| data engineering roadmap 2023: DATA ENGINEERING IN THE AGE OF AI GENERATIVE MODELS AND DEEP LEARNING UNLEASHED Siddharth Konkimalla, MANIKANTH SARISA, MOHIT SURENDER REDDY, SANJAY BAUSKAR, .The advances in data engineering technologies, including big data infrastructure, knowledge graphs, and mechanism design, will have a long-lasting impact on artificial intelligence (AI) research and development. This paper introduces data engineering in AI with a focus on the basic concepts, applications, and emerging frontiers. As a new research field, most data engineering in AI is yet to be properly defined, and there are abundant problems and applications to be explored. The primary purpose of this paper is to expose the AI community to this shining star of data science, stimulate AI researchers to think differently and form a roadmap of data engineering for AI. Since this is primarily an informal essay rather than an academic paper, its coverage is limited. The vast majority of the stimulating studies and ongoing projects are not mentioned in the paper. |
| data engineering roadmap 2023: Data Engineering with Python Paul Crickard, 2020-10-23 Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projects Key Features Become well-versed in data architectures, data preparation, and data optimization skills with the help of practical examples Design data models and learn how to extract, transform, and load (ETL) data using Python Schedule, automate, and monitor complex data pipelines in production Book DescriptionData engineering provides the foundation for data science and analytics, and forms an important part of all businesses. This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python. The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You’ll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You’ll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you’ll build architectures on which you’ll learn how to deploy data pipelines. By the end of this Python book, you’ll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production.What you will learn Understand how data engineering supports data science workflows Discover how to extract data from files and databases and then clean, transform, and enrich it Configure processors for handling different file formats as well as both relational and NoSQL databases Find out how to implement a data pipeline and dashboard to visualize results Use staging and validation to check data before landing in the warehouse Build real-time pipelines with staging areas that perform validation and handle failures Get to grips with deploying pipelines in the production environment Who this book is for This book is for data analysts, ETL developers, and anyone looking to get started with or transition to the field of data engineering or refresh their knowledge of data engineering using Python. This book will also be useful for students planning to build a career in data engineering or IT professionals preparing for a transition. No previous knowledge of data engineering is required. |
| data engineering roadmap 2023: Data Observability for Data Engineering Michele Pinto, Sammy El Khammal, 2023-12-29 Discover actionable steps to maintain healthy data pipelines to promote data observability within your teams with this essential guide to elevating data engineering practices Key Features Learn how to monitor your data pipelines in a scalable way Apply real-life use cases and projects to gain hands-on experience in implementing data observability Instil trust in your pipelines among data producers and consumers alike Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn the age of information, strategic management of data is critical to organizational success. The constant challenge lies in maintaining data accuracy and preventing data pipelines from breaking. Data Observability for Data Engineering is your definitive guide to implementing data observability successfully in your organization. This book unveils the power of data observability, a fusion of techniques and methods that allow you to monitor and validate the health of your data. You’ll see how it builds on data quality monitoring and understand its significance from the data engineering perspective. Once you're familiar with the techniques and elements of data observability, you'll get hands-on with a practical Python project to reinforce what you've learned. Toward the end of the book, you’ll apply your expertise to explore diverse use cases and experiment with projects to seamlessly implement data observability in your organization. Equipped with the mastery of data observability intricacies, you’ll be able to make your organization future-ready and resilient and never worry about the quality of your data pipelines again.What you will learn Implement a data observability approach to enhance the quality of data pipelines Collect and analyze key metrics through coding examples Apply monkey patching in a Python module Manage the costs and risks associated with your data pipeline Understand the main techniques for collecting observability metrics Implement monitoring techniques for analytics pipelines in production Build and maintain a statistics engine continuously Who this book is for This book is for data engineers, data architects, data analysts, and data scientists who have encountered issues with broken data pipelines or dashboards. Organizations seeking to adopt data observability practices and managers responsible for data quality and processes will find this book especially useful to increase the confidence of data consumers and raise awareness among producers regarding their data pipelines. |
| data engineering roadmap 2023: Advances in Model and Data Engineering in the Digitalization Era Mohamed Mosbah, |
| data engineering roadmap 2023: Data Engineering with Apache Spark, Delta Lake, and Lakehouse Manoj Kukreja, Danil Zburivsky, 2021-10-22 Understand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big data Key FeaturesBecome well-versed with the core concepts of Apache Spark and Delta Lake for building data platformsLearn how to ingest, process, and analyze data that can be later used for training machine learning modelsUnderstand how to operationalize data models in production using curated dataBook Description In the world of ever-changing data and schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on. Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. You'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. Once you've explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using Delta Lake. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. Finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way. By the end of this data engineering book, you'll know how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks. What you will learnDiscover the challenges you may face in the data engineering worldAdd ACID transactions to Apache Spark using Delta LakeUnderstand effective design strategies to build enterprise-grade data lakesExplore architectural and design patterns for building efficient data ingestion pipelinesOrchestrate a data pipeline for preprocessing data using Apache Spark and Delta Lake APIsAutomate deployment and monitoring of data pipelines in productionGet to grips with securing, monitoring, and managing data pipelines models efficientlyWho this book is for This book is for aspiring data engineers and data analysts who are new to the world of data engineering and are looking for a practical guide to building scalable data platforms. If you already work with PySpark and want to use Delta Lake for data engineering, you'll find this book useful. Basic knowledge of Python, Spark, and SQL is expected. |
| data engineering roadmap 2023: Engineering MLOps Emmanuel Raj, 2021-04-19 Get up and running with machine learning life cycle management and implement MLOps in your organization Key FeaturesBecome well-versed with MLOps techniques to monitor the quality of machine learning models in productionExplore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed modelsPerform CI/CD to automate new implementations in ML pipelinesBook Description Engineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production. The book begins by familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then you'll then move on to explore options for serializing and packaging ML models post-training to deploy them to facilitate machine learning inference, model interoperability, and end-to-end model traceability. You'll learn how to build ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitor pipelines to systematically build, deploy, monitor, and govern ML solutions for businesses and industries. Finally, you'll apply the knowledge you've gained to build real-world projects. By the end of this ML book, you'll have a 360-degree view of MLOps and be ready to implement MLOps in your organization. What you will learnFormulate data governance strategies and pipelines for ML training and deploymentGet to grips with implementing ML pipelines, CI/CD pipelines, and ML monitoring pipelinesDesign a robust and scalable microservice and API for test and production environmentsCurate your custom CD processes for related use cases and organizationsMonitor ML models, including monitoring data drift, model drift, and application performanceBuild and maintain automated ML systemsWho this book is for This MLOps book is for data scientists, software engineers, DevOps engineers, machine learning engineers, and business and technology leaders who want to build, deploy, and maintain ML systems in production using MLOps principles and techniques. Basic knowledge of machine learning is necessary to get started with this book. |
| data engineering roadmap 2023: Future-Oriented Technology Assessment Haydar Yalcin, Tugrul U. Daim, 2024-11-13 Comprehensive resource explaining how to evaluate technologies for different purposes in any industry using four different practical approaches By identifying emerging technology and application trends through analyses of published papers and patents, Future-Oriented Technology Assessment offers a comprehensive view of technology assessment structured into three different practical approaches: Technology Evaluation, Technology Roadmapping, and Technology Intelligence. The first three chapters include studies which utilize technology gap analysis, multiple criteria decision analysis, expert assessment quantification or neural networks to evaluate or forecast technology alternatives. The next four chapters use technology roadmapping, which charts a comprehensive plan for implementing technology. The final five chapters apply bibliometric analysis, patent analysis, and network analysis to identify technology trends and the leaders in the field. Additional topics covered in Future-Oriented Technology Assessment include: Smart grid technology as an alternative to fossil fuel consumption Heat pump water heaters that reduce the cost of energy and improve energy efficiency, with particular focus on research from the US and China Nanotechnology in construction in Saudi Arabia to improve heat insulation, energy efficiency, and tensile strength in green building designs With comprehensive, practical insight into evaluating emerging technologies across different industries, Future-Oriented Technology Assessment is an essential read for researchers in technology and professionals in engineering and technology management, along with professionals and graduate students in related disciplines and programs of study. |
| data engineering roadmap 2023: Effective Data Storytelling Brent Dykes, 2019-12-10 Master the art and science of data storytelling—with frameworks and techniques to help you craft compelling stories with data. The ability to effectively communicate with data is no longer a luxury in today’s economy; it is a necessity. Transforming data into visual communication is only one part of the picture. It is equally important to engage your audience with a narrative—to tell a story with the numbers. Effective Data Storytelling will teach you the essential skills necessary to communicate your insights through persuasive and memorable data stories. Narratives are more powerful than raw statistics, more enduring than pretty charts. When done correctly, data stories can influence decisions and drive change. Most other books focus only on data visualization while neglecting the powerful narrative and psychological aspects of telling stories with data. Author Brent Dykes shows you how to take the three central elements of data storytelling—data, narrative, and visuals—and combine them for maximum effectiveness. Taking a comprehensive look at all the elements of data storytelling, this unique book will enable you to: Transform your insights and data visualizations into appealing, impactful data stories Learn the fundamental elements of a data story and key audience drivers Understand the differences between how the brain processes facts and narrative Structure your findings as a data narrative, using a four-step storyboarding process Incorporate the seven essential principles of better visual storytelling into your work Avoid common data storytelling mistakes by learning from historical and modern examples Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals is a must-have resource for anyone who communicates regularly with data, including business professionals, analysts, marketers, salespeople, financial managers, and educators. |
| data engineering roadmap 2023: New Horizons for a Data-Driven Economy José María Cavanillas, Edward Curry, Wolfgang Wahlster, 2016-04-04 In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment. |
| data engineering roadmap 2023: The Data Warehouse Toolkit Ralph Kimball, Margy Ross, 2011-08-08 This old edition was published in 2002. The current and final edition of this book is The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition which was published in 2013 under ISBN: 9781118530801. The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including: Retail sales and e-commerce Inventory management Procurement Order management Customer relationship management (CRM) Human resources management Accounting Financial services Telecommunications and utilities Education Transportation Health care and insurance By the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts. |
| data engineering roadmap 2023: The Elements of Big Data Value Edward Curry, Andreas Metzger, Sonja Zillner, Jean-Christophe Pazzaglia, Ana García Robles, 2021-08-01 This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation. |
| data engineering roadmap 2023: Data and the Built Environment Ian Gordon, |
| data engineering roadmap 2023: Network Optimization in Intelligent Internet of Things Applications Payal Khurana Batra, Pawan Singh Mehra, Sudeep Tanwar, 2024-09-25 Network Optimization in Intelligent Internet of Things Applications: Principles and Challenges sheds light on the optimization methods that form the basis of effective communication between networked devices. It is an excellent resource as it provides readers with a thorough understanding of the methods, ideas, and tactics essential to attaining seamless connectivity and improving performance. This book presents the fundamental ideas that govern network optimization, from maximizing throughput and lowering latency to handling a variety of communication protocols and minimizing energy use. It also addresses scalability issues, security flaws, and constantly changing IoT environments along with optimization techniques. This book uses cutting-edge research and real-world examples to give readers the knowledge and skills to address the complex problems associated with network optimization in intelligent IoT applications. It also examines machine learning-driven predictive analytics, robust security protocols, flexible routing algorithms, and the integration of edge computing - all crucial instruments for overcoming obstacles and attaining peak performance. This book provides a comprehensive understanding of the principles, challenges, and cutting-edge solutions in IoT network optimization for all kinds of readers, whether it is students, academicians, researchers, or industry professionals. This book unleashes the potential of networked smart devices, which can be unleashed in various sectors. |
| data engineering roadmap 2023: Introducing MLOps Mark Treveil, Nicolas Omont, Clément Stenac, Kenji Lefevre, Du Phan, Joachim Zentici, Adrien Lavoillotte, Makoto Miyazaki, Lynn Heidmann, 2020-11-30 More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. This book helps you: Fulfill data science value by reducing friction throughout ML pipelines and workflows Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized |
| data engineering roadmap 2023: AI and Data Engineering Solutions for Effective Marketing Alla, Lhoussaine, Hmioui, Aziz, Bentalha, Badr, 2024-07-17 In the world of contemporary marketing, a challenge exists — the relationship between data engineering, artificial intelligence, and the essential elements of effective marketing. Businesses find themselves at a crossroads, grappling with the imperative to navigate this complex landscape. This challenge serves as the backdrop for the exploration in AI and Data Engineering Solutions for Effective Marketing, a comprehensive reference tailored for academic scholars. Seamlessly integrating theoretical models with real-world applications, the book delves into critical facets of strategic and operational marketing. From the adoption of data science techniques to grappling with big data's vast potential, it offers a guide for academics seeking profound insights into the future of marketing strategies and their efficient execution. Designed for researchers, practitioners, and students with an interest in the intersection of artificial intelligence, data engineering, and marketing, this book serves as a guide for implementing new marketing management solutions and optimizing their operational efficiency. While the primary audience is researchers and practitioners in the field, the book is also tailored to benefit students seeking a deep understanding of the latest developments in marketing. |
| data engineering roadmap 2023: Database Systems for Advanced Applications Makoto Onizuka, |
| data engineering roadmap 2023: A Roadmap to Industry 4.0: Smart Production, Sharp Business and Sustainable Development Anand Nayyar, Akshi Kumar, 2019-11-27 Business innovation and industrial intelligence are paving the way for a future in which smart factories, intelligent machines, networked processes and Big Data are combined to foster industrial growth. The maturity and growth of instrumentation, monitoring and automation as key technology drivers support Industry 4.0 as a viable, competent and actionable business model. This book offers a primer, helping readers understand this paradigm shift from industry 1.0 to industry 4.0. The focus is on grasping the necessary pre-conditions, development & technological aspects that conceptually describe this transformation, along with the practices, models and real-time experience needed to achieve sustainable smart manufacturing technologies. The primary goal is to address significant questions of what, how and why in this context, such as:What is Industry 4.0?What is the current status of its implementation?What are the pillars of Industry 4.0?How can Industry 4.0 be effectively implemented?How are firms exploiting the Internet of Things (IoT), Big Data and other emerging technologies to improve their production and services?How can the implementation of Industry 4.0 be accelerated?How is Industry 4.0 changing the workplace landscape?Why is this melding of the virtual and physical world needed for smart production engineering environments?Why is smart production a game-changing new form of product design and manufacturing? |
| data engineering roadmap 2023: Data Engineering with AWS Gareth Eagar, 2023-10-31 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 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 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 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. |
| data engineering roadmap 2023: Proceedings of International Conference on Computational Intelligence and Data Engineering Nabendu Chaki, Nagaraju Devarakonda, Agostino Cortesi, 2023-06-17 This book is a collection of high-quality research work on cutting-edge technologies and the most-happening areas of computational intelligence and data engineering. It includes selected papers from the International Conference on Computational Intelligence and Data Engineering (ICCIDE 2022). It covers various topics, including collective intelligence, intelligent transportation systems, fuzzy systems, Bayesian network, ant colony optimization, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence and speech processing. |
| data engineering roadmap 2023: Deep Learning for Coders with fastai and PyTorch Jeremy Howard, Sylvain Gugger, 2020-06-29 Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala |
| data engineering roadmap 2023: Data Governance Dimitrios Sargiotis, |
| data engineering roadmap 2023: Building the Data Lakehouse Bill Inmon, Ranjeet Srivastava, Mary Levins, 2021-10 The data lakehouse is the next generation of the data warehouse and data lake, designed to meet today's complex and ever-changing analytics, machine learning, and data science requirements. Learn about the features and architecture of the data lakehouse, along with its powerful analytical infrastructure. Appreciate how the universal common connector blends structured, textual, analog, and IoT data. Maintain the lakehouse for future generations through Data Lakehouse Housekeeping and Data Future-proofing. Know how to incorporate the lakehouse into an existing data governance strategy. Incorporate data catalogs, data lineage tools, and open source software into your architecture to ensure your data scientists, analysts, and end users live happily ever after. |
| data engineering roadmap 2023: Artificial Intelligence and Information Technologies Arvind Dagur, Dhirendra Kumar Shukla, Nazarov Fayzullo Makhmadiyarovich, Akhatov Akmal Rustamovich, Jabborov Jamol Sindorovich, 2024-07-31 This book contains the proceedings of a non-profit conference with the objective of providing a platform for academicians, researchers, scholars and students from various institutions, universities and industries in India and abroad to exchange their research and innovative ideas in the field of Artificial Intelligence and information technologies. It begins with exploring the research and innovation in the field of Artificial Intelligence and information technologies, including secure transaction, monitoring, real time assistance and security for advanced stage learners, researchers and academicians has been presented. It goes on to cover: Broad knowledge and research trends about Artificial Intelligence and information technologies and their role in today’s digital era Depiction of system model and architecture for clear picture of Artificial Intelligence in real life Discussion on the role of Artificial Intelligence in various real-life problems such as banking, healthcare, navigation, communication and security Explanation of the challenges and opportunities in Artificial Intelligence-based healthcare, education, banking and related industries Recent information technologies and challenges in this new epoch This book will be beneficial to researchers, academicians, undergraduate students, postgraduate students, research scholars, professionals, technologists and entrepreneurs. |
| data engineering roadmap 2023: Business Intelligence Roadmap Larissa Terpeluk Moss, S. Atre, 2003 This software will enable the user to learn about business intelligence roadmap. |
| data engineering roadmap 2023: Artificial Intelligence Applications and Innovations Ilias Maglogiannis, |
| data engineering roadmap 2023: Proceedings of the International Conference on Signal Processing and Computer Vision (SIPCOV 2023) R. Murugan, 2024 |
| data engineering roadmap 2023: Quantum Computing and Supply Chain Management: A New Era of Optimization Hassan, Ahdi, Bhattacharya, Pronaya, Dutta, Pushan Kumar, Verma, Jai Prakash, Kundu, Neel Kanth, 2024-07-23 Today's supply chains are becoming more complex and interconnected. As a result, traditional optimization engines struggle to cope with the increasing demands for real-time order fulfillment and inventory management. With the expansion and diversification of supply chain networks, these engines require additional support to handle the growing complexity effectively. This poses a significant challenge for supply chain professionals who must find efficient and cost-effective solutions to streamline their operations and promptly meet customer demands. Quantum Computing and Supply Chain Management: A New Era of Optimization offers a transformative solution to these challenges. By harnessing the power of quantum computing, this book explores how supply chain planners can overcome the limitations of traditional optimization engines. Quantum computing's ability to process vast amounts of data from IoT sensors in real time can revolutionize inventory management, resource allocation, and logistics within the supply chain. It provides a theoretical framework and practical examples to illustrate how quantum algorithms can enhance transparency, optimize dynamic inventory allocation, and improve supply chain resilience. |
| data engineering roadmap 2023: The Emerald Handbook of Smart Cities in the Gulf Region Miltiadis D. Lytras, Afnan Alkhaldi, Sawsan Malik, 2024-11-22 This definitive reference edition uniquely integrates urban planning, advanced computational, and government policy-making aspects, with a focus on disseminating the momentum of Smart Cities Research in the Gulf Region. |
| data engineering roadmap 2023: Transforming Glycoscience National Research Council, Division on Earth and Life Studies, Board on Life Sciences, Board on Chemical Sciences and Technology, Committee on Assessing the Importance and Impact of Glycomics and Glycosciences, 2012-10-23 A new focus on glycoscience, a field that explores the structures and functions of sugars, promises great advances in areas as diverse as medicine, energy generation, and materials science, this report finds. Glycans-also known as carbohydrates, saccharides, or simply as sugars-play central roles in many biological processes and have properties useful in an array of applications. However, glycans have received little attention from the research community due to a lack of tools to probe their often complex structures and properties. Transforming Glycoscience: A Roadmap for the Future presents a roadmap for transforming glycoscience from a field dominated by specialists to a widely studied and integrated discipline, which could lead to a more complete understanding of glycans and help solve key challenges in diverse fields. |
| data engineering roadmap 2023: Requirements Engineering: Foundation for Software Quality Alessio Ferrari, Birgit Penzenstadler, 2023-04-03 This book constitutes the refereed proceedings of the 29th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2023, which took place in Barcelona, Spain, during April 17-20, 2023. The 12 full technical design and scientific evaluation papers, 8 short research previews and vision papers, and 5 experience reports presented in this volume were carefully reviewed and selected from 78 submissions. They were organized in topical sections as follows: Requirements communication and conceptualization; NLP and machine learning for AI; RE for artificial intelligence; crowd RE; and RE in practice. |
| data engineering roadmap 2023: Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World Georgios Anagnostou, Andreas Benardos, Vassilis P. Marinos, 2023-04-12 Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World contains the contributions presented at the ITA-AITES World Tunnel Congress 2023 (Athens, Greece, 12 – 18 May, 2023). Tunnels and underground space are a predominant engineering practice that can provide sustainable, cost-efficient and environmentally friendly solutions to the ever-growing needs of modern societies. This underground expansion in more diverse and challenging infrastructure types or to novel underground uses can foster the changes needed. At the same time, the tunneling and underground space community needs to be better prepared and equipped with knowledge, tools and experience, to deal with the prevailing conditions, to successfully challenge and overcome adversities on this path. The papers in this book aim at contributing to the analysis of challenging conditions, the presentation and dissemination good practices, the introduction of new concepts, new tools and innovative elements that can help engineers and all stakeholders to reach their end goals. Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World covers a wide range of aspects and topics related to the whole chain of the construction and operation of underground structures: Knowledge and Passion to Expand Underground for Sustainability and Resilience Geological, Geotechnical Site Investigation and Ground Characterization Planning and Designing of Tunnels and Underground Structures Mechanised Tunnelling and Microtunnelling Conventional Tunnelling, Drill-and-Blast Applications Tunnelling in Challenging Conditions - Case Histories and Lessons Learned Innovation, Robotics and Automation BIM, Big Data and Machine Learning Applications in Tunnelling Safety, Risk and Operation of Underground Infrastructure, and Contractual Practices, Insurance and Project Management The book is a must-have reference for all professionals and stakeholders involved in tunneling and underground space development projects. |
| data engineering roadmap 2023: IEEE Technology and Engineering Management Society Body of Knowledge (TEMSBOK) Gustavo Giannattasio, Elif Kongar, Marina Dabić, Celia Desmond, Michael Condry, Sudeendra Koushik, Roberto Saracco, 2023-09-25 IEEE Technology and Engineering Management Society Body of Knowledge (TEMSBOK) IEEE TEMS Board of Directors-approved body of knowledge dedicated to technology and engineering management The IEEE Technology and Engineering Management Society Body of Knowledge (TEMSBOK) establishes a set of common practices for technology and engineering management, acts as a reference for entrepreneurs, establishes a basis for future official certifications, and summarizes the literature on the management field in order to publish reference documentation for new initiatives. The editors have used a template approach with authors that instructed them on how to introduce their manuscript, how to organize the technology and area fundamentals, the managing approach, techniques and benefits, realistic examples that show the application of concepts, recommended best use (focusing on how to identify the most adequate approach to typical cases), with a summary and conclusion of each section, plus a list of references for further study. The book is structured according to the following area knowledge chapters: business analysis, technology adoption, innovation, entrepreneurship, project management, digital disruption, digital transformation of industry, data science and management, and ethics and legal issues. Specific topics covered include: Market requirement analysis, business analysis for governance planning, financial analysis, evaluation and control, and risk analysis of market opportunities Leading and managing working groups, optimizing group creation and evolution, enterprise agile governance, and leading agile organizations and working groups Marketing plans for new products and services, risk analysis and challenges for entrepreneurs, and procurement and collaboration Projects, portfolios and programs, economic constraints and roles, integration management and control of change, and project plan structure The IEEE Technology and Engineering Management Society Body of Knowledge (TEMSBOK) will appeal to engineers, graduates, and professionals who wish to prepare for challenges in initiatives using new technologies, as well as managers who are responsible for conducting business involving technology and engineering. |
| data engineering roadmap 2023: Focus , 2010 |
| data engineering roadmap 2023: Competitiveness and Private Sector Development Western Balkans Competitiveness Outlook 2024: Kosovo OECD, 2024-06-26 Inclusive and sustainable economic growth in the six Western Balkan (WB6) economies depends on greater economic competitiveness. Although the gap is closing gradually, the standards of living in WB6 are well below those of the OECD and EU. Accelerating the rate of socio-economic convergence will require a holistic and growth oriented approach to policy making. This is the fourth study of the region (formerly under the title 'Competitiveness in South East Europe') and it comprehensively assesses policy reforms in the WB6 economies across 15 policy areas key to strengthening their competitiveness. It enables WB6 economies to compare economic performance against regional peers, as well as EU-OECD good practices and standards, and to design future policies based on rich evidence and actionable policy recommendations. The regional profile presents assessment findings across five policy clusters crucial to accelerating socio-economic convergence of the WB6 by fostering regional co-operation: business environment, skills, infrastructure and connectivity, digital transformation and greening. Economy-specific profiles complement the regional assessment, offering each WB6 economy an in-depth analysis of their policies supporting competitiveness. They also track the implementation of the previous 2021 study's recommendations and provide additional ones tailored to the economies’ evolving challenges. These recommendations aim to inform structural economic reforms and facilitate the region’s socio-economic convergence towards the standards of the EU and OECD. |
| data engineering roadmap 2023: Illustrating Digital Innovations Towards Intelligent Fashion Pethuru Raj, |
| data engineering roadmap 2023: Guide to the Software Engineering Body of Knowledge (Swebok(r)) IEEE Computer Society, 2014 In the Guide to the Software Engineering Body of Knowledge (SWEBOK(R) Guide), the IEEE Computer Society establishes a baseline for the body of knowledge for the field of software engineering, and the work supports the Society's responsibility to promote the advancement of both theory and practice in this field. It should be noted that the Guide does not purport to define the body of knowledge but rather to serve as a compendium and guide to the knowledge that has been developing and evolving over the past four decades. Now in Version 3.0, the Guide's 15 knowledge areas summarize generally accepted topics and list references for detailed information. The editors for Version 3.0 of the SWEBOK(R) Guide are Pierre Bourque (Ecole de technologie superieure (ETS), Universite du Quebec) and Richard E. (Dick) Fairley (Software and Systems Engineering Associates (S2EA)). |
| data engineering roadmap 2023: Rewired Eric Lamarre, Kate Smaje, Rodney Zemmel, 2023-06-20 In Rewired, the world's most influential management consulting firm, McKinsey & Company, delivers a road-tested, how-to manual their own consultants use to help companies build the capabilities to outcompete in the age of digital and AI. Many companies are stuck with digital transformations that are not moving the needle. There are no quick fixes but there is a playbook. The answer is in rewiring your business so hundreds, thousands, of teams can harness technology to continuously create great customer experiences, lower unit costs, and generate value. It's the capabilities of the organization that win the race. McKinsey Digital's top leaders Eric Lamarre, Kate Smaje and Rodney W. Zemmel provide proven how-to details on what it takes in six comprehensive sections – creating the transformation roadmap, building a talent bench, adopting a new operating model, producing a distributed technology environment so teams can innovate, embedding data everywhere, and unlocking user adoption and enterprise scaling. Tested, iterated, reworked, and tested again over the years, McKinsey's digital and AI transformation playbook is captured in the pages of Rewired. It contains diagnostic assessments, operating model designs, technology and data architecture diagrams, how-to checklists, best practices and detailed implementation methods, all exemplified with demonstrated case studies and illustrated with 100+ exhibits. Rewired is for leaders who are ready to roll up their sleeves and do the hard work needed to rewire their company for long-term success. |
| data engineering roadmap 2023: 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 |
| data engineering roadmap 2023: Big Data Integration Xin Luna Dong, Divesh Srivastava, 2015-02-01 The big data era is upon us: data are being generated, analyzed, and used at an unprecedented scale, and data-driven decision making is sweeping through all aspects of society. Since the value of data explodes when it can be linked and fused with other data, addressing the big data integration (BDI) challenge is critical to realizing the promise of big data. BDI differs from traditional data integration along the dimensions of volume, velocity, variety, and veracity. First, not only can data sources contain a huge volume of data, but also the number of data sources is now in the millions. Second, because of the rate at which newly collected data are made available, many of the data sources are very dynamic, and the number of data sources is also rapidly exploding. Third, data sources are extremely heterogeneous in their structure and content, exhibiting considerable variety even for substantially similar entities. Fourth, the data sources are of widely differing qualities, with significant differences in the coverage, accuracy and timeliness of data provided. This book explores the progress that has been made by the data integration community on the topics of schema alignment, record linkage and data fusion in addressing these novel challenges faced by big data integration. Each of these topics is covered in a systematic way: first starting with a quick tour of the topic in the context of traditional data integration, followed by a detailed, example-driven exposition of recent innovative techniques that have been proposed to address the BDI challenges of volume, velocity, variety, and veracity. Finally, it presents merging topics and opportunities that are specific to BDI, identifying promising directions for the data integration community. |
| data engineering roadmap 2023: Mastering Web development Cybellium Ltd, Unleash Your Potential in Web Development with Mastering Web Development In today's digital age, web development is a skill that empowers individuals and organizations to create impactful online experiences, from websites and web applications to e-commerce platforms. Mastering web development opens the doors to limitless possibilities, whether you're a seasoned developer or just starting on your coding journey. Mastering Web Development is your comprehensive guide to becoming a proficient web developer, providing you with the knowledge, skills, and strategies to create dynamic and cutting-edge web solutions. Your Path to Web Development Excellence Web development is more than just writing code—it's about crafting user-friendly, responsive, and visually engaging websites and applications. Whether you're new to web development or looking to expand your skills, this book will empower you to master the art of web development. What You Will Discover Foundations of Web Development: Gain a strong understanding of HTML, CSS, and JavaScript—the core building blocks of the web. Front-End Development: Dive into front-end technologies, including responsive design, UI/UX principles, and popular front-end frameworks. Back-End Development: Explore back-end programming languages, server-side scripting, and databases to create dynamic web applications. Web Development Tools: Master the use of essential web development tools, such as code editors, version control, and debugging tools. Web Security: Learn best practices for securing web applications and protecting against common security threats. Web Development Trends: Stay up-to-date with the latest trends in web development, including Progressive Web Apps (PWAs) and Single Page Applications (SPAs). Why Mastering Web Development Is Essential Comprehensive Coverage: This book provides comprehensive coverage of web development topics, ensuring that you have a well-rounded understanding of web technologies and practices. Expert Guidance: Benefit from insights and advice from experienced web developers and industry experts who share their knowledge and best practices. Career Advancement: Web development skills are in high demand, and this book will help you unlock your full potential in this dynamic field. Stay Competitive: In a digitally-driven world, mastering web development is vital for staying competitive and creating impactful online experiences. Your Journey to Web Development Mastery Begins Here Mastering Web Development is your roadmap to excelling in the world of web development and advancing your career. Whether you aspire to be a front-end developer, back-end developer, or full-stack developer, this guide will equip you with the skills and knowledge to achieve your goals. Don't miss the opportunity to become a proficient web developer. Start your journey to web development mastery today and join the ranks of professionals who are shaping the digital landscape. Mastering Web Development is the ultimate resource for individuals seeking to excel in the field of web development. Whether you are new to web development or looking to enhance your skills, this book will provide you with the knowledge and strategies to become a proficient web developer. Don't wait; begin your journey to web development mastery today! © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a Transnationa…
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and …
Belmont Forum Adopts Open Data Principles for Environmental Chan…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes …
