Advanced Data Management Technologies Unit 8 — Extract, Transform, Load
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Data Center (CDP-DC) Reference Architecture
Cloudera Data Platform - Data Center (CDP-DC) Reference Architecture Important Notice © 2010-2020 Cloudera, Inc. All rights reserved. Cloudera, the Cloudera logo, and any other product or service names or slogans contained in this document, except as otherwise disclaimed, are trademarks of Cloudera and its suppliers or licensors, and may not be copied, imitated or used, in whole or in part, without the prior written permission of Cloudera or the applicable trademark holder. Hadoop and the Hadoop elephant logo are trademarks of the Apache Software Foundation. All other trademarks, registered trademarks, product names and company names or logos mentioned in this document are the property of their respective owners to any products, services, processes or other information, by trade name, trademark, manufacturer, supplier or otherwise does not constitute or imply endorsement, sponsorship or recommendation thereof by us. Complying with all applicable copyright laws is the responsibility of the user. Without limiting the rights under copyright, no part of this document may be reproduced, stored in or introduced into a retrieval system, or transmitted in any form or by any means (electronic, mechanical, photocopying, recording, or otherwise), or for any purpose, without the express written permission of Cloudera. Cloudera may have patents, patent applications, trademarks, copyrights, or other intellectual property rights covering subject matter in this document. Except as expressly provided in any written license agreement from Cloudera, the furnishing of this document does not give you any license to these patents, trademarks copyrights, or other intellectual property. The information in this document is subject to change without notice. Cloudera shall not be liable for any damages resulting from technical errors or omissions which may be present in this document, or from use of this document. -
A Guide to Data Governance Building a Roadmap for Trusted Data a Guide to Data Governance Contents
A Guide to Data Governance Building a roadmap for trusted data A Guide to Data Governance Contents What is Data Governance? ...............................................................................................3 Why Do We Need It? ............................................................................................................................................................................. 4 The Need to Create Trusted Data ..................................................................................................................................................... 4 The Need to Protect Data .................................................................................................................................................................... 5 Requirements For Governing Data In A Modern Enterprise ........................................7 Common Business Vocabulary ........................................................................................................................................................... 7 Governing Data Across A Distributed Data Landscape............................................................................................................ 7 Data Governance Classification ......................................................................................................................................................... 8 Data Governance Roles and Responsibilities .............................................................................................................................. -
2020 Global Data Management Research the Data-Driven Organization, a Transformation in Progress Methodology
BenchmarkBenchmark Report report 2020 Global data management research The data-driven organization, a transformation in progress Methodology Experian conducted a survey to look at global trends in data management. This study looks at how data practitioners and data-driven business leaders are leveraging their data to solve key business challenges and how data management practices are changing over time. Produced by Insight Avenue for Experian in October 2019, the study surveyed more than 1,100 people across six countries around the globe: the United States, the United Kingdom, Germany, France, Brazil, and Australia. Organizations that were surveyed came from a variety of industries, including IT, telecommunications, manufacturing, retail, business services, financial services, healthcare, public sector, education, utilities, and more. A variety of roles from all areas of the organization were surveyed, which included titles such as chief data officer, chief marketing officer, data analyst, financial analyst, data engineer, data steward, and risk manager. Respondents were chosen based on their visibility into their organization’s customer or prospect data management practices. Page 2 | The data-driven organization, a transformation in progress Benchmark report 2020 Global data management research The data-informed organization, a transformation in progress Executive summary ..........................................................................................................................................................5 Section 1: -
Value of Data Management
USGS Data Management Training Modules: Value of Data Management “Data is a precious thing and will last longer than the systems themselves.” -- Tim Berners-Lee …. But only if that data is properly managed! U.S. Department of the Interior Accessibility | FOIA | Privacy | Policies and Notices U.S. Geological Survey Version 1, October 2013 Narration: Introduction Slide 1 USGS Data Management Training Modules – the Value of Data Management Welcome to the USGS Data Management Training Modules, a three part training series that will guide you in understanding and practicing good data management. Today we will discuss the Value of Data Management. As Tim Berners-Lee said, “Data is a precious thing and will last longer than the systems themselves.” ……We will illustrate here that the validity of that statement depends on proper management of that data! Module Objectives · Describe the various roles and responsibilities of data management. · Explain how data management relates to everyday work. · Emphasize the value of data management. From Flickr by cybrarian77 Narration:Slide 2 Module Objectives In this module, you will learn how to: 1. Describe the various roles and responsibilities of data management. 2. Explain how data management relates to everyday work and the greater good. 3. Motivate (with examples) why data management is valuable. These basic lessons will provide the foundation for understanding why good data management is worth pursuing. 2 Terms and Definitions · Data Management (DM) – the development, execution and supervision of plans, -
Data Stewardship Committee: Minutes of February 23, 2015
Data Stewardship Committee: Minutes of February 23, 2015 In attendance: Craig Abbey (OIA), Gary Pacer (EAS), Brian O’Connor (CAS), Tom Wendt (VPRE), David Love (SEAS), Leah Feroleto (SW), Troy Joseph (GEMS), Chris Connor (GEMS/UG Admissions), Greg Olsen (VPEM), John Gottardy (Financial Aid), Sue Krzystofiak (HR), Beth Corry (Financial Services), Shirley Walker (Student Accounts), Michael Koziej (Campus Living), Kelly Hayes-McAlonie (Capital Planning), Tom Okon (Business Reporting and Services), Mark Molnar (OIA), Laurie Barnum (Resource Planning) Peter Elkin (Biomedical Informatics), Rachel Link (OIA). Meeting called to order at 4:00 p.m. by Gary Pacer. Gary thanked all in attendance for making the time to participate and asked attendees to introduce themselves and the areas represented. Gary gave a brief overview of the Data Stewardship Committee, which is a subsidiary of the Data Governance Council. Craig Abbey and Gary Pacer are co-chairing the DSC, and the initial invitation sent to the DSC members included the charge from the Provost for both the DSC and DGC. Thirteen data domains are represented on the committee, providing a university-wide perspective of how data governance is managed across the institution, how data are defined for particular projects, and identifying common vernacular to solve or address problems. The DSC membership is comprised of Data Stewards, Ex Officio members, and Ad Lucem (Latin for “to the light”) members, who serve to help lead the data stewardship process. Gary next reminded those in attendance of the work that lies ahead for the committee. The initial focus is to prepare the report recommending permanent data management organization and structure. -
Master Data Management Whitepaper.Indd
Creating a Master Data Environment An Incremental Roadmap for Public Sector Organizations Master Data Management (MDM) is the processes and technologies used to create and maintain consistent and accurate representations of master data. Movements toward modularity, service orientation, and SaaS make Master Data Management a critical issue. Master Data Management leverages both tools and processes that enable the linkage of critical data through a unifi ed platform that provides a common point of reference. When properly done, MDM streamlines data management and sharing across the enterprise among different areas to provide more effective service delivery. CMA possesses more than 15 years of experience in Master Data Management and more than 20 in data management and integration. CMA has also focused our efforts on public sector since our inception, more than 30 years ago. This document describes the fundamental keys to success for developing and maintaining a long term master data program within a public service organization. www.cma.com 2 Understanding Master Data transactional data. As it becomes more volatile, it typically is considered more transactional. Simple Master Data Behavior entities are rarely a challenge to manage and are rarely Master data can be described by the way that it interacts considered master-data elements. The less complex an with other data. For example, in transaction systems, element, the less likely the need to manage change for master data is almost always involved with transactional that element. The more valuable the data element is to data. This relationship between master data and the organization, the more likely it will be considered a transactional data may be fundamentally viewed as a master data element. -
Research Data Management Best Practices
Research Data Management Best Practices Introduction ............................................................................................................................................................................ 2 Planning & Data Management Plans ...................................................................................................................................... 3 Naming and Organizing Your Files .......................................................................................................................................... 6 Choosing File Formats ............................................................................................................................................................. 9 Working with Tabular Data ................................................................................................................................................... 10 Describing Your Data: Data Dictionaries ............................................................................................................................... 12 Describing Your Project: Citation Metadata ......................................................................................................................... 15 Preparing for Storage and Preservation ............................................................................................................................... 17 Choosing a Repository ......................................................................................................................................................... -
Data Governance with Oracle
Data Governance with Oracle Defining and Implementing a Pragmatic Data Governance Process with Oracle Metadata Management and Oracle Data Quality Solutions ORACLE WHITE P A P E R | SEPTEMBER 2015 Disclaimer The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle. DATA GOVERNANCE WITH ORACLE Table of Contents Disclaimer 1 Introduction 1 First Define the Business Problem 2 Identify Executive Sponsor 3 Manage Glossary of Business Terms 4 Identify Critical Data Elements 4 Classify Data from an Information Security Perspective 5 Manage Business Rules 6 Manage Allowable Values for Business Terms 7 Support for Data Lineage and Impact Analysis 8 Manage Data Stewardship Workflows 10 Govern Big Data 11 Manage Data Quality Rules 12 Execute Data Quality Rules 12 View Data Quality Dashboard 16 Data Quality Remediation 16 Data Privacy and Security 17 Ingredients for Data Governance Success 17 Governance with Any Enterprise System 19 Align with Other Oracle Solutions 20 About the Author 22 DATA GOVERNANCE WITH ORACLE . DATA GOVERNANCE WITH ORACLE Introduction Data governance is the formulation of policy to optimize, secure, and leverage information as an enterprise asset by aligning the objectives of multiple functions. Data governance programs have traditionally been focused on people and process. In this whitepaper, we will discuss how key data governance capabilities are enabled by Oracle Enterprise Metadata Manager (OEMM) and Oracle Enterprise Data Quality (EDQ). -
Research Data Management: Sharing and Storage
Research Data Management: Strategies for Data Sharing and Storage 1 Research Data Management Services @ MIT Libraries • Workshops • Web guide: http://libraries.mit.edu/data-management • Individual assistance/consultations – includes assistance with creating data management plans 2 Why Share and Archive Your Data? ● Funder requirements ● Publication requirements ● Research credit ● Reproducibility, transparency, and credibility ● Increasing collaborations, enabling future discoveries 3 Research Data: Common Types by Discipline General Social Sciences Hard Sciences • images • survey responses • measurements generated by sensors/laboratory • video • focus group and individual instruments interviews • mapping/GIS data • computer modeling • economic indicators • numerical measurements • simulations • demographics • observations and/or field • opinion polling studies • specimen 4 5 Research Data: Stages Raw Data raw txt file produced by an instrument Processed Data data with Z-scores calculated Analyzed Data rendered computational analysis Finalized/Published Data polished figures appear in Cell 6 Setting Up for Reuse: ● Formats ● Versioning ● Metadata 7 Formats: Considerations for Long-term Access to Data In the best case, your data formats are both: • Non-proprietary (also known as open), and • Unencrypted and uncompressed 8 Formats: Considerations for Long-term Access to Data In the best case, your data files are both: • Non-proprietary (also known as open), and • Unencrypted and uncompressed 9 Formats: Preferred Examples Proprietary Format -
Data Management, Analysis Tools, and Analysis Mechanics
Chapter 2 Data Management, Analysis Tools, and Analysis Mechanics This chapter explores different tools and techniques for handling data for research purposes. This chapter assumes that a research problem statement has been formulated, research hypotheses have been stated, data collection planning has been conducted, and data have been collected from various sources (see Volume I for information and details on these phases of research). This chapter discusses how to combine and manage data streams, and how to use data management tools to produce analytical results that are error free and reproducible, once useful data have been obtained to accomplish the overall research goals and objectives. Purpose of Data Management Proper data handling and management is crucial to the success and reproducibility of a statistical analysis. Selection of the appropriate tools and efficient use of these tools can save the researcher numerous hours, and allow other researchers to leverage the products of their work. In addition, as the size of databases in transportation continue to grow, it is becoming increasingly important to invest resources into the management of these data. There are a number of ancillary steps that need to be performed both before and after statistical analysis of data. For example, a database composed of different data streams needs to be matched and integrated into a single database for analysis. In addition, in some cases data must be transformed into the preferred electronic format for a variety of statistical packages. Sometimes, data obtained from “the field” must be cleaned and debugged for input and measurement errors, and reformatted. The following sections discuss considerations for developing an overall data collection, handling, and management plan, and tools necessary for successful implementation of that plan. -
BEST PRACTICES for DATA MANAGEMENT TECHNICAL GUIDE Nov 2018 EPA ID # 542-F-18-003
BEST PRACTICES FOR DATA MANAGEMENT TECHNICAL GUIDE EPA ID # 542-F-18-003 Section 1 - Introduction This technical guide provides best practices Why is EPA Issuing this technical guide? for efficiently managing the large amount of data generated throughout the data life The U.S. Environmental Protection Agency (EPA) cycle. Thorough, up-front remedial developed this guide to support achievement of the investigation and feasibility study (RI/FS) July 2017 Superfund Task Force goals. Two additional planning and scoping combined with companion technical guides should be used in decision support tools and visualization can conjunction with this data management technical help reduce RI/FS cost and provide a more guide: complete conceptual site model (CSM) • Smart Scoping for Environmental earlier in the process. In addition, data Investigations management plays an important role in • Strategic Sampling Approaches adaptive management application during the RI/FS and remedial design and action. This section defines the data life cycle approach and describes the benefits a comprehensive data life cycle management approach can accrue. What is the “Data Life Cycle” Management Approach? The Superfund program collects, reviews and works with large volumes of sampling, monitoring and environmental data that are used for decisions at different scales. For example, site-specific Superfund data developed by EPA, potentially responsible parties, states, tribes, federal agencies and others can include: • Geologic and hydrogeologic data; • Geospatial data (Geographic Information System [GIS] and location data); • Chemical characteristics; • Physical characteristics; and • Monitoring and remediation system performance data. In addition, EPA recognizes that regulatory information and other non-technical data are used to develop a CSM and support Superfund decisions. -
Data Stewardship on the Map: a Study of Tasks and Roles in Dutch Research Institutes
Data stewardship on the map: A study of tasks and roles in Dutch research institutes lcrdm TheHet LandelijkNational CoordinationCoördinatiepunt Point Research Research Data Data Management (islcrd een m)landelijk is a national netwerk network van experts of expertsop het gebied on research van research data management data management (rdm) (rdm). inHet the lcrdm Netherlands. maakt de The koppeling lcrdm connects tussen beleid policy en anddagelijkse daily practice. praktijk. Binnen Within the het lcrd lcrdmm experts werken experts worksamen together om rdm-onderwerpen to put rdm topics te onagenderen the agenda die te thatgroot ask zijn for voor mutual één national instelling cooperation. en die vragen1 om een gezamenlijke landelijke aanpak. documentversion: april/may 2019 meermore informatie:information: www.lcrdm.nl www.lcrdm.nl Contents Introduction 5 Tasks, responsibilities and roles as described in existing literature 7 Classification in task areas 8 What Dutch research institutes ask for in vacancies 9 National Data Stewardship survey 13 Interviews 18 Examples of (future/desired) embedding of data stewardship 23 Conclusion 27 Appendix 1: Links 30 Colophon 31 3 4 Introduction ‘Effective long-term scientific data stewardship touches on processes, standards, and best practices in multiple knowledge domains, including science, data management/preserva- tion, and technology.’ 1 (Peng, G. et al. 2018). 1 Scientific Stewardship in the Open Data and Big Data Era — Roles and Responsibi- Good research requires good data stewardship. Data stewardship encompasses all the lities of Stewards and Other different tasks and responsibilities that relate to caring for data during the various phases of Major Product Stakeholders Peng, G. et al.