Data Engineering And Applications

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Data Engineering and Data Science

Author : Kukatlapalli Pradeep Kumar,Aynur Unal,Vinay Jha Pillai,Hari Murthy,M. Niranjanamurthy
Publisher : John Wiley & Sons
Page : 367 pages
File Size : 42,8 Mb
Release : 2023-08-29
Category : Mathematics
ISBN : 9781119841975

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Data Engineering and Data Science by Kukatlapalli Pradeep Kumar,Aynur Unal,Vinay Jha Pillai,Hari Murthy,M. Niranjanamurthy Pdf

DATA ENGINEERING and DATA SCIENCE Written and edited by one of the most prolific and well-known experts in the field and his team, this exciting new volume is the “one-stop shop” for the concepts and applications of data science and engineering for data scientists across many industries. The field of data science is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it is rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesn’t need to know the whole spectrum of skills. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In this exciting new volume, the team of editors and contributors sketch the broad outlines of data engineering, then walk through more specific descriptions that illustrate specific data engineering roles. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This book brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.

Data Engineering on Azure

Author : Vlad Riscutia
Publisher : Simon and Schuster
Page : 334 pages
File Size : 49,9 Mb
Release : 2021-08-17
Category : Computers
ISBN : 9781617298929

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Data Engineering on Azure by Vlad Riscutia Pdf

Build a data platform to the industry-leading standards set by Microsoft’s own infrastructure. Summary In Data Engineering on Azure you will learn how to: Pick the right Azure services for different data scenarios Manage data inventory Implement production quality data modeling, analytics, and machine learning workloads Handle data governance Using DevOps to increase reliability Ingesting, storing, and distributing data Apply best practices for compliance and access control Data Engineering on Azure reveals the data management patterns and techniques that support Microsoft’s own massive data infrastructure. Author Vlad Riscutia, a data engineer at Microsoft, teaches you to bring an engineering rigor to your data platform and ensure that your data prototypes function just as well under the pressures of production. You'll implement common data modeling patterns, stand up cloud-native data platforms on Azure, and get to grips with DevOps for both analytics and machine learning. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build secure, stable data platforms that can scale to loads of any size. When a project moves from the lab into production, you need confidence that it can stand up to real-world challenges. This book teaches you to design and implement cloud-based data infrastructure that you can easily monitor, scale, and modify. About the book In Data Engineering on Azure you’ll learn the skills you need to build and maintain big data platforms in massive enterprises. This invaluable guide includes clear, practical guidance for setting up infrastructure, orchestration, workloads, and governance. As you go, you’ll set up efficient machine learning pipelines, and then master time-saving automation and DevOps solutions. The Azure-based examples are easy to reproduce on other cloud platforms. What's inside Data inventory and data governance Assure data quality, compliance, and distribution Build automated pipelines to increase reliability Ingest, store, and distribute data Production-quality data modeling, analytics, and machine learning About the reader For data engineers familiar with cloud computing and DevOps. About the author Vlad Riscutia is a software architect at Microsoft. Table of Contents 1 Introduction PART 1 INFRASTRUCTURE 2 Storage 3 DevOps 4 Orchestration PART 2 WORKLOADS 5 Processing 6 Analytics 7 Machine learning PART 3 GOVERNANCE 8 Metadata 9 Data quality 10 Compliance 11 Distributing data

Big Data in Engineering Applications

Author : Sanjiban Sekhar Roy,Pijush Samui,Ravinesh Deo,Stavros Ntalampiras
Publisher : Springer
Page : 384 pages
File Size : 49,5 Mb
Release : 2018-05-02
Category : Technology & Engineering
ISBN : 9789811084768

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Big Data in Engineering Applications by Sanjiban Sekhar Roy,Pijush Samui,Ravinesh Deo,Stavros Ntalampiras Pdf

This book presents the current trends, technologies, and challenges in Big Data in the diversified field of engineering and sciences. It covers the applications of Big Data ranging from conventional fields of mechanical engineering, civil engineering to electronics, electrical, and computer science to areas in pharmaceutical and biological sciences. This book consists of contributions from various authors from all sectors of academia and industries, demonstrating the imperative application of Big Data for the decision-making process in sectors where the volume, variety, and velocity of information keep increasing. The book is a useful reference for graduate students, researchers and scientists interested in exploring the potential of Big Data in the application of engineering areas.

Data, Engineering and Applications

Author : Rajesh Kumar Shukla,Jitendra Agrawal,Sanjeev Sharma,Geetam Singh Tomer
Publisher : Springer
Page : 331 pages
File Size : 55,7 Mb
Release : 2019-04-24
Category : Computers
ISBN : 9789811363511

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Data, Engineering and Applications by Rajesh Kumar Shukla,Jitendra Agrawal,Sanjeev Sharma,Geetam Singh Tomer Pdf

This book presents a compilation of current trends, technologies, and challenges in connection with Big Data. Many fields of science and engineering are data-driven, or generate huge amounts of data that are ripe for the picking. There are now more sources of data than ever before, and more means of capturing data. At the same time, the sheer volume and complexity of the data have sparked new developments, where many Big Data problems require new solutions. Given its scope, the book offers a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of Big Data applications.

Data Pipelines Pocket Reference

Author : James Densmore
Publisher : O'Reilly Media
Page : 277 pages
File Size : 54,6 Mb
Release : 2021-02-10
Category : Computers
ISBN : 9781492087809

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Data Pipelines Pocket Reference by James Densmore Pdf

Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting

Designing Data-Intensive Applications

Author : Martin Kleppmann
Publisher : "O'Reilly Media, Inc."
Page : 658 pages
File Size : 42,9 Mb
Release : 2017-03-16
Category : Computers
ISBN : 9781491903100

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Designing Data-Intensive Applications by Martin Kleppmann Pdf

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

Data Engineering with Python

Author : Paul Crickard
Publisher : Packt Publishing Ltd
Page : 357 pages
File Size : 53,7 Mb
Release : 2020-10-23
Category : Computers
ISBN : 9781839212307

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Data Engineering with Python by Paul Crickard Pdf

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

Author : Olaf Wolkenhauer
Publisher : John Wiley & Sons
Page : 296 pages
File Size : 52,6 Mb
Release : 2004-04-07
Category : Technology & Engineering
ISBN : 9780471464105

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Data Engineering by Olaf Wolkenhauer Pdf

Although data engineering is a multi-disciplinary field withapplications in control, decision theory, and the emerging hot areaof bioinformatics, there are no books on the market that make thesubject accessible to non-experts. This book fills the gap in thefield, offering a clear, user-friendly introduction to the maintheoretical and practical tools for analyzing complex systems. Anftp site features the corresponding MATLAB and Mathematical toolsand simulations. Market: Researchers in data management, electrical engineering,computer science, and life sciences.

Data Science in Engineering and Management

Author : Zdzislaw Polkowski,Sambit Kumar Mishra,Julian Vasilev
Publisher : CRC Press
Page : 159 pages
File Size : 47,9 Mb
Release : 2021-12-31
Category : Technology & Engineering
ISBN : 9781000520842

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Data Science in Engineering and Management by Zdzislaw Polkowski,Sambit Kumar Mishra,Julian Vasilev Pdf

This book brings insight into data science and offers applications and implementation strategies. It includes current developments and future directions and covers the concept of data science along with its origins. It focuses on the mechanisms of extracting data along with classifications, architectural concepts, and business intelligence with predictive analysis. Data Science in Engineering and Management: Applications, New Developments, and Future Trends introduces the concept of data science, its use, and its origins, as well as presenting recent trends, highlighting future developments; discussing problems and offering solutions. It provides an overview of applications on data linked to engineering and management perspectives and also covers how data scientists, analysts, and program managers who are interested in productivity and improving their business can do so by incorporating a data science workflow effectively. This book is useful to researchers involved in data science and can be a reference for future research. It is also suitable as supporting material for undergraduate and graduate-level courses in related engineering disciplines.

Data, Engineering and Applications

Author : Rajesh Kumar Shukla,Jitendra Agrawal,Sanjeev Sharma,Geetam Singh Tomer
Publisher : Springer
Page : 189 pages
File Size : 49,6 Mb
Release : 2019-03-18
Category : Computers
ISBN : 9789811363474

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Data, Engineering and Applications by Rajesh Kumar Shukla,Jitendra Agrawal,Sanjeev Sharma,Geetam Singh Tomer Pdf

This book presents a compilation of current trends, technologies, and challenges in connection with Big Data. Many fields of science and engineering are data-driven, or generate huge amounts of data that are ripe for the picking. There are now more sources of data than ever before, and more means of capturing data. At the same time, the sheer volume and complexity of the data have sparked new developments, where many Big Data problems require new solutions. Given its scope, the book offers a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of Big Data applications.

Data Mining for Scientific and Engineering Applications

Author : R.L. Grossman,C. Kamath,P. Kegelmeyer,V. Kumar,R. Namburu
Publisher : Springer Science & Business Media
Page : 608 pages
File Size : 55,8 Mb
Release : 2013-12-01
Category : Computers
ISBN : 9781461517337

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Data Mining for Scientific and Engineering Applications by R.L. Grossman,C. Kamath,P. Kegelmeyer,V. Kumar,R. Namburu Pdf

Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications. Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering.

Engineering Web Applications

Author : Sven Casteleyn,Florian Daniel,Peter Dolog,Maristella Matera
Publisher : Springer Science & Business Media
Page : 357 pages
File Size : 45,9 Mb
Release : 2009-07-25
Category : Computers
ISBN : 9783540922018

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Engineering Web Applications by Sven Casteleyn,Florian Daniel,Peter Dolog,Maristella Matera Pdf

Nowadays, Web applications are almost omnipresent. The Web has become a platform not only for information delivery, but also for eCommerce systems, social networks, mobile services, and distributed learning environments. Engineering Web applications involves many intrinsic challenges due to their distributed nature, content orientation, and the requirement to make them available to a wide spectrum of users who are unknown in advance. The authors discuss these challenges in the context of well-established engineering processes, covering the whole product lifecycle from requirements engineering through design and implementation to deployment and maintenance. They stress the importance of models in Web application development, and they compare well-known Web-specific development processes like WebML, WSDM and OOHDM to traditional software development approaches like the waterfall model and the spiral model. .

Intelligent Data Engineering and Analytics

Author : Suresh Chandra Satapathy,Yu-Dong Zhang,Vikrant Bhateja,Ritanjali Majhi
Publisher : Springer Nature
Page : 758 pages
File Size : 46,5 Mb
Release : 2020-08-29
Category : Technology & Engineering
ISBN : 9789811556791

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Intelligent Data Engineering and Analytics by Suresh Chandra Satapathy,Yu-Dong Zhang,Vikrant Bhateja,Ritanjali Majhi Pdf

This book gathers the proceedings of the 8th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2020), held at NIT Surathkal, Karnataka, India, on 4–5 January 2020. In these proceedings, researchers, scientists, engineers and practitioners share new ideas and lessons learned in the field of intelligent computing theories with prospective applications in various engineering disciplines. The respective papers cover broad areas of the information and decision sciences, and explore both the theoretical and practical aspects of data-intensive computing, data mining, evolutionary computation, knowledge management and networks, sensor networks, signal processing, wireless networks, protocols and architectures. Given its scope, the book offers a valuable resource for graduate students in various engineering disciplines.

97 Things Every Data Engineer Should Know

Author : Tobias Macey
Publisher : "O'Reilly Media, Inc."
Page : 243 pages
File Size : 52,9 Mb
Release : 2021-06-11
Category : Computers
ISBN : 9781492062363

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97 Things Every Data Engineer Should Know by Tobias Macey Pdf

Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: The Importance of Data Lineage - Julien Le Dem Data Security for Data Engineers - Katharine Jarmul The Two Types of Data Engineering and Data Engineers - Jesse Anderson Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy The End of ETL as We Know It - Paul Singman Building a Career as a Data Engineer - Vijay Kiran Modern Metadata for the Modern Data Stack - Prukalpa Sankar Your Data Tests Failed! Now What? - Sam Bail

Intelligent Data Engineering and Automated Learning – IDEAL 2020

Author : Cesar Analide,Paulo Novais,David Camacho,Hujun Yin
Publisher : Springer Nature
Page : 633 pages
File Size : 48,6 Mb
Release : 2020-10-29
Category : Computers
ISBN : 9783030623654

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Intelligent Data Engineering and Automated Learning – IDEAL 2020 by Cesar Analide,Paulo Novais,David Camacho,Hujun Yin Pdf

This two-volume set of LNCS 12489 and 12490 constitutes the thoroughly refereed conference proceedings of the 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020, held in Guimaraes, Portugal, in November 2020.* The 93 papers presented were carefully reviewed and selected from 134 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2020 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspiredmodels, agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI. * The conference was held virtually due to the COVID-19 pandemic.