a data pipeline on AWS like a pro!What you will learnSeamlessly ingest streaming data with Amazon Kinesis Data FirehoseOptimize, denormalize, and join datasets with AWS Glue StudioUse Amazon S3 events to trigger a Lambda process to transform a fileLoad data into a Redshift data warehouse and run queries with easeVisualize and explore data using Amazon QuickSightExtract sentiment data from a dataset using Amazon ComprehendBuild transactional data lakes using Apache Iceberg with Amazon AthenaLearn how a data mesh approach can be implemented on AWSWho this book is forThis book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts, while gaining practical experience with common data engineering services on AWS, will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book, but it's not a prerequisite. Familiarity with the AWS console and core services will also help you follow along.

English [en] · PDF · 32.1MB · 2024 · 📘 Book (non-fiction) · 🚀/zlib · Save

lgli/AWS - Creating a Modern Analytics Architecture (AWS).pdf

Creating a Modern Analytics Architecture AWS AWS

Creating a Modern Analytics Archietecture.

English [en] · PDF · 0.9MB · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

upload/newsarch_ebooks_2025_10/2017/09/24/Advanced Analytics with Spark.pdf

Advanced analytics with Spark : patterns for learning from data at scale Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills O'Reilly Media, Incorporated, 2nd Edition, Jul 06, 2017

Sandy Ryza, Uri Laserson, Sean Owen And Josh Wills. Previous Edition Published: 2015. Includes Index.

English [en] · PDF · 5.5MB · 2017 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

lgli/Advanced Analytics with Excel 2019 (Manisha Nigam).epub

Advanced Analytics with Excel 2019: Perform Data Analysis Using Excel’s Most Popular Features (English Editions) Manisha Nigam BPB Publications, BPB Online LLP, [N.p.], 2020

Explore different ways and methods to consolidate data, complex analysis, and prediction or forecast based on trends KEY FEATURES ● Use the Analysis ToolPak to perform complex Data analysis ● Get well versed with the formulas, functions, and components in Excel ● Handy templates to give you a head start ● Usage of multiple examples to explain the application in a real-world scenario ● Implement macros for your everyday tasks that will help you save your time ● Explore different Charts types for Data visualization DESCRIPTION Book explains and simplify the usage of Excel features and functionalities, with the help of examples. It starts with ‘Getting Started with Excel’ and ‘Performing functions with shortcut keys’ which will help you in getting started with Excel. Then ‘Formulas and Functions’ gives an initial understanding of what are operators, formulas, functions, their components. Further ‘Data Visualization with new Charts types’, ‘Gantt and Milestone chart’, ‘SmartArt and Organization Chart’ give details on the different chart types available in Excel. In the intermediate section you will learn ‘Get creative with Icons, 3D models, Digital Inking’ details multiple new and improved features that got introduced to enhance the visual presentation. In the end, Chapters ‘Mail Merge using Excel’, ‘Create Custom Excel Template’ and ‘Macros in Excel’ explain the Excel features that help in automating tasks. You will learn how to generate multiple documents automatically with customization, create and use your own templates and use of macros to do repeated task automatically. And at last Chapter ‘Get help for your problem’ lists few problem statements and their probable solutions with references to the Excel feature or functionality that can be used to resolve the problem. WHAT WILL YOU LEARN ● Get familiar with the most used advanced Excel formulas and functions for Data analysis ● Learn how to create a Gantt / Timeline / Milestone Chart in Excel ● Use charts for Better Data visualization ● Build organization charts with SmartArt tools in Excel ● Use the Analysis ToolPak & Power Pivots to perform complex Data analysis ● Learn how to link and share workbooks for automatic updates WHO THIS BOOK IS FOR This book is for professionals from any domain, who are searching for shortcuts & advanced methods to resolve their daily problems. Table of Contents 1. Getting Started with Excel 2. Perform Functions with Shortcut Keys 3. Formulas and Functions 4. Data Visualization with New Chart types 5. Gantt and Milestone Chart 6. SmartArt & Organization Chart 7. Get creative with Icons, 3D models, Digital Inking 8. Putting Data in perspective with Pivots 9. Complex Data Analysis using ToolPak 10. Forecasting in Excel 11. Mail Merge using Excel 12. Create Custom Excel Template 13. Macros in Excel 14. Get help for your problem AUTHOR BIO Manisha Nigam is a seasoned management professional with twenty plus years of information technology experience, working with multinationals across the globe. Her expertise in Excel comes from the vast experience she has in using and understanding the software over the years, that helped her in working efficiently and managing complex programs. A certified PMP (PMI – Project Management Professional), CSM (Scrum Alliance – Scrum Master), TOGAF 9.1 (The Open Group – Enterprise Architecture) and holds a post graduate degree in computer science and business management from prestigious universities in India. Her LinkedIn Profile: linkedin.com/in/mnigam10

English [en] · EPUB · 28.9MB · 2020 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/zlib · Save

nexusstc/Machine Learning: Fundamental Algorithms for Supervised and Unsupervised Learning With Real-World Applications (Advanced Data Analytics Book 1)/604b778d6077385b58e6584aaea0f595.epub

Machine Learning: Fundamental Algorithms for Supervised and Unsupervised Learning With Real-World Applications (Advanced Data Analytics Book 1) Joshua Chapmann

Computers can't LEARN... Right?! Machine Learning is a branch of computer science that wants to stop programming computers using a detailed list of commands to follow blindly. Instead, their aim is to implement high-level routines that teach computers how to approach new and unknown problems – these are called algorithms . In practice, they want to give computers the ability to Learn and to Adapt. We can use these algorithms to obtain insights, recognize patterns and make predictions from data, images, sounds or videos we have never seen before – or even knew existed. Unfortunately, the true power and applications of today’s Machine Learning Algorithms remain deeply misunderstood by most people. Through this book I want fix this confusion, I want to shed light on the most relevant Machine Learning Algorithms used in the industry. I will show you exactly how each algorithm works, why it works and when you should use it. Supervised Learning Algorithms K-Nearest Neighbour Naïve Bayes Regressions Unsupervised Learning Algorithms: Support Vector Machines Neural Networks Decision Trees

English [en] · EPUB · 1.2MB · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

upload/newsarch_ebooks/2020/06/01/Machine Learning_ Introduction to Supervised and Unsupervised Learning Algorithms with Real-World Applications (Advanced Data Analytics Book 1).pdf

Machine Learning: Introduction to Supervised and Unsupervised Learning Algorithms with Real-World Applications (Advanced Data Analytics Book 1) Maynard, Morgan Advanced Data Analytics, 1, 2020

How Do Computers Learn?Machine Learning is a branch of computer science that wants to stop programming using a detailed list of commands. Instead, their aim is to implement high-level routines that teach computers how to approach new and unknown problems – these are learning algorithms.In practice, these Algorithms empower computers to Learn and to Adapt.We can use these algorithms to obtain insights, recognize patterns and make predictions from data, images, sounds or videos we have never seen before. Unfortunately, the true power and applications of today’s Machine Learning Algorithms remain deeply misunderstood by many people.Through this book I want fix this confusion, I want to shed light on the most relevant Machine Learning Algorithms in use today. I will show you exactly how each algorithm works, why it works and when you should use it.Supervised Learning AlgorithmsK-Nearest NeighbourNaïve BayesRegressionsUnsupervised Learning Algorithms:Support Vector MachinesNeural NetworksDecision Trees

English [en] · PDF · 3.0MB · 2020 · 📘 Book (non-fiction) · 🚀/lgli/upload/zlib · Save

upload/motw_shc_2025_10/shc/Advanced Analytics With Spark_ Patterns fo - Sandy Ryza.pdf

Advanced Analytics with Spark Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills

Table of Contents 5 Foreword 9 Preface 11 What’s in This Book 12 Using Code Examples 12 Safari® Books Online 12 How to Contact Us 13 Acknowledgments 13 Chapter 1. Analyzing Big Data 15 The Challenges of Data Science 17 Introducing Apache Spark 18 About This Book 20 Chapter 2. Introduction to Data Analysis with Scala and Spark 23 Scala for Data Scientists 24 The Spark Programming Model 25 Record Linkage 25 Getting Started: The Spark Shell and SparkContext 27 Bringing Data from the Cluster to the Client 32 Shipping Code from the Client to the Cluster 36 Structuring Data with Tuples and Case Classes 37 Aggregations 42 Creating Histograms 43 Summary Statistics for Continuous Variables 44 Creating Reusable Code for Computing Summary Statistics 45 Simple Variable Selection and Scoring 50 Where to Go from Here 51 Chapter 3. Recommending Music and the Audioscrobbler Data Set 53 Data Set 54 The Alternating Least Squares Recommender Algorithm 55 Preparing the Data 57 Building a First Model 60 Spot Checking Recommendations 62 Evaluating Recommendation Quality 64 Computing AUC 65 Hyperparameter Selection 67 Making Recommendations 69 Where to Go from Here 70 Chapter 4. Predicting Forest Cover with Decision Trees 73 Fast Forward to Regression 73 Vectors and Features 74 Training Examples 75 Decision Trees and Forests 76 Covtype Data Set 79 Preparing the Data 80 A First Decision Tree 81 Decision Tree Hyperparameters 85 Tuning Decision Trees 87 Categorical Features Revisited 89 Random Decision Forests 91 Making Predictions 93 Where to Go from Here 93 Chapter 5. Anomaly Detection in Network Traffic with K-means Clustering 95 Anomaly Detection 96 K-means Clustering 96 Network Intrusion 97 KDD Cup 1999 Data Set 98 A First Take on Clustering 99 Choosing k 101 Visualization in R 103 Feature Normalization 105 Categorical Variables 108 Using Labels with Entropy 109 Clustering in Action 110 Where to Go from Here 111 Chapter 6. Understanding Wikipedia with Latent Semantic Analysis 113 The Term-Document Matrix 114 Getting the Data 116 Parsing and Preparing the Data 116 Lemmatization 118 Computing the TF-IDFs 119 Singular Value Decomposition 121 Finding Important Concepts 123 Querying and Scoring with the Low-Dimensional Representation 126 Term-Term Relevance 127 Document-Document Relevance 129 Term-Document Relevance 130 Multiple-Term Queries 131 Where to Go from Here 133 Chapter 7. Analyzing Co-occurrence Networks with GraphX 135 The MEDLINE Citation Index: A Network Analysis 136 Getting the Data 137 Parsing XML Documents with Scala’s XML Library 139 Analyzing the MeSH Major Topics and Their Co-occurrences 141 Constructing a Co-occurrence Network with GraphX 143 Understanding the Structure of Networks 146 Connected Components 146 Degree Distribution 149 Filtering Out Noisy Edges 152 Processing EdgeTriplets 153 Analyzing the Filtered Graph 154 Small-World Networks 156 Cliques and Clustering Coefficients 157 Computing Average Path Length with Pregel 158 Where to Go from Here 163 Chapter 8. Geospatial and Temporal Data Analysis on the New York City Taxi Trip Data 165 Getting the Data 166 Working with Temporal and Geospatial Data in Spark 167 Temporal Data with JodaTime and NScalaTime 167 Geospatial Data with the Esri Geometry API and Spray 169 Exploring the Esri Geometry API 169 Intro to GeoJSON 171 Preparing the New York City Taxi Trip Data 173 Handling Invalid Records at Scale 174 Geospatial Analysis 178 Sessionization in Spark 181 Building Sessions: Secondary Sorts in Spark 182 Where to Go from Here 185 Chapter 9. Estimating Financial Risk through Monte Carlo Simulation 187 Terminology 188 Methods for Calculating VaR 189 Variance-Covariance 189 Historical Simulation 189 Monte Carlo Simulation 189 Our Model 190 Getting the Data 191 Preprocessing 192 Determining the Factor Weights 195 Sampling 197 The Multivariate Normal Distribution 199 Running the Trials 200 Visualizing the Distribution of Returns 203 Evaluating Our Results 204 Where to Go from Here 206 Chapter 10. Analyzing Genomics Data and the BDG Project 209 Decoupling Storage from Modeling 210 Ingesting Genomics Data with the ADAM CLI 212 Parquet Format and Columnar Storage 218 Predicting Transcription Factor Binding Sites from ENCODE Data 220 Querying Genotypes from the 1000 Genomes Project 227 Where to Go from Here 228 Chapter 11. Analyzing Neuroimaging Data with PySpark and Thunder 231 Overview of PySpark 232 PySpark Internals 233 Overview and Installation of the Thunder Library 235 Loading Data with Thunder 236 Thunder Core Data Types 243 Categorizing Neuron Types with Thunder 245 Where to Go from Here 250 Appendix A. Deeper into Spark 251 Serialization 253 Accumulators 253 Spark and the Data Scientist’s Workflow 254 File Formats 256 Spark Subprojects 257 MLlib 257 Spark Streaming 258 Spark SQL 259 GraphX 259 Appendix B. Upcoming MLlib Pipelines API 261 Beyond Mere Modeling 261 The Pipelines API 262 Text Classification Example Walkthrough 264 Index 267 About the Authors 275

PDF · 4.2MB · 📗 Book (unknown) · 🚀/upload · Save

nexusstc/Neural Networks: Introduction to Artificial Neurons, Backpropagation and Multilayer Feedforward Neural Networks with Real-World Applications (Advanced Data Analytics Book 2)/5d3d790744a5fa702ac63c8382386e76.azw3

Neural Networks: Introduction to Artificial Neurons, Backpropagation and Multilayer Feedforward Neural Networks with Real-World Applications (Advanced Data Analytics Book 2) Maynard, Morgan Independently publizhed, 2020

Software Engineers recognize that computers can process and store much more data than humans, yet even supercomputers can’t carry out tasks that come easily to the human brain, such as facial recognition or natural language processing. MIT’s state-of-the-art research facility, named “Centre for Brains, Minds and Machines”, is a perfect testimonial to this fundamental interaction between the human brain and computers in today’s world.Hence engineers began studying the processes and structures of our human brains, hoping to build a computer model of its functions – Neural Networks were born. These models are very simplistic, but fundamentally replicate the inner structures of our own brains downright to the functions of an individual neuronIn this book I show you exactly how engineres model the inner functions and structure of the human brains, covering the fundamental mathematical equations and underlying concepts. In particular, you will learn about:- How to Build a Computer model of a Brain Cell (or Neuron)- The Fundamental properties of a Neural Network- Multilayer Forward Networks- Using the Backpropagation algorithm to learn and adapt- Counter Propagation Networks - How to train a Neural network (validation and testing techniques to avoid overfitting)

English [en] · AZW3 · 6.6MB · 2020 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

Serverless ETL and Analytics with AWS Glue: Your comprehensive reference guide to learning about AWS Glue and its features Vishal Pathak, Subramanya Vajiraya,  Noritaka Sekiyama,  Tomohiro Tanaka , Albert Quiroga , Ishan Gaur Packt Publishing Pvt Ltd, 2022

Your comprehensive reference guide to learning about AWS Glue and its features

English [en] · MOBI · 17.9MB · 2022 · 📗 Book (unknown) · 🚀/zlib · Save

lgli/Z:\Bibliotik_\1\73.237.8.177\Jen Stirrup & Ruben Oliva Ramos-Advanced Analytics with R and Tableau_477.pdf

Advanced analytics with R and Tableau : advanced visual analytical solutions for your business Oliva Ramos, Ruben;Stirrup, Jen Packt Publishing, Limited, Packt Publishing, Birmingham, UK, 2017

Leverage the power of advanced analytics and predictive modeling in Tableau using the statistical powers of RAbout This BookA comprehensive guide that will bring out the creativity in you to visualize the results of complex calculations using Tableau and RCombine Tableau analytics and visualization with the power of R using this step-by-step guideWondering how R can be used with Tableau? This book is your one-stop solution.Who This Book Is ForThis book will appeal to Tableau users who want to go beyond the Tableau interface and deploy the full potential of Tableau, by using R to perform advanced analytics with Tableau.A basic familiarity with R is useful but not compulsory, as the book will start off with concrete examples of R and will move quickly into more advanced spheres of analytics using online data sources to support hands-on learning. Those R developers who want to integrate R in Tableau will also benefit from this book.What You Will LearnIntegrate Tableau's analytics with the industry-standard, statistical prowess of R.Make R function calls in Tableau, and visualize R functions with Tableau using RServe.Use the CRISP-DM methodology to create a roadmap for analytics investigations.Implement various supervised and unsupervised learning algorithms in R to return values to Tableau.Make quick, cogent, and data-driven decisions for your business using advanced analytical techniques such as forecasting, predictions, association rules, clustering, classification, and other advanced Tableau/R calculated field functions.In DetailTableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation.Moving from data visualization into deeper, more advanced analytics? This book will intensify data skills for data viz-savvy users who want to move into analytics and data science in order to enhance their businesses by harnessing the analytical power of R and the stunning visualization capabilities of Tableau. Readers will come across a wide range of machine learning algorithms and learn how descriptive, prescriptive, predictive, and visually appealing analytical solutions can be designed with R and Tableau. In order to maximize learning, hands-on examples will ease the transition from being a data-savvy user to a data analyst using sound statistical tools to perform advanced analytics.By the end of this book, you will get to grips with advanced calculations in R and Tableau for analytics and prediction with the help of use cases and hands-on examples.Style and approachTableau (uniquely) offers excellent visualization combined with advanced analytics; R is at the pinnacle of statistical computational languages. When you want to move from one view of data to another, backed up by complex computations, the combination of R and Tableau makes the perfect solution. This example-rich guide will teach you how to combine these two to perform advanced analytics by integrating Tableau with R and create beautiful data visualizations.

English [en] · PDF · 5.8MB · 2017 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

upload/newsarch_ebooks/2022/06/15/extracted__Advanced_Analytics_with_PySpark_Patterns_for_Learning_from_Data_at_Scale_Using_Python_and_Spark.zip/Advanced Analytics with PySpark Patterns for Learning from Data at Scale Using Python and Spark/Advanced Analytics with PySpark Patterns for Learning from Data at Scale Using Python and Spark.epub

Advanced analytics with PySpark : patterns for learning from data at Scale using Python and Spark Akash Tandon, Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills O'Reilly Media, Incorporated, May 17, 2022

The amount of data being generated today is staggering and growing. Apache Spark has emerged as the de facto tool to analyze big data and is now a critical part of the data science toolbox. Updated for Spark 3.0, this practical guide brings together Spark, statistical methods, and real-world datasets to teach you how to approach analytics problems using PySpark, Spark's Python API, and other best practices in Spark programming. Data scientists Akash Tandon, Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills offer an introduction to the Spark ecosystem, then dive into patterns that apply common techniques-including classification, clustering, collaborative filtering, and anomaly detection, to fields such as genomics, security, and finance. This updated edition also covers NLP and image processing. If you have a basic understanding of machine learning and statistics and you program in Python, this book will get you started with large-scale data analysis. Familiarize yourself with Spark's programming model and ecosystem Learn general approaches in data science Examine complete implementations that analyze large public datasets Discover which machine learning tools make sense for particular problems Explore code that can be adapted to many uses

English [en] · EPUB · 8.8MB · 2022 · 📘 Book (non-fiction) · 🚀/lgli/upload/zlib · Save

Serverless ETL and Analytics with AWS Glue: Your comprehensive reference guide to learning about AWS Glue and its features Vishal Pathak, Subramanya Vajiraya, Noritaka Sekiyama, Tomohiro Tanaka, Albert Quiroga Bertolin, Ishan Gaur Packt Publishing - ebooks Account, Birmingham, 2022

Build efficient data lakes that can scale to virtually unlimited size using AWS Glue Key Features: Learn to work with AWS Glue to overcome typical implementation challenges in data lakes Create and manage serverless ETL pipelines that can scale to manage big data Written by AWS Glue community members, this practical guide shows you how to implement AWS Glue in no time Book Description: Organizations these days have gravitated toward services such as AWS Glue that undertake undifferentiated heavy lifting and provide serverless Spark, enabling you to create and manage data lakes in a serverless fashion. This guide shows you how AWS Glue can be used to solve real-world problems along with helping you learn about data processing, data integration, and building data lakes. Beginning with AWS Glue basics, this book teaches you how to perform various aspects of data analysis such as ad hoc queries, data visualization, and real-time analysis using this service. It also provides a walk-through of CI/CD for AWS Glue and how to shift left on quality using automated regression tests. You'll find out how data security aspects such as access control, encryption, auditing, and networking are implemented, as well as getting to grips with useful techniques such as picking the right file format, compression, partitioning, and bucketing. As you advance, you'll discover AWS Glue features such as crawlers, Lake Formation, governed tables, lineage, DataBrew, Glue Studio, and custom connectors. The concluding chapters help you to understand various performance tuning, troubleshooting, and monitoring options. By the end of this AWS book, you'll be able to create, manage, troubleshoot, and deploy ETL pipelines using AWS Glue. What You Will Learn: Apply various AWS Glue features to manage and create data lakes Use Glue DataBrew and Glue Studio for data preparation Optimize data layout in cloud storage to accelerate analytics workloads Manage metadata including database, table, and schema definitions Secure your data during access control, encryption, auditing, and networking Monitor AWS Glue jobs to detect delays and loss of data Integrate Spark ML and SageMaker with AWS Glue to create machine learning models Who this book is for: This book is for ETL developers, data engineers, and data analysts who want to understand how AWS Glue can help you solve your business problems. Basic knowledge of AWS data services is assumed.

English [en] · EPUB · 13.8MB · 2022 · 📘 Book (non-fiction) · 🚀/zlib · Save

nexusstc/Geospatial Data Analytics on AWS: Discover how to manage and analyze geospatial data in the cloud/9099cd9ee2db2d3eb9ec5e839c7dcfb3.pdf

Geospatial Data Analytics on AWS : Discover How to Manage and Analyze Geospatial Data in the Cloud Scott Bateman, Janahan Gnanachandran, Jeff DeMuth Packt Publishing, Limited, 1, 2023

Build an end-to-end geospatial data lake in AWS using popular AWS services such as RDS, Redshift, DynamoDB, and Athena to manage geodata Purchase of the print or Kindle book includes a free PDF eBook. Key Features Explore the architecture and different use cases to build and manage geospatial data lakes in AWS Discover how to leverage AWS purpose-built databases to store and analyze geospatial data Learn how to recognize which anti-patterns to avoid when managing geospatial data in the cloud Book Description 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. What you will learn Discover how to optimize the cloud to store your geospatial data Explore management strategies for your data repository using AWS Single Sign-On and IAM Create effective SQL queries against your geospatial data using Athena Validate postal addresses using Amazon Location services Process structured and unstructured geospatial data efficiently using R Use Amazon SageMaker to enable machine learning features in your application Explore the free and subscription satellite imagery data available for use in your GIS Who this book is for If you understand the importance of accurate coordinates, but not necessarily the cloud, then this book is for you. This book is best suited for GIS developers, GIS analysts, data analysts, and data scientists looking to enhance their solutions with geospatial data for cloud-centric applications. A basic understanding of geographic concepts is suggested, but no experience with the cloud is necessary for understanding the concepts in this book. Table of Contents Introduction to Geospatial Data in the Cloud Quality and Temporal Geospatial Data Concepts Geospatial Data Lake Architecture Using Geospatial Data with Amazon Redshift Using Geospatial Data with Amazon Aurora PostgreSQL Serverless Geospatial Querying Geospatial Data with Amazon Athena Geospatial Containers on AWS Geospatial Data with Amazon EMR Geospatial Data Analysis using Python on AWS Cloud9 Geospatial Data Analysis using SageMaker Using Amazon QuickSight to Visualize Geospatial Data Open Data on AWS Leveraging OpenStreetMap on AWS Map and Feature Services on AWS Satellite Imagery on AWS

English [en] · PDF · 18.2MB · 2023 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/Sandy Ryza & Uri Laserson & Sean Owen & and Josh Wills - Advanced Analytics with Spark - Sandy Ryza, Uri Laserson, Sean.rtf

Advanced Analytics with Spark - Sandy Ryza, Uri Laserson, Sean Sandy Ryza & Uri Laserson & Sean Owen & and Josh Wills

RTF · 5.5MB · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

nexusstc/Advanced Analytics with PySpark: Patterns for Learning from Data at Scale Using Python and Spark/a87eb040e66d5e8e5e3ed2e6de429112.pdf

Advanced analytics with PySpark : patterns for learning from data at Scale using Python and Spark Akash Tandon, Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills O'Reilly Media, Incorporated, May 17, 2022

The amount of data being generated today is staggering and growing. Apache Spark has emerged as the de facto tool to analyze big data and is now a critical part of the data science toolbox. Updated for Spark 3.0, this practical guide brings together Spark, statistical methods, and real-world datasets to teach you how to approach analytics problems using PySpark, Spark's Python API, and other best practices in Spark programming. Data scientists Akash Tandon, Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills offer an introduction to the Spark ecosystem, then dive into patterns that apply common techniques-including classification, clustering, collaborative filtering, and anomaly detection, to fields such as genomics, security, and finance. This updated edition also covers NLP and image processing. If you have a basic understanding of machine learning and statistics and you program in Python, this book will get you started with large-scale data analysis. • Familiarize yourself with Spark's programming model and ecosystem • Learn general approaches in data science • Examine complete implementations that analyze large public datasets • Discover which machine learning tools make sense for particular problems • Explore code that can be adapted to many uses

English [en] · PDF · 10.2MB · 2022 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/zlib · Save

lgli/AWS Certified Advanced Networking – Specialty (ANS-C01) Certification Guide - Tim McConnaughy & Steve McNutt & Christopher Miles.epub

AWS Certified Advanced Networking – Specialty (ANS-C01) Certification Guide: A pragmatic guide to acing the AWS ANS-C01 exam Tim McConnaughy & Steve McNutt & Christopher Miles Packt Publishing, 2025

Become proficient in networking in AWS and prepare to confidently pass the certification exam by using exam-specific study materials Purchase of this book unlocks access to web-based exam prep resources including practice tests, flashcards, exam tips, and the eBook PDF Key Features Get a thorough understanding of the latest AWS ANS-C01 exam objectives Explore AWS networking options, services, features, and their relationships Prepare for exam success with mock exams that correctly reflect exam-level difficulty Book Description The AWS Certified Advanced Networking – Specialty certification exam focuses on leveraging AWS services alongside industry standards to create secure, resilient, and scalable cloud networks. Written by industry experts with decades of experience in the field, this comprehensive exam guide will enable you to transform into an AWS networking expert, going beyond the ANS-C01 exam blueprint to maximize your impact in the field. You’ll learn all about intricate AWS networking options and services with clear explanations, detailed diagrams, and practice questions in each chapter. The chapters help you gain hands-on experience with essential components, such as VPC networking, AWS Direct Connect, Route 53, security frameworks, and infrastructure as code. With access to mock exams, interactive flashcards, and invaluable exam tips, you have everything you need to excel in the AWS ANS-C01 exam. This book not only prepares you to confidently take the exam, but also deepens your understanding and provides practical insights that are vital for a successful career in AWS cloud networking. By the end of this exam guide, you’ll be thoroughly trained to take the AWS ANS-C01 exam and efficiently design and maintain network architectures across a wide range of AWS services. What you will learn Build resilient, scalable networks by using AWS network constructs Integrate hybrid connectivity models by using AWS and third-party architecture Assess the various load balancing and DNS options that AWS provides Examine security frameworks in AWS and the constructs that support secure connectivity Utilize AWS monitoring tools to optimize and diagnose network connectivity Comprehend AWS ANS-C01 exam questions to maximize your chances of answering correctly Who this book is for This book is for professional networkers who want to achieve certification in AWS cloud networking. Anyone currently working as a network engineer or architect, as well as individuals looking to transition into AWS networking will also find this book valuable. A foundational understanding of basic network concepts and an in-depth knowledge of the cloud service connectivity model, specifically the distinctions between IaaS, PaaS, and SaaS services. Table of Contents Advanced VPC Networking VPC Traffic and Performance Monitoring Networking Across Multiple AWS Accounts AWS Direct Connect Hybrid Networking with AWS Transit Gateway Connecting Third-Party Networks to AWS AWS Route 53: Basics AWS Route 53: Advanced AWS Elastic Load Balancing AWS CDN and Global Traffic Management Security Framework AWS Security Services Infrastructure as Code Data Analytics and Optimization Conclusion

English [en] · EPUB · 37.6MB · 2025 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/zlib · Save

lgli/Fedler, Steve [Fedler, Steve] - AWS: Amazon Web Services. A Complete Guide from Beginners to Advanced. (2020, ).epub

AWS: Amazon Web Services. A Complete Guide from Beginners to Advanced. Fedler, Steve [Fedler, Steve] 2020

English [en] · EPUB · 1.3MB · 2020 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

upload/newsarch_ebooks/2020/06/01/Machine Learning_ Introduction to Supervised and Unsupervised Learning Algorithms with Real-World Applications (Advanced Data Analytics Book 1).azw3

Machine Learning_ Introduction to Supervised and Unsupervised Learning Algorithms with Real-World Applications (Advanced Data Analytics Book 1).azw3 Maynard, Morgan

AZW3 · 6.4MB · 📗 Book (unknown) · 🚀/upload · Save

lgli/Fedler, Steve - AWS: Amazon Web Services. A Complete Guide from Beginners to Advanced. (2020, ).pdf

AWS: Amazon Web Services. A Complete Guide from Beginners to Advanced. Fedler, Steve 2020

English [en] · PDF · 1.0MB · 2020 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

lgli/Steve Fedler [Fedler, Steve] - AWS: Amazon Web Services. A Complete Guide from Beginners to Advanced. (2020, ).mobi

AWS: Amazon Web Services. A Complete Guide from Beginners to Advanced. Steve Fedler [Fedler, Steve] 2020

English [en] · MOBI · 2.1MB · 2020 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

lgli/Steve Fedler [Fedler, Steve] - AWS: Amazon Web Services. A Complete Guide from Beginners to Advanced. (2020, ).azw3

AWS: Amazon Web Services. A Complete Guide from Beginners to Advanced. Steve Fedler [Fedler, Steve] 2020

English [en] · AZW3 · 1.4MB · 2020 · 📘 Book (non-fiction) · 🚀/lgli/zlib · Save

upload/newsarch_ebooks/2023/09/29/AWS Certified Advanced Networking Study Guide - Todd Montgomery.epub

AWS CERTIFIED ADVANCED NETWORKING STUDY GUIDE : spe cialty (ans-c01) exam 2nd edition Todd Montgomery Sybex; 2 edition, Second Edition, 2023

The latest edition of the official study guide for the AWS Advanced Networking certification specialty exam The newly revised second edition of the AWS Certified Advanced Networking Study Guide: Specialty (ANS-C01) Exam delivers an expert review of Amazon Web Services Networking fundamentals as they relate to the ANS-C01 exam. You’ll find detailed explanations of critical exam topics combined with real-world scenarios that will help you build the robust knowledge base you need for the test―and to succeed in the field as an AWS Certified Networking specialist. Learn about the design, implementation and deployment of AWS cloud-based Networking solutions, core services implementation, AWS service architecture design and maintenance (including architectural best practices), monitoring, Hybrid networks, security, compliance, governance, and network automation. The book also offers one year of free access to Sybex’s online interactive learning environment and expert study tools, featuring flashcards, a glossary of useful terms, chapter tests, practice exams, and a test bank to help you keep track of your progress and measure your exam readiness. The coveted AWS Advanced Networking credential proves your skills with Amazon Web Services and hybrid IT network architectures at scale. It assesses your ability to apply deep technical knowledge to the design and implementation of AWS Networking services. This book provides you with comprehensive review and practice opportunities so you can succeed on the challenging ANS-C01 exam the first time around. It also offers: Coverage of all relevant exam domains and competencies Explanations of how to apply the AWS skills discussed within to the real world in the context of an AWS Certified Networking-related career Complimentary access to the practical Sybex online learning environment, complete with practice exams, flashcards, a glossary, and test bank AWS certification proves to potential employers that you have the knowledge and practical skills you need to deliver forward-looking, resilient, cloud-based solutions. The AWS Certified Advanced Networking Study Guide: Specialty (ANS-C01) Exam, 2nd Edition , is your ticket to the next big step in your career.

English [en] · EPUB · 29.0MB · 2023 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/nexusstc/upload/zlib · Save

lgli/1804614424 .pdf

Data Engineering with AWS: Acquire the skills to design and build AWS-based data transformation pipelines like a pro - Second Edition Gareth Eagar Packt Publishing Limited, Expert Insight, 2, 2023

Looking to revolutionize your data transformation game with AWS? Look no further! From strong foundations to hands-on building of data engineering pipelines, our expert-led manual has got you covered. Key Features Delve into robust AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines Stay up to date with a comprehensive revised chapter on Data Governance Build modern data platforms with a new section covering transactional data lakes and data mesh Book Description This book, authored by a seasoned Senior Data Architect with 25 years of experience, aims to help you achieve proficiency in using the AWS ecosystem for data engineering. This revised edition provides updates in every chapter to cover the latest AWS services and features, takes a refreshed look at data governance, and includes a brand-new section on building modern data platforms which covers; implementing a data mesh approach, open-table formats (such as Apache Iceberg), and using DataOps for automation and observability. You'll begin by reviewing the key concepts and essential AWS tools in a data engineer's toolkit and getting acquainted with modern data management approaches. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how that transformed data is used by various data consumers. You’ll learn how to ensure strong data governance, and about populating data marts and data warehouses along with how a data lakehouse fits into the picture. After that, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. Then, you'll explore how the power of machine learning and artificial intelligence can be used to draw new insights from data. In the final chapters, you'll discover transactional data lakes, data meshes, and how to build a cutting-edge data platform on AWS. By the end of this AWS book, you'll be able to execute data engineering tasks and implement a data pipeline on AWS like a pro! What you will learn Seamlessly ingest streaming data with Amazon Kinesis Data Firehose Optimize, denormalize, and join datasets with AWS Glue Studio Use Amazon S3 events to trigger a Lambda process to transform a file Load data into a Redshift data warehouse and run queries with ease Visualize and explore data using Amazon QuickSight Extract sentiment data from a dataset using Amazon Comprehend Build transactional data lakes using Apache Iceberg with Amazon Athena Learn how a data mesh approach can be implemented on AWS Who this book is for This book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts, while gaining practical experience with common data engineering services on AWS, will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book, but it’s not a prerequisite. Familiarity with the AWS console and core services will also help you follow along. Table of Contents An Introduction to Data Engineering Data Management Architectures for Analytics The AWS Data Engineer’s Toolkit Data Governance, Security, and Cataloging Architecting Data Engineering Pipelines Ingesting Batch and Streaming Data Transforming Data to Optimize for Analytics Identifying and Enabling Data Consumers A Deeper Dive into Data Marts and Amazon Redshift Orchestrating the Data Pipeline (N.B. Please use the Look Inside option to see further chapters)

English [en] · PDF · 31.3MB · 2023 · 📘 Book (non-fiction) · 🚀/lgli/lgrs/zlib · Save

upload/misc/ThoseBooks/Computers & Technology/Databases & Big Data/Advanced Analytics with R and Tableau Advanced analytics using data classification, unsupervised learning and data visualization (9781786460110, 2017)/9781786460110(1).epub

Advanced Analytics with R and Tableau : Leverage the Power of Advanced Analytics and Predictive Modeling in Tableau Using the Statistical Powers of R Stirrup, Jen & Ramos, Ruben Oliva Packt Publishing, Limited, Packt Publishing, Birmingham, UK, 2017

Leverage the power of advanced analytics and predictive modeling in Tableau using the statistical powers of RAbout This BookA comprehensive guide that will bring out the creativity in you to visualize the results of complex calculations using Tableau and RCombine Tableau analytics and visualization with the power of R using this step-by-step guideWondering how R can be used with Tableau? This book is your one-stop solution.Who This Book Is ForThis book will appeal to Tableau users who want to go beyond the Tableau interface and deploy the full potential of Tableau, by using R to perform advanced analytics with Tableau.A basic familiarity with R is useful but not compulsory, as the book will start off with concrete examples of R and will move quickly into more advanced spheres of analytics using online data sources to support hands-on learning. Those R developers who want to integrate R in Tableau will also benefit from this book.What You Will LearnIntegrate Tableau's analytics with the industry-standard, statistical prowess of R.Make R function calls in Tableau, and visualize R functions with Tableau using RServe.Use the CRISP-DM methodology to create a roadmap for analytics investigations.Implement various supervised and unsupervised learning algorithms in R to return values to Tableau.Make quick, cogent, and data-driven decisions for your business using advanced analytical techniques such as forecasting, predictions, association rules, clustering, classification, and other advanced Tableau/R calculated field functions.In DetailTableau and R offer accessible analytics by allowing a combination of easy-to-use data visualization along with industry-standard, robust statistical computation.Moving from data visualization into deeper, more advanced analytics? This book will intensify data skills for data viz-savvy users who want to move into analytics and data science in order to enhance their businesses by harnessing the analytical power of R and the stunning visualization capabilities of Tableau. Readers will come across a wide range of machine learning algorithms and learn how descriptive, prescriptive, predictive, and visually appealing analytical solutions can be designed with R and Tableau. In order to maximize learning, hands-on examples will ease the transition from being a data-savvy user to a data analyst using sound statistical tools to perform advanced analytics.By the end of this book, you will get to grips with advanced calculations in R and Tableau for analytics and prediction with the help of use cases and hands-on examples.Style and approachTableau (uniquely) offers excellent visualization combined with advanced analytics; R is at the pinnacle of statistical computational languages. When you want to move from one view of data to another, backed up by complex computations, the combination of R and Tableau makes the perfect solution. This example-rich guide will teach you how to combine these two to perform advanced analytics by integrating Tableau with R and create beautiful data visualizations.

English [en] · EPUB · 3.3MB · 2017 · 📗 Book (unknown) · 🚀/upload/zlib · Save