Data Engineering A Complete Guide - 2020 Edition PDF Download
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Author: Gerardus Blokdyk
Publisher: 5starcooks
ISBN: 9781867316718
Category :
Languages : en
Pages : 300
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Book Description
What Data engineering modifications can you make work for you? What are the revised rough estimates of the financial savings/opportunity for Data engineering improvements? Have you included everything in your Data engineering cost models? How will you know that the Data engineering project has been successful? Do Data engineering rules make a reasonable demand on a users capabilities? Defining, designing, creating, and implementing a process to solve a challenge or meet an objective is the most valuable role... In EVERY group, company, organization and department. Unless you are talking a one-time, single-use project, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Data Engineering investments work better. This Data Engineering All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth Data Engineering Self-Assessment. Featuring 939 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Data Engineering improvements can be made. In using the questions you will be better able to: - diagnose Data Engineering projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Data Engineering and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Data Engineering Scorecard, you will develop a clear picture of which Data Engineering areas need attention. Your purchase includes access details to the Data Engineering self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Data Engineering Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.
Author: Gerardus Blokdyk
Publisher: 5starcooks
ISBN: 9781867316718
Category :
Languages : en
Pages : 300
Get Book Here
Book Description
What Data engineering modifications can you make work for you? What are the revised rough estimates of the financial savings/opportunity for Data engineering improvements? Have you included everything in your Data engineering cost models? How will you know that the Data engineering project has been successful? Do Data engineering rules make a reasonable demand on a users capabilities? Defining, designing, creating, and implementing a process to solve a challenge or meet an objective is the most valuable role... In EVERY group, company, organization and department. Unless you are talking a one-time, single-use project, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Data Engineering investments work better. This Data Engineering All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth Data Engineering Self-Assessment. Featuring 939 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Data Engineering improvements can be made. In using the questions you will be better able to: - diagnose Data Engineering projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Data Engineering and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Data Engineering Scorecard, you will develop a clear picture of which Data Engineering areas need attention. Your purchase includes access details to the Data Engineering self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Data Engineering Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.
Author: Paul Crickard
Publisher: Packt Publishing Ltd
ISBN: 1839212306
Category : Computers
Languages : en
Pages : 357
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Book Description
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.
Author: Gerardus Blokdyk
Publisher: 5starcooks
ISBN: 9781867301882
Category :
Languages : en
Pages : 308
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Book Description
What internal processes need improvement? Why do you expend time and effort to implement measurement, for whom? Do you monitor the Data and Knowledge Engineering decisions made and fine tune them as they evolve? Are there regulatory / compliance issues? Do you monitor the effectiveness of your Data and Knowledge Engineering activities? Defining, designing, creating, and implementing a process to solve a challenge or meet an objective is the most valuable role... In EVERY group, company, organization and department. Unless you are talking a one-time, single-use project, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Data And Knowledge Engineering investments work better. This Data And Knowledge Engineering All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth Data And Knowledge Engineering Self-Assessment. Featuring 949 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Data And Knowledge Engineering improvements can be made. In using the questions you will be better able to: - diagnose Data And Knowledge Engineering projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Data And Knowledge Engineering and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Data And Knowledge Engineering Scorecard, you will develop a clear picture of which Data And Knowledge Engineering areas need attention. Your purchase includes access details to the Data And Knowledge Engineering self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Data And Knowledge Engineering Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.
Author: Vlad Riscutia
Publisher: Simon and Schuster
ISBN: 1617298921
Category : Computers
Languages : en
Pages : 334
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Book Description
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
Author: Ralph Kimball
Publisher: John Wiley & Sons
ISBN: 1118082141
Category : Computers
Languages : en
Pages : 464
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Book Description
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.
Author: Gerardus Blokdyk
Publisher:
ISBN: 9781867464495
Category :
Languages : en
Pages : 0
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Book Description
Author: Gerardus Blokdyk
Publisher: 5starcooks
ISBN: 9781867330806
Category :
Languages : en
Pages : 308
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Book Description
Where is Engineering statistics data gathered? Whose voice (department, ethnic group, women, older workers, etc) might you have missed hearing from in your company, and how might you amplify this voice to create positive momentum for your business? Are you satisfied with your current role? If not, what is missing from it? Implementation Planning: is a pilot needed to test the changes before a full roll out occurs? What is the scope of the Engineering statistics effort? This premium Engineering Statistics self-assessment will make you the credible Engineering Statistics domain master by revealing just what you need to know to be fluent and ready for any Engineering Statistics challenge. How do I reduce the effort in the Engineering Statistics work to be done to get problems solved? How can I ensure that plans of action include every Engineering Statistics task and that every Engineering Statistics outcome is in place? How will I save time investigating strategic and tactical options and ensuring Engineering Statistics costs are low? How can I deliver tailored Engineering Statistics advice instantly with structured going-forward plans? There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all Engineering Statistics essentials are covered, from every angle: the Engineering Statistics self-assessment shows succinctly and clearly that what needs to be clarified to organize the required activities and processes so that Engineering Statistics outcomes are achieved. Contains extensive criteria grounded in past and current successful projects and activities by experienced Engineering Statistics practitioners. Their mastery, combined with the easy elegance of the self-assessment, provides its superior value to you in knowing how to ensure the outcome of any efforts in Engineering Statistics are maximized with professional results. Your purchase includes access details to the Engineering Statistics self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows you exactly what to do next. Your exclusive instant access details can be found in your book. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Engineering Statistics Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.
Author: Gerardus Blokdyk
Publisher:
ISBN: 9780655958451
Category : Electronic books
Languages : en
Pages : 0
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Book Description
Data Design A Complete Guide - 2020 Edition.
Author: James Densmore
Publisher: O'Reilly Media
ISBN: 1492087807
Category : Computers
Languages : en
Pages : 277
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Book Description
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
Author: Bill Chambers
Publisher: "O'Reilly Media, Inc."
ISBN: 1491912294
Category : Computers
Languages : en
Pages : 594
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Book Description
Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. Youâ??ll explore the basic operations and common functions of Sparkâ??s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Sparkâ??s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasetsâ??Sparkâ??s core APIsâ??through worked examples Dive into Sparkâ??s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Sparkâ??s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation
