| business intelligence engineer amazon: AWS certification guide - AWS Certified DevOps Engineer - Professional Cybellium Ltd, AWS Certification Guide - AWS Certified DevOps Engineer – Professional Master the Art of AWS DevOps at a Professional Level Embark on a comprehensive journey to mastering DevOps practices in the AWS ecosystem with this definitive guide for the AWS Certified DevOps Engineer – Professional certification. Tailored for DevOps professionals aiming to validate their expertise, this book is an invaluable resource for mastering the blend of operations and development on AWS. Within These Pages, You'll Discover: Advanced DevOps Techniques: Deep dive into the advanced practices of AWS DevOps, from infrastructure as code to automated scaling and management. Comprehensive Coverage of AWS Services: Explore the full range of AWS services relevant to DevOps, including their integration and optimization for efficient workflows. Practical, Real-World Scenarios: Engage with detailed case studies and practical examples that demonstrate effective DevOps strategies in action on AWS. Focused Exam Preparation: Get a thorough understanding of the exam structure, with in-depth chapters aligned with each domain of the certification exam, complemented by targeted practice questions. Written by a DevOps Veteran Authored by an experienced AWS DevOps Engineer, this guide marries practical field expertise with a deep understanding of AWS services, offering readers insider insights and proven strategies. Your Comprehensive Guide to DevOps Certification Whether you’re an experienced DevOps professional or looking to take your skills to the next level, this book is your comprehensive companion, guiding you through the complexities of AWS DevOps and preparing you for the Professional certification exam. Elevate Your DevOps Skills Go beyond the basics and gain a profound, practical understanding of DevOps practices in the AWS environment. This guide is more than a certification prep book; it's a blueprint for excelling in AWS DevOps at a professional level. Begin Your Advanced DevOps Journey Embark on your path to becoming a certified AWS DevOps Engineer – Professional. With this guide, you're not just preparing for an exam; you're advancing your career in the fast-evolving field of AWS DevOps. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com |
| business intelligence engineer amazon: AWS Certified Enterprise IT Engineer Cybellium, Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| business intelligence engineer amazon: Data Engineering with AWS Gareth Eagar, 2021-12-29 The missing expert-led manual for the AWS ecosystem — go from foundations to building data engineering pipelines effortlessly Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Learn about common data architectures and modern approaches to generating value from big data Explore AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines Learn how to architect and implement data lakes and data lakehouses for big data analytics from a data lakes expert Book DescriptionWritten by a Senior Data Architect with over twenty-five years of experience in the business, Data Engineering for AWS is a book whose sole aim is to make you proficient in using the AWS ecosystem. Using a thorough and hands-on approach to data, this book will give aspiring and new data engineers a solid theoretical and practical foundation to succeed with AWS. As you progress, you’ll be taken through the services and the skills you need to architect and implement data pipelines on AWS. You'll begin by reviewing important data engineering concepts and some of the core AWS services that form a part of the data engineer's toolkit. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how the transformed data is used by various data consumers. You’ll also learn about populating data marts and data warehouses along with how a data lakehouse fits into the picture. Later, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. In the final chapters, you'll understand how the power of machine learning and artificial intelligence can be used to draw new insights from data. By the end of this AWS book, you'll be able to carry out data engineering tasks and implement a data pipeline on AWS independently.What you will learn Understand data engineering concepts and emerging technologies 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 Run complex SQL queries on data lake data using Amazon Athena Load data into a Redshift data warehouse and run queries Create a visualization of your data using Amazon QuickSight Extract sentiment data from a dataset using Amazon Comprehend 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. |
| business intelligence engineer amazon: AWS Cloud Engineer Guide Sizwe Molefe, 2024-09-27 DESCRIPTION Cloud computing provides a more efficient, reliable, secure, and cost-effective way to run applications. Cloud computing offers customers access to rapidly growing amounts of data storage and computation resources while centralizing IT operations in the cloud provider's datacenter or in colocation data centers. Understand AWS basics such as EC2, VPCs, S3, and IAM while learning to design secure and scalable cloud architectures. This book guides you through automating infrastructure with CloudFormation and exploring advanced topics like containers, continuous integration and continuous delivery (CI/CD) pipelines, and cloud migration. You will also discover serverless computing with Lambda, API Gateway, and DynamoDB, enabling you to build efficient, modern applications. With real-world examples and best practices, this resource helps you optimize your AWS environment for both performance and cost, ensuring you can build and maintain robust cloud solutions. By the end of this book, you will be able to confidently design, build, and operate scalable and secure cloud solutions on AWS. Gain the expertise to leverage the full potential of cloud computing and drive innovation in your organization. KEY FEATURES ● Learn about AWS cloud in-depth with real-world examples and scenarios. ● Expand your understanding of serverless and containerization compute technology on AWS. ● Explore API’s along with API Gateway and its different use cases. WHAT YOU WILL LEARN ● How to get started with and launch EC2 instances. ● Working with and simplifying VPC’s, security groups, and network access control lists on AWS. ● Learn how to secure your AWS environment through the use of IAM roles and policies. ● Learn how to build scalable and fault-tolerant database systems using AWS database services such as RDS and Aurora. ● Learn how to set up a CI/CD pipeline on AWS. WHO THIS BOOK IS FOR Whether you are a system administrator, cloud architect, solutions architect, cloud engineer, DevOps engineer, security engineer, or cloud professional, this book provides valuable insights and practical guidance to help you build and operate robust cloud solutions on AWS. TABLE OF CONTENTS 1. Creating an AWS Environment 2. Amazon Elastic Compute Cloud 3. Amazon Virtual Private Cloud 4. Amazon S3: Simple Storage Service 5. Amazon API Gateway 6. AWS Database Services 7. Elastic Load Balancing and Auto Scaling 8. Amazon Route 53 9. Decouple Applications 10. CloudFormation 11. AWS Monitoring 12. AWS Security and Encryption 13. AWS Containers 14. Automating Deployments with CI/CD in AWS 15. AWS Cloud Migrations |
| business intelligence engineer amazon: Introduction to Engineering Dr. Darius Gnanaraj Solomon, 2021-07-21 The purpose of this e-book is to provide details about different disciplines of engineering to students who are planning to pursue a degree in engineering and help them to decide on a career in engineering. This book explores different disciplines of engineering and provides a broad background in each area. Basic concepts, as well as a few applications related to the following disciplines of Engineering, are presented in this book: Automobile/Aerospace Engineering, Civil Engineering, Computer Science & Engineering, Electrical and Electronics Engineering, Mechanical Engineering, and Production/Manufacturing Engineering. Illustrations are provided using colorful photographs having rich information. Details related to career opportunities and opportunities for further higher studies are available in this book. The authors hope that this book will help aspiring students of engineering programs to choose the discipline of their choice. |
| business intelligence engineer amazon: ⬆️ Amazon Web Services Certified (AWS Certified) Data Analytics Specialty (DAS-C01) Practice Tests Exams 83 Questions & No Answers PDF Daniel Danielecki, 2023-11-01 ⚠️ IMPORTANT: This PDF is without correct answers marked; that way, you can print it out or solve it digitally before checking the correct answers. We also sell this PDF with answers marked; please check our Shop to find one. ⌛️ Short and to the point; why should you buy the PDF with these Practice Tests Exams: 1. Always happy to answer your questions on Google Play Books and outside :) 2. Failed? Please submit a screenshot of your exam result and request a refund; we'll always accept it. 3. Learn about topics, such as: - Active Directory; - Amazon Athena; - Amazon Aurora; - Amazon CloudWatch; - Amazon DynamoDB; - Amazon Elastic Compute Cloud (Amazon EC2); - Amazon Elastic Map Reduce (Amazon EMR); - Apache Kafka; - Amazon Kinesis; - Amazon OpenSearch Service; - Amazon QuickSight; - Amazon Redshift; - Amazon Relational Database Service (Amazon RDS); - Amazon Simple Storage Service (Amazon S3); - Apache Spark; - AWS CloudFormation; - AWS Command Line Interface (AWS CLI); - AWS Glue; - AWS Identity and Access Management (AWS IAM); - AWS Key Management Service (AWS KMS); - AWS Lambda; - Extract, Transform, Load (ETL); - Hadoop Distributed File System (HDFS); - Input/Output operations Per Second (IOPS); - Virtual Private Clouds (VPC); - Much More! 4. Questions are similar to the actual exam, without duplications (like in other courses ;-)). 5. These tests are not an Amazon Web Services Certified (AWS Certified) Data Analytics Specialty (DAS-C01) Exam Dump. Some people use brain dumps or exam dumps, but that's absurd, which we don't practice. 6. 83 unique questions. |
| business intelligence engineer amazon: The Self-Taught Cloud Computing Engineer Dr. Logan Song, 2023-09-22 Transform into a cloud-savvy professional by mastering cloud technologies through hands-on projects and expert guidance, paving the way for a thriving cloud computing career Key Features Learn all about cloud computing at your own pace with this easy-to-follow guide Develop a well-rounded skill set, encompassing fundamentals, data, machine learning, and security Work on real-world industrial projects and business use cases, and chart a path for your personal cloud career advancement Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe Self-Taught Cloud Computing Engineer is a comprehensive guide to mastering cloud computing concepts by building a broad and deep cloud knowledge base, developing hands-on cloud skills, and achieving professional cloud certifications. Even if you’re a beginner with a basic understanding of computer hardware and software, this book serves as the means to transition into a cloud computing career. Starting with the Amazon cloud, you’ll explore the fundamental AWS cloud services, then progress to advanced AWS cloud services in the domains of data, machine learning, and security. Next, you’ll build proficiency in Microsoft Azure Cloud and Google Cloud Platform (GCP) by examining the common attributes of the three clouds while distinguishing their unique features. You’ll further enhance your skills through practical experience on these platforms with real-life cloud project implementations. Finally, you’ll find expert guidance on cloud certifications and career development. By the end of this cloud computing book, you’ll have become a cloud-savvy professional well-versed in AWS, Azure, and GCP, ready to pursue cloud certifications to validate your skills.What you will learn Develop the core skills needed to work with cloud computing platforms such as AWS, Azure, and GCP Gain proficiency in compute, storage, and networking services across multi-cloud and hybrid-cloud environments Integrate cloud databases, big data, and machine learning services in multi-cloud environments Design and develop data pipelines, encompassing data ingestion, storage, processing, and visualization in the clouds Implement machine learning pipelines in a multi-cloud environment Secure cloud infrastructure ecosystems with advanced cloud security services Who this book is for Whether you're new to cloud computing or a seasoned professional looking to expand your expertise, this book is for anyone in the information technology domain who aspires to thrive in the realm of cloud computing. With this comprehensive roadmap, you’ll have the tools to build a successful cloud computing career. |
| business intelligence engineer amazon: Sustainable Development Through Data Analytics and Innovation Jorge Marx Gómez, Lawal O. Yesufu, 2022-09-26 Sustainable development is based on the idea that societies should advance without compromising their future development requirements. This book explores how the application of data analytics and digital technologies can ensure that development changes are executed on the basis of factual data and information. It addresses how innovations that rely on digital technologies can support sustainable development across all sectors and all social, economic, and environmental aspects and help us achieve the Sustainable Development Goals (SDGs). The book also highlights techniques, processes, models, tools, and practices used to achieve sustainable development through data analysis. The various topics covered in this book are critically evaluated, not only theoretically, but also from an application perspective. It will be of interest to researchers and students, especially those in the fields of applied data analytics, business intelligence and knowledge management. |
| business intelligence engineer amazon: Infrastructure Monitoring with Amazon CloudWatch Ewere Diagboya, 2021-04-16 Explore real-world examples of issues with systems and find ways to resolve them using Amazon CloudWatch as a monitoring service Key FeaturesBecome well-versed with monitoring fundamentals such as understanding the building blocks and architecture of networkingLearn how to ensure your applications never face downtimeGet hands-on with observing serverless applications and servicesBook Description CloudWatch is Amazon's monitoring and observability service, designed to help those in the IT industry who are interested in optimizing resource utilization, visualizing operational health, and eventually increasing infrastructure performance. This book helps IT administrators, DevOps engineers, network engineers, and solutions architects to make optimum use of this cloud service for effective infrastructure productivity. You'll start with a brief introduction to monitoring and Amazon CloudWatch and its core functionalities. Next, you'll get to grips with CloudWatch features and their usability. Once the book has helped you develop your foundational knowledge of CloudWatch, you'll be able to build your practical skills in monitoring and alerting various Amazon Web Services, such as EC2, EBS, RDS, ECS, EKS, DynamoDB, AWS Lambda, and ELB, with the help of real-world use cases. As you progress, you'll also learn how to use CloudWatch to detect anomalous behavior, set alarms, visualize logs and metrics, define automated actions, and rapidly troubleshoot issues. Finally, the book will take you through monitoring AWS billing and costs. By the end of this book, you'll be capable of making decisions that enhance your infrastructure performance and maintain it at its peak. What you will learnUnderstand the meaning and importance of monitoringExplore the components of a basic monitoring systemUnderstand the functions of CloudWatch Logs, metrics, and dashboardsDiscover how to collect different types of metrics from EC2Configure Amazon EventBridge to integrate with different AWS servicesGet up to speed with the fundamentals of observability and the AWS services used for observabilityFind out about the role Infrastructure As Code (IaC) plays in monitoringGain insights into how billing works using different CloudWatch featuresWho this book is for This book is for developers, DevOps engineers, site reliability engineers, or any IT individual with hands-on intermediate-level experience in networking, cloud computing, and infrastructure management. A beginner-level understanding of AWS and application monitoring will also be helpful to grasp the concepts covered in the book more effectively. |
| business intelligence engineer amazon: Data Science on AWS Chris Fregly, Antje Barth, 2021-04-07 With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more |
| business intelligence engineer amazon: Business Intelligence Marie-Aude Aufaure, Esteban Zimányi, 2012-01-11 Business Intelligence (BI) promises an organization the capability of collecting and analyzing internal and external data to generate knowledge and value, providing decision support at the strategic, tactical, and operational levels. Business Intelligence is now impacted by the Big Data phenomena and the evolution of society and users, and needs to take into account high-level semantics, reasoning about unstructured and structured data, and to provide a simplified access and better understanding of diverse BI tools accessible trough mobile devices. In particular, BI applications must cope with additional heterogeneous (often Web-based) sources, e.g., from social networks, blogs, competitors’, suppliers’, or distributors’ data, governmental or NGO-based analysis and papers, or from research publications. The lectures held at the First European Business Intelligence Summer School (eBISS), which are presented here in an extended and refined format, cover not only established BI technologies like data warehouses, OLAP query processing, or performance issues, but extend into new aspects that are important in this new environment and for novel applications, e.g., semantic technologies, social network analysis and graphs, services, large-scale management, or collaborative decision making. Combining papers by leading researchers in the field, this volume will equip the reader with the state-of-the-art background necessary for inventing the future of BI. It will also provide the reader with an excellent basis and many pointers for further research in this growing field. |
| business intelligence engineer amazon: Citizenship and Ethics Thomas A. Bryer, So Hee Jeon, 2021-04-03 Scholarship is a multi-generational collective enterprise with a commitment to advancing knowledge, inspiring reflection, and facilitating stronger neighborhoods, cities and countries. This book explicitly adopts this lens as a recognition of the contributions of Prof. Terry Cooper to scholarship and practice, and as a mechanism to connect the past to the present and ultimately the future of scholarship in public ethics and citizen engagement. This “multi-generational” approach is designed to reveal the persistent and future ongoing need to engage as a scholarly and practitioner community with these questions. The book is broken into three main sections: citizenship and neighborhood governance, public service ethics and citizenship, and global explorations of citizenship and ethics. Unique in this collection is the explicit linkage across the main focus areas of citizenship and ethics, as well as the comparative and global context in which these issues are explored. Cases and data are examined from the United States, Chile, Thailand, India, China, Georgia, and Myanmar. Ultimately, it is made clear through each individual chapter and the collective whole that research on citizenship and ethics within public affairs and service has a rich history, remains critical to the strengthening of public institutions today, and will only increase in global significance in the years ahead. |
| business intelligence engineer amazon: Analytics Engineering with SQL and Dbt Rui Pedro Machado, Helder Russa, 2023-12-08 With the shift from data warehouses to data lakes, data now lands in repositories before it's been transformed, enabling engineers to model raw data into clean, well-defined datasets. dbt (data build tool) helps you take data further. This practical book shows data analysts, data engineers, BI developers, and data scientists how to create a true self-service transformation platform through the use of dynamic SQL. Authors Rui Machado from Monstarlab and Hélder Russa from Jumia show you how to quickly deliver new data products by focusing more on value delivery and less on architectural and engineering aspects. If you know your business well and have the technical skills to model raw data into clean, well-defined datasets, you'll learn how to design and deliver data models without any technical influence. With this book, you'll learn: What dbt is and how a dbt project is structured How dbt fits into the data engineering and analytics worlds How to collaborate on building data models The main tools and architectures for building useful, functional data models How to fit dbt into data warehousing and laking architecture How to build tests for data transformations |
| business intelligence engineer amazon: Proceedings of TEEM 2023 José Alexandre de Carvalho Gonçalves, |
| business intelligence engineer amazon: Competing on Analytics: Updated, with a New Introduction Thomas Davenport, Jeanne Harris, 2017-08-29 The New Edition of a Business Classic This landmark work, the first to introduce business leaders to analytics, reveals how analytics are rewriting the rules of competition. Updated with fresh content, Competing on Analytics provides the road map for becoming an analytical competitor, showing readers how to create new strategies for their organizations based on sophisticated analytics. Introducing a five-stage model of analytical competition, Davenport and Harris describe the typical behaviors, capabilities, and challenges of each stage. They explain how to assess your company’s capabilities and guide it toward the highest level of competition. With equal emphasis on two key resources, human and technological, this book reveals how even the most highly analytical companies can up their game. With an emphasis on predictive, prescriptive, and autonomous analytics for marketing, supply chain, finance, M&A, operations, R&D, and HR, the book contains numerous new examples from different industries and business functions, such as Disney’s vacation experience, Google’s HR, UPS’s logistics, the Chicago Cubs’ training methods, and Firewire Surfboards’ customization. Additional new topics and research include: Data scientists and what they do Big data and the changes it has wrought Hadoop and other open-source software for managing and analyzing data Data products—new products and services based on data and analytics Machine learning and other AI technologies The Internet of Things and its implications New computing architectures, including cloud computing Embedding analytics within operational systems Visual analytics The business classic that turned a generation of leaders into analytical competitors, Competing on Analytics is the definitive guide for transforming your company’s fortunes in the age of analytics and big data. |
| business intelligence engineer amazon: Page's Engineering Weekly , 1914 |
| business intelligence engineer amazon: Business Intelligence with Databricks SQL Vihag Gupta, 2022-09-16 Master critical skills needed to deploy and use Databricks SQL and elevate your BI from the warehouse to the lakehouse with confidence Key FeaturesLearn about business intelligence on the lakehouse with features and functions of Databricks SQLMake the most of Databricks SQL by getting to grips with the enablers of its data warehousing capabilitiesA unique approach to teaching concepts and techniques with follow-along scenarios on real datasetsBook Description In this new era of data platform system design, data lakes and data warehouses are giving way to the lakehouse – a new type of data platform system that aims to unify all data analytics into a single platform. Databricks, with its Databricks SQL product suite, is the hottest lakehouse platform out there, harnessing the power of Apache Spark™, Delta Lake, and other innovations to enable data warehousing capabilities on the lakehouse with data lake economics. This book is a comprehensive hands-on guide that helps you explore all the advanced features, use cases, and technology components of Databricks SQL. You'll start with the lakehouse architecture fundamentals and understand how Databricks SQL fits into it. The book then shows you how to use the platform, from exploring data, executing queries, building reports, and using dashboards through to learning the administrative aspects of the lakehouse – data security, governance, and management of the computational power of the lakehouse. You'll also delve into the core technology enablers of Databricks SQL – Delta Lake and Photon. Finally, you'll get hands-on with advanced SQL commands for ingesting data and maintaining the lakehouse. By the end of this book, you'll have mastered Databricks SQL and be able to deploy and deliver fast, scalable business intelligence on the lakehouse. What you will learnUnderstand how Databricks SQL fits into the Databricks Lakehouse PlatformPerform everyday analytics with Databricks SQL Workbench and business intelligence toolsOrganize and catalog your data assetsProgram the data security model to protect and govern your dataTune SQL warehouses (computing clusters) for optimal query experienceTune the Delta Lake storage format for maximum query performanceDeliver extreme performance with the Photon query execution engineImplement advanced data ingestion patterns with Databricks SQLWho this book is for This book is for business intelligence practitioners, data warehouse administrators, and data engineers who are new to Databrick SQL and want to learn how to deliver high-quality insights unhindered by the scale of data or infrastructure. This book is also for anyone looking to study the advanced technologies that power Databricks SQL. Basic knowledge of data warehouses, SQL-based analytics, and ETL processes is recommended to effectively learn the concepts introduced in this book and appreciate the innovation behind the platform. |
| business intelligence engineer amazon: Mastering Project Discovery Elliot Bendoly, Daniel Bachrach, Kathy Koontz, Porter Schermerhorn, 2024-04-11 Introducing a comprehensive approach to invigorate project leadership, this book provides a framework – the OUtCoMES Cycle – for developing, managing, advancing, and optimizing engineering and analytics projects. All too often, issues of moral hazard and completion bias prevent engineering and analytics managers and team leaders from asking the critical question 'What’s the problem?', before committing time, energy, and resources to solve it. This book draws attention to the definition, structuring, option consideration and ultimately the addressing of the right problems, exploring the OUtCoMES Cycle framework that facilitates and energizes systematic thinking, knowledge sharing, and on-the-fly adjustment with an explicit focus on the maximization of value and ROI. Each chapter includes discussions and lessons in analytical and engineering problem identification, problem structuring, iterative problem development (mental and computational) and problem resolution, at least three embedded real-world case studies, and a closing 'Practitioner’s Recap' to contextualize key chapter takeaways. Written by a team of established academic scholars and practicing analysts and engineers, this is an accessible and culture-shifting action guide for instructors interested in training the next generation of project and analytics leaders, students of analytics and engineering, as well as practicing project leaders and principals. |
| business intelligence engineer amazon: Improving E-Commerce Web Applications Through Business Intelligence Techniques Sreedhar, G., 2018-02-02 As the Internet becomes increasingly interconnected with modern society, the transition to online business has developed into a prevalent form of commerce. While there exist various advantages and disadvantages to online business, it plays a major role in contemporary business methods. Improving E-Commerce Web Applications Through Business Intelligence Techniques provides emerging research on the core areas of e-commerce web applications. While highlighting the use of data mining, search engine optimization, and online marketing to advance online business, readers will learn how the role of online commerce is becoming more prevalent in modern business. This book is an important resource for vendors, website developers, online customers, and scholars seeking current research on the development and use of e-commerce. |
| business intelligence engineer amazon: Hands-On Artificial Intelligence on Amazon Web Services Subhashini Tripuraneni, Charles Song, 2019-10-04 Perform cloud-based machine learning and deep learning using Amazon Web Services such as SageMaker, Lex, Comprehend, Translate, and Polly Key FeaturesExplore popular machine learning and deep learning services with their underlying algorithmsDiscover readily available artificial intelligence(AI) APIs on AWS like Vision and Language ServicesDesign robust architectures to enable experimentation, extensibility, and maintainability of AI appsBook Description From data wrangling through to translating text, you can accomplish this and more with the artificial intelligence and machine learning services available on AWS. With this book, you’ll work through hands-on exercises and learn to use these services to solve real-world problems. You’ll even design, develop, monitor, and maintain machine and deep learning models on AWS. The book starts with an introduction to AI and its applications in different industries, along with an overview of AWS artificial intelligence and machine learning services. You’ll then get to grips with detecting and translating text with Amazon Rekognition and Amazon Translate. The book will assist you in performing speech-to-text with Amazon Transcribe and Amazon Polly. Later, you’ll discover the use of Amazon Comprehend for extracting information from text, and Amazon Lex for building voice chatbots. You will also understand the key capabilities of Amazon SageMaker such as wrangling big data, discovering topics in text collections, and classifying images. Finally, you’ll cover sales forecasting with deep learning and autoregression, before exploring the importance of a feedback loop in machine learning. By the end of this book, you will have the skills you need to implement AI in AWS through hands-on exercises that cover all aspects of the ML model life cycle. What you will learnGain useful insights into different machine and deep learning modelsBuild and deploy robust deep learning systems to productionTrain machine and deep learning models with diverse infrastructure specificationsScale AI apps without dealing with the complexity of managing the underlying infrastructureMonitor and Manage AI experiments efficientlyCreate AI apps using AWS pre-trained AI servicesWho this book is for This book is for data scientists, machine learning developers, deep learning researchers, and artificial intelligence enthusiasts who want to harness the power of AWS to implement powerful artificial intelligence solutions. A basic understanding of machine learning concepts is expected. |
| business intelligence engineer amazon: Engineering Analytics Luis Rabelo, Edgar Gutierrez-Franco, Alfonso Sarmiento, Christopher Mejía-Argueta, 2021-09-27 Engineering analytics is becoming a necessary skill for every engineer. Areas such as Operations Research, Simulation, and Machine Learning can be totally transformed through massive volumes of data. This book is intended to be an introduction to Engineering Analytics that can be used to improve performance tracking, customer segmentation for resource optimization, patterns and classification strategies, and logistics control towers. Basic methods in the areas of visual, descriptive, predictive, and prescriptive analytics and Big Data are introduced. Industrial case studies and example problem demonstrations are used throughout the book to reinforce the concepts and applications. The book goes on to cover visual analytics and its relationships, simulation from the respective dimensions and Machine Learning and Artificial Intelligence from different paradigms viewpoints. The book is intended for professionals wanting to work on analytical problems, for Engineering students, Researchers, Chief-Technology Officers, and Directors that work within the areas and fields of Industrial Engineering, Computer Science, Statistics, Electrical Engineering Operations Research, and Big Data. |
| business intelligence engineer amazon: Data Science mit AWS Chris Fregly, Antje Barth, 2022-04-13 Von der ersten Idee bis zur konkreten Anwendung: Ihre Data-Science-Projekte in der AWS-Cloud realisieren Der US-Besteller zu Amazon Web Services jetzt auf Deutsch Beschreibt alle wichtigen Konzepte und die wichtigsten AWS-Dienste mit vielen Beispielen aus der Praxis Deckt den kompletten End-to-End-Prozess von der Entwicklung der Modelle bis zum ihrem konkreten Einsatz ab Mit Best Practices für alle Aspekte der Modellerstellung einschließlich Training, Deployment, Sicherheit und MLOps Mit diesem Buch lernen Machine-Learning- und KI-Praktiker, wie sie erfolgreich Data-Science-Projekte mit Amazon Web Services erstellen und in den produktiven Einsatz bringen. Es bietet einen detaillierten Einblick in den KI- und Machine-Learning-Stack von Amazon, der Data Science, Data Engineering und Anwendungsentwicklung vereint. Chris Fregly und Antje Barth beschreiben verständlich und umfassend, wie Sie das breite Spektrum an AWS-Tools nutzbringend für Ihre ML-Projekte einsetzen. Der praxisorientierte Leitfaden zeigt Ihnen konkret, wie Sie ML-Pipelines in der Cloud erstellen und die Ergebnisse dann innerhalb von Minuten in Anwendungen integrieren. Sie erfahren, wie Sie alle Teilschritte eines Workflows zu einer wiederverwendbaren MLOps-Pipeline bündeln, und Sie lernen zahlreiche reale Use Cases zum Beispiel aus den Bereichen Natural Language Processing, Computer Vision oder Betrugserkennung kennen. Im gesamten Buch wird zudem erläutert, wie Sie Kosten senken und die Performance Ihrer Anwendungen optimieren können. |
| business intelligence engineer amazon: Utilizing Big Data Paradigms for Business Intelligence Darmont, Jérôme, Loudcher, Sabine, 2018-08-10 Because efficient compilation of information allows managers and business leaders to make the best decisions for the financial solvency of their organizations, data analysis is an important part of modern business administration. Understanding the use of analytics, reporting, and data mining in everyday business environments is imperative to the success of modern businesses. Utilizing Big Data Paradigms for Business Intelligence is a pivotal reference source that provides vital research on how to address the challenges of data extraction in business intelligence using the five “Vs” of big data: velocity, volume, value, variety, and veracity. This book is ideally designed for business analysts, investors, corporate managers, entrepreneurs, and researchers in the fields of computer science, data science, and business intelligence. |
| business intelligence engineer amazon: Driving Digital Transformation through Data and AI Alexander Borek, Nadine Prill, 2020-11-03 Leading tech companies such as Netflix, Amazon and Uber use data science and machine learning at scale in their core business processes, whereas most traditional companies struggle to expand their machine learning projects beyond a small pilot scope. This book enables organizations to truly embrace the benefits of digital transformation by anchoring data and AI products at the core of their business. It provides executives with the essential tools and concepts to establish a data and AI portfolio strategy as well as the organizational setup and agile processes that are required to deliver machine learning products at scale. Key consideration is given to advancing the data architecture and governance, balancing stakeholder needs and breaking organizational silos through new ways of working. Each chapter includes templates, common pitfalls and global case studies covering industries such as insurance, fashion, consumer goods, finance, manufacturing and automotive. Covering a holistic perspective on strategy, technology, product and company culture, Driving Digital Transformation through Data and AI guides the organizational transformation required to get ahead in the age of AI. |
| business intelligence engineer amazon: Computerworld , 2003-10-06 For more than 40 years, Computerworld has been the leading source of technology news and information for IT influencers worldwide. Computerworld's award-winning Web site (Computerworld.com), twice-monthly publication, focused conference series and custom research form the hub of the world's largest global IT media network. |
| business intelligence engineer amazon: Research Handbook on E-Government Welch, Eric W., 2021-10-15 E-government is an increasingly well-established and wide-ranging field, in which there has been an explosion of new technologies, applications, and data resulting in new challenges and opportunities for e-government research and practice. This Research Handbook advances research in the field of e-government by first recognizing its roots and documenting its growth and progress. It investigates the advent and implications of new technologies, and structures the content around core topics of service, management, engagement and access. Two additional sections examine the role of e-government in developing countries and smart cities. |
| business intelligence engineer amazon: Data Science and Analytics Strategy Kailash Awati, Alexander Scriven, 2023-04-05 This book describes how to establish data science and analytics capabilities in organisations using Emergent Design, an evolutionary approach that increases the chances of successful outcomes while minimising upfront investment. Based on their experiences and those of a number of data leaders, the authors provide actionable advice on data technologies, processes, and governance structures so that readers can make choices that are appropriate to their organisational contexts and requirements. The book blends academic research on organisational change and data science processes with real-world stories from experienced data analytics leaders, focusing on the practical aspects of setting up a data capability. In addition to a detailed coverage of capability, culture, and technology choices, a unique feature of the book is its treatment of emerging issues such as data ethics and algorithmic fairness. Data Science and Analytics Strategy: An Emergent Design Approach has been written for professionals who are looking to build data science and analytics capabilities within their organisations as well as those who wish to expand their knowledge and advance their careers in the data space. Providing deep insights into the intersection between data science and business, this guide will help professionals understand how to help their organisations reap the benefits offered by data. Most importantly, readers will learn how to build a fit-for-purpose data science capability in a manner that avoids the most common pitfalls. |
| business intelligence engineer amazon: Leadership Robert N. Lussier, Christopher F. Achua, 2022-02-15 Leadership: Theory, Application, and Skill Development offers an applied introduction to leadership theories and concepts. Bestselling authors Robert N. Lussier and Christopher F. Achua use current, real-world examples and step-by-step behavioral models to help prepare readers for a wide range of leadership situations and challenges. The Seventh Edition equips students with the leadership skills they need to thrive in today′s business world with 23 new cases profiling a diverse group of leaders as well as new coverage of crisis leadership, servant leadership, social impact, and high-performing organizations. This title is accompanied by a complete teaching and learning package. |
| business intelligence engineer amazon: Fundamentals of Data Engineering Joe Reis, Matt Housley, 2022-06-22 Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, and governance that are critical in any data environment regardless of the underlying technology. This book will help you: Get a concise overview of the entire data engineering landscape Assess data engineering problems using an end-to-end framework of best practices Cut through marketing hype when choosing data technologies, architecture, and processes Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle |
| business intelligence engineer amazon: Azure Data Factory Cookbook Dmitry Foshin, Tonya Chernyshova, Dmitry Anoshin, Xenia Ireton, 2024-02-28 Data Engineers guide to solve real-world problems encountered while building and transforming data pipelines using Azure's data integration tool Key Features Solve real-world data problems and create data-driven workflows with ease using Azure Data Factory Build an ADF pipeline that operates on pre-built ML model and Azure AI Get up and running with Fabric Data Explorer and extend ADF with Logic Apps and Azure functions Book DescriptionThis new edition of the Azure Data Factory book, fully updated to reflect ADS V2, will help you get up and running by showing you how to create and execute your first job in ADF. There are updated and new recipes throughout the book based on developments happening in Azure Synapse, Deployment with Azure DevOps, and Azure Purview. The current edition also runs you through Fabric Data Factory, Data Explorer, and some industry-grade best practices with specific chapters on each. You’ll learn how to branch and chain activities, create custom activities, and schedule pipelines, as well as discover the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage. With practical recipes, you’ll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premises infrastructure with cloud-native tools to get relevant business insights. You'll familiarize yourself with the common errors that you may encounter while working with ADF and find out the solutions to them. You’ll also understand error messages and resolve problems in connectors and data flows with the debugging capabilities of ADF. By the end of this book, you’ll be able to use ADF with its latest advancements as the main ETL and orchestration tool for your data warehouse projects.What you will learn Build and Manage data pipelines with ease using the latest version of ADF Configure, load data, and operate data flows with Azure Synapse Get up and running with Fabric Data Factory Working with Azure Data Factory and Azure Purview Create big data pipelines using Databricks and Delta tables Integrate ADF with commonly used Azure services such as Azure ML, Azure Logic Apps, and Azure Functions Learn industry-grade best practices for using Azure Data Factory Who this book is for This book is for ETL developers, data warehouse and ETL architects, software professionals, and anyone else who wants to learn about the common and not-so-common challenges faced while developing traditional and hybrid ETL solutions using Microsoft's Azure Data Factory. You’ll also find this book useful if you are looking for recipes to improve or enhance your existing ETL pipelines. Basic knowledge of data warehousing is a prerequisite. |
| business intelligence engineer amazon: The Adaptable Degree Melanie Dreyer-Lude, 2024-09-20 This book utilized a mixed-methods research study of the career experiences of theatre graduates in the U.S. to provide data on employment patterns and job satisfaction. With a population of over 1,000 participants, this study examined where graduates were working, how their careers had changed over time, which skills acquired with their theatre degree were being used in current employment, and whether they believed their course of study was worth the financial investment, given their current circumstances. Evidence from this study revealed that a theatre degree provided many of the skills the employment market is currently seeking and that theatre graduates were gainfully employed in multiple sectors of the economy. This important data-based, field-specific information will aid chairs, deans, provosts, politicians, students and parents in deicision-making at a time when arts and humanities departments across the country are under the threat of elimination. |
| business intelligence engineer amazon: Python Business Intelligence Cookbook Robert Dempsey, 2015-12-22 Leverage the computational power of Python with more than 60 recipes that arm you with the required skills to make informed business decisions About This Book Want to minimize risk and optimize profits of your business? Learn to create efficient analytical reports with ease using this highly practical, easy-to-follow guide Learn to apply Python for business intelligence tasks—preparing, exploring, analyzing, visualizing and reporting—in order to make more informed business decisions using data at hand Learn to explore and analyze business data, and build business intelligence dashboards with the help of various insightful recipes Who This Book Is For This book is intended for data analysts, managers, and executives with a basic knowledge of Python, who now want to use Python for their BI tasks. If you have a good knowledge and understanding of BI applications and have a “working” system in place, this book will enhance your toolbox. What You Will Learn Install Anaconda, MongoDB, and everything you need to get started with your data analysis Prepare data for analysis by querying cleaning and standardizing data Explore your data by creating a Pandas data frame from MongoDB Gain powerful insights, both statistical and predictive, to make informed business decisions Visualize your data by building dashboards and generating reports Create a complete data processing and business intelligence system In Detail The amount of data produced by businesses and devices is going nowhere but up. In this scenario, the major advantage of Python is that it's a general-purpose language and gives you a lot of flexibility in data structures. Python is an excellent tool for more specialized analysis tasks, and is powered with related libraries to process data streams, to visualize datasets, and to carry out scientific calculations. Using Python for business intelligence (BI) can help you solve tricky problems in one go. Rather than spending day after day scouring Internet forums for “how-to” information, here you'll find more than 60 recipes that take you through the entire process of creating actionable intelligence from your raw data, no matter what shape or form it's in. Within the first 30 minutes of opening this book, you'll learn how to use the latest in Python and NoSQL databases to glean insights from data just waiting to be exploited. We'll begin with a quick-fire introduction to Python for BI and show you what problems Python solves. From there, we move on to working with a predefined data set to extract data as per business requirements, using the Pandas library and MongoDB as our storage engine. Next, we will analyze data and perform transformations for BI with Python. Through this, you will gather insightful data that will help you make informed decisions for your business. The final part of the book will show you the most important task of BI—visualizing data by building stunning dashboards using Matplotlib, PyTables, and iPython Notebook. Style and approach This is a step-by-step guide to help you prepare, explore, analyze and report data, written in a conversational tone to make it easy to grasp. Whether you're new to BI or are looking for a better way to work, you'll find the knowledge and skills here to get your job done efficiently. |
| business intelligence engineer amazon: The Business Model Innovation Playbook Gennaro Cuofano, 2019-11-19 Business model innovation is about increasing the success of an organization with existing products and technologies by crafting a compelling value proposition able to propel a new business model to scale up customers and create a lasting competitive advantage. And it all starts by mastering the key customers. - The importance of business model innovation - Business model innovation enables you to create competitive moats - A multi-faceted concept - Analysts use business models to produce financial analyses - Academics study business models for the sake of classifying things - Most people confuse business models for business plans - Startups confuse business models for monetization strategies - Business model innovation is an experimentation mindset for entrepreneurs - An entrepreneur is not a scientist - Business model innovation is at the same time a mindset, a framework and a set of tools for entrepreneurs - Myth one: the best product wins - Myth two: technology is what gives a competitive advantage - Myth three: business model innovation is just about how you make money - What kind of questions do you need to ask with business model innovation? - Paths toward business model innovation - Engineer an innovative business model from scratch - Find an innovative business model along the way - Use business model innovation as a survival mechanism - Business model innovation examples - Netflix business model innovation (case study) - Amazon business model innovation (case study) - Apple business model innovation (case study) - Google business model innovation (case study) - Facebook business model innovation (case study) - Is business model innovation for anyone? - Key takeaways |
| business intelligence engineer amazon: Mastering Business Intelligence with MicroStrategy Dmitry Anoshin, Himani Rana, Ning Ma, 2016-07-29 Build world-class enterprise Business Intelligence solutions with MicroStrategy 10 About This Book Fix the gap between BI tools and implementation/integration processes with big data and predictive analytics using this comprehensive guide to MicroStrategy 10 Highly practical, example-rich guide that lets you implement business intelligence with MicroStrategy 10 in your organization Create the best user experience with BI dashboards using MicroStrategy using this up-to-date, comprehensive guide Who This Book Is For This book is intended for BI, DWH, ETL developers, BI/DWH/Analytics managers, analysts, and business users who already have MicroStrategy in their organization and want to take it to the next level in order to increase performance and improve user experience. In addition, it helps the reader to migrate from MicroStrategy 9 to MicroStrategy 10 and to start using the new capabilities. What You Will Learn Explore various visualization techniques for presenting analyzed data Customize MicroStrategy in order to meet your business requirements Develop and design mobile dashboards Use the advanced techniques such as designing reports, documents and interactive dashboards for building dashboards Understand the concepts of data discovery and Desktop capabilities Explore the best practices for Microstrategy system administration Find and fix issues based on connections, environment or documents Integrate third party ESRI map tools with MicroStrategy to create geo based reports In Detail Business intelligence is becoming more important by the day, with cloud offerings and mobile devices gaining wider acceptance and achieving better market penetration. MicroStrategy Reporting Suite is an absolute leader in the BI market and offers rich capabilities from basic data visualizations to predictive analytics. It lets you various delivery methods such as the Web, desktops, and mobiles. Using real-world BI scenarios, this book helps you to implement Business Analytics solutions in big e-commerce companies. It kicks off with MicroStrategy 10 features and then covers schema design models and techniques. Building upon your existing knowledge, the book will teach you advanced techniques for building documents and dashboards. It further teaches various graphical techniques for presenting data for analysis using maps, graphs, and advanced charts. Although MicroStrategy has rich functionality, the book will show how to customize it in order to meet your business requirements. You will also become familiar with the native analytical functions that will help you to maximize the impact of BI solutions with powerful predictive analytics. Furthermore, the book will focus on MicroStrategy Mobile Analytics along with data discovery and desktop capabilities such as connecting various data sources and building interactive dashboards. The book will also uncover best practices, troubleshooting techniques for MicroStrategy system administration, and also security and authentication techniques. Lastly, you will learn to use Hadoop for MicroStrategy reporting. By the end of the book, you will become proficient in evaluating any BI software in order to choose the best one that meets all business requirements. Style and approach This book will be focusing on providing extensive guide to plan how to design and develop complex BI architecture for real world scenario, using Microstrategy 10, best practices and collected experience working with BI, predictive analytics, and Microstrategy and big data. |
| business intelligence engineer amazon: Effective Business Intelligence with QuickSight Rajesh Nadipalli, 2017-03-10 From data to actionable business insights using Amazon QuickSight! About This Book A practical hands-on guide to improving your business with the power of BI and Quicksight Immerse yourself with an end-to-end journey for effective analytics using QuickSight and related services Packed with real-world examples with Solution Architectures needed for a cloud-powered Business Intelligence service Who This Book Is For This book is for Business Intelligence architects, BI developers, Big Data architects, and IT executives who are looking to modernize their business intelligence architecture and deliver a fast, easy-to-use, cloud powered business intelligence service. What You Will Learn Steps to test drive QuickSight and see how it fits in AWS big data eco system Load data from various sources such as S3, RDS, Redshift, Athena, and SalesForce and visualize using QuickSight Understand how to prepare data using QuickSight without the need of an IT developer Build interactive charts, reports, dashboards, and storyboards using QuickSight Access QuickSight using the mobile application Architect and design for AWS Data Lake Solution, leveraging AWS hosted services Build a big data project with step-by-step instructions for data collection, cataloguing, and analysis Secure your data used for QuickSight from S3, RedShift, and RDS instances Manage users, access controls, and SPICE capacity In Detail Amazon QuickSight is the next-generation Business Intelligence (BI) cloud service that can help you build interactive visualizations on top of various data sources hosted on Amazon Cloud Infrastructure. QuickSight delivers responsive insights into big data and enables organizations to quickly democratize data visualizations and scale to hundreds of users at a fraction of the cost when compared to traditional BI tools. This book begins with an introduction to Amazon QuickSight, feature differentiators from traditional BI tools, and how it fits in the overall AWS big data ecosystem. With practical examples, you will find tips and techniques to load your data to AWS, prepare it, and finally visualize it using QuickSight. You will learn how to build interactive charts, reports, dashboards, and stories using QuickSight and share with others using just your browser and mobile app. The book also provides a blueprint to build a real-life big data project on top of AWS Data Lake Solution and demonstrates how to build a modern data lake on the cloud with governance, data catalog, and analysis. It reviews the current product shortcomings, features in the roadmap, and how to provide feedback to AWS. Grow your profits, improve your products, and beat your competitors. Style and approach This book takes a fast-paced, example-driven approach to demonstrate the power of QuickSight to improve your business' efficiency. Every chapter is accompanied with a use case that shows the practical implementation of the step being explained. |
| business intelligence engineer amazon: Operating AI Ulrika Jagare, 2022-04-19 A holistic and real-world approach to operationalizing artificial intelligence in your company In Operating AI, Director of Technology and Architecture at Ericsson AB, Ulrika Jägare, delivers an eye-opening new discussion of how to introduce your organization to artificial intelligence by balancing data engineering, model development, and AI operations. You'll learn the importance of embracing an AI operational mindset to successfully operate AI and lead AI initiatives through the entire lifecycle, including key areas such as; data mesh, data fabric, aspects of security, data privacy, data rights and IPR related to data and AI models. In the book, you’ll also discover: How to reduce the risk of entering bias in our artificial intelligence solutions and how to approach explainable AI (XAI) The importance of efficient and reproduceable data pipelines, including how to manage your company's data An operational perspective on the development of AI models using the MLOps (Machine Learning Operations) approach, including how to deploy, run and monitor models and ML pipelines in production using CI/CD/CT techniques, that generates value in the real world Key competences and toolsets in AI development, deployment and operations What to consider when operating different types of AI business models With a strong emphasis on deployment and operations of trustworthy and reliable AI solutions that operate well in the real world—and not just the lab—Operating AI is a must-read for business leaders looking for ways to operationalize an AI business model that actually makes money, from the concept phase to running in a live production environment. |
| business intelligence engineer amazon: The Nautical Magazine and Naval Chronicle , 1852 |
| business intelligence engineer amazon: Time Series Analysis on AWS Michaël Hoarau, 2022-02-28 Leverage AWS AI/ML managed services to generate value from your time series data Key FeaturesSolve modern time series analysis problems such as forecasting and anomaly detectionGain a solid understanding of AWS AI/ML managed services and apply them to your business problemsExplore different algorithms to build applications that leverage time series dataBook Description Being a business analyst and data scientist, you have to use many algorithms and approaches to prepare, process, and build ML-based applications by leveraging time series data, but you face common problems, such as not knowing which algorithm to choose or how to combine and interpret them. Amazon Web Services (AWS) provides numerous services to help you build applications fueled by artificial intelligence (AI) capabilities. This book helps you get to grips with three AWS AI/ML-managed services to enable you to deliver your desired business outcomes. The book begins with Amazon Forecast, where you'll discover how to use time series forecasting, leveraging sophisticated statistical and machine learning algorithms to deliver business outcomes accurately. You'll then learn to use Amazon Lookout for Equipment to build multivariate time series anomaly detection models geared toward industrial equipment and understand how it provides valuable insights to reinforce teams focused on predictive maintenance and predictive quality use cases. In the last chapters, you'll explore Amazon Lookout for Metrics, and automatically detect and diagnose outliers in your business and operational data. By the end of this AWS book, you'll have understood how to use the three AWS AI services effectively to perform time series analysis. What you will learnUnderstand how time series data differs from other types of dataExplore the key challenges that can be solved using time series dataForecast future values of business metrics using Amazon ForecastDetect anomalies and deliver forewarnings using Lookout for EquipmentDetect anomalies in business metrics using Amazon Lookout for MetricsVisualize your predictions to reduce the time to extract insightsWho this book is for If you're a data analyst, business analyst, or data scientist looking to analyze time series data effectively for solving business problems, this is the book for you. Basic statistics knowledge is assumed, but no machine learning knowledge is necessary. Prior experience with time series data and how it relates to various business problems will help you get the most out of this book. This guide will also help machine learning practitioners find new ways to leverage their skills to build effective time series-based applications. |
| business intelligence engineer amazon: Computerworld , 2003-12-08 For more than 40 years, Computerworld has been the leading source of technology news and information for IT influencers worldwide. Computerworld's award-winning Web site (Computerworld.com), twice-monthly publication, focused conference series and custom research form the hub of the world's largest global IT media network. |
| business intelligence engineer amazon: The Nautical Magazine and Naval Chronicle for 1852 Various, 2013-02-28 The 1852 Nautical Magazine reports on the Arctic, China and Turkey, British docks, collieries and shipbuilding, and the Great Exhibition. |
BUSINESS | English meaning - Cambridge Dictionary
BUSINESS definition: 1. the activity of buying and selling goods and services: 2. a particular company that buys and….
VENTURE | English meaning - Cambridge Dictionary
VENTURE definition: 1. a new activity, usually in business, that involves risk or uncertainty: 2. to risk going….
ENTERPRISE | English meaning - Cambridge Dictionary
ENTERPRISE definition: 1. an organization, especially a business, or a difficult and important plan, especially one that….
INCUMBENT | English meaning - Cambridge Dictionary
INCUMBENT definition: 1. officially having the named position: 2. to be necessary for someone: 3. the person who has or….
AD HOC | English meaning - Cambridge Dictionary
AD HOC definition: 1. made or happening only for a particular purpose or need, not planned before it happens: 2. made….
LEVERAGE | English meaning - Cambridge Dictionary
LEVERAGE definition: 1. the action or advantage of using a lever: 2. power to influence people and get the results you….
ENTREPRENEUR | English meaning - Cambridge Dictionary
ENTREPRENEUR definition: 1. someone who starts their own business, especially when this involves seeing a new opportunity….
CULTIVATE | English meaning - Cambridge Dictionary
CULTIVATE definition: 1. to prepare land and grow crops on it, or to grow a particular crop: 2. to try to develop and….
EQUITY | English meaning - Cambridge Dictionary
EQUITY definition: 1. the value of a company, divided into many equal parts owned by the shareholders, or one of the….
LIAISE | English meaning - Cambridge Dictionary
LIAISE definition: 1. to speak to people in other organizations, etc. in order to work with them or exchange….
BUSINESS | English meaning - Cambridge Dictionary
BUSINESS definition: 1. the activity of buying and selling goods and services: 2. a particular company that buys and….
VENTURE | English meaning - Cambridge Dictionary
VENTURE definition: 1. a new activity, usually in business, that involves risk or uncertainty: 2. to risk going….
ENTERPRISE | English meaning - Cambridge Dictionary
ENTERPRISE definition: 1. an organization, especially a business, or a difficult and important plan, especially one that….
INCUMBENT | English meaning - Cambridge Dictionary
INCUMBENT definition: 1. officially having the named position: 2. to be necessary for someone: 3. the person who has or….
AD HOC | English meaning - Cambridge Dictionary
AD HOC definition: 1. made or happening only for a particular purpose or need, not planned before it happens: 2. made….
LEVERAGE | English meaning - Cambridge Dictionary
LEVERAGE definition: 1. the action or advantage of using a lever: 2. power to influence people and get the results you….
ENTREPRENEUR | English meaning - Cambridge Dictionary
ENTREPRENEUR definition: 1. someone who starts their own business, especially when this involves seeing a new opportunity….
CULTIVATE | English meaning - Cambridge Dictionary
CULTIVATE definition: 1. to prepare land and grow crops on it, or to grow a particular crop: 2. to try to develop and….
EQUITY | English meaning - Cambridge Dictionary
EQUITY definition: 1. the value of a company, divided into many equal parts owned by the shareholders, or one of the….
LIAISE | English meaning - Cambridge Dictionary
LIAISE definition: 1. to speak to people in other organizations, etc. in order to work with them or exchange….
