{"product_id":"data-lakes-hardback-9781786305855","title":"Data Lakes (Hardback) 9781786305855","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eData Lakes\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eAnne Laurent (Edited by), A Laurent (Author), Dominique Laurent (Edited by), Cédrine Madera (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781786305855, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 13 March 2020\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e244 pages\u003cbr\u003e23.4 x 16 x 1.8 cm, 0.476 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThe concept of a data lake is less than 10 years old, but they are already hugely implemented within large companies. Their goal is to efficiently deal with ever-growing volumes of heterogeneous data, while also facing various sophisticated user needs. However, defining and building a data lake is still a challenge, as no consensus has been reached so far.  Data Lakes presents recent outcomes and trends in the field of data repositories. The main topics discussed are the data-driven architecture of a data lake; the management of metadata – supplying key information about the stored data, master data and reference data; the roles of linked data and fog computing in a data lake ecosystem; and how gravity principles apply in the context of data lakes.   A variety of case studies are also presented, thus providing the reader with practical examples of data lake management.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1. Introduction to Data Lakes: Definitions and Discussions \u003c\/b\u003e\u003cb\u003e1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAnne LAURENT, Dominique LAURENT and Cédrine MADERA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1. Introduction to data lakes 1\u003c\/p\u003e \u003cp\u003e1.2. Literature review and discussion 3\u003c\/p\u003e \u003cp\u003e1.3. The data lake challenges 7\u003c\/p\u003e \u003cp\u003e1.4. Data lakes versus decision-making systems 10\u003c\/p\u003e \u003cp\u003e1.5. Urbanization for data lakes 13\u003c\/p\u003e \u003cp\u003e1.6. Data lake functionalities 17\u003c\/p\u003e \u003cp\u003e1.7. Summary and concluding remarks 20\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2. Architecture of Data Lakes \u003c\/b\u003e\u003cb\u003e21\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHoussem CHIHOUB, Cédrine MADERA, Christoph QUIX and Rihan HAI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1. Introduction 21\u003c\/p\u003e \u003cp\u003e2.2. State of the art and practice 25\u003c\/p\u003e \u003cp\u003e2.2.1. Definition 25\u003c\/p\u003e \u003cp\u003e2.2.2. Architecture 25\u003c\/p\u003e \u003cp\u003e2.2.3. Metadata 26\u003c\/p\u003e \u003cp\u003e2.2.4. Data quality 27\u003c\/p\u003e \u003cp\u003e2.2.5. Schema-on-read 27\u003c\/p\u003e \u003cp\u003e2.3. System architecture 28\u003c\/p\u003e \u003cp\u003e2.3.1. Ingestion layer 29\u003c\/p\u003e \u003cp\u003e2.3.2. Storage layer 31\u003c\/p\u003e \u003cp\u003e2.3.3. Transformation layer 32\u003c\/p\u003e \u003cp\u003e2.3.4. Interaction layer 33\u003c\/p\u003e \u003cp\u003e2.4. Use case: the Constance system 33\u003c\/p\u003e \u003cp\u003e2.4.1. System overview 33\u003c\/p\u003e \u003cp\u003e2.4.2. Ingestion layer 35\u003c\/p\u003e \u003cp\u003e2.4.3. Maintenance layer 35\u003c\/p\u003e \u003cp\u003e2.4.4. Query layer 37\u003c\/p\u003e \u003cp\u003e2.4.5. Data quality control 38\u003c\/p\u003e \u003cp\u003e2.4.6. Extensibility and flexibility 38\u003c\/p\u003e \u003cp\u003e2.5. Concluding remarks 39\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3. Exploiting Software Product Lines and Formal Concept Analysis for the Design of Data Lake Architectures \u003c\/b\u003e\u003cb\u003e41\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMarianne HUCHARD, Anne LAURENT, Thérèse LIBOUREL, Cédrine MADERA and André MIRALLES\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1. Our expectations 41\u003c\/p\u003e \u003cp\u003e3.2. Modeling data lake functionalities 43\u003c\/p\u003e \u003cp\u003e3.3. Building the knowledge base of industrial data lakes 46\u003c\/p\u003e \u003cp\u003e3.4. Our formalization approach 49\u003c\/p\u003e \u003cp\u003e3.5. Applying our approach 51\u003c\/p\u003e \u003cp\u003e3.6. Analysis of our first results 53\u003c\/p\u003e \u003cp\u003e3.7. Concluding remarks 55\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4. Metadata in Data Lake Ecosystems \u003c\/b\u003e\u003cb\u003e57\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAsma ZGOLLI, Christine COLLET† and Cédrine MADERA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1. Definitions and concepts 57\u003c\/p\u003e \u003cp\u003e4.2. Classification of metadata by NISO 58\u003c\/p\u003e \u003cp\u003e4.2.1. Metadata schema 59\u003c\/p\u003e \u003cp\u003e4.2.2. Knowledge base and catalog 60\u003c\/p\u003e \u003cp\u003e4.3. Other categories of metadata 61\u003c\/p\u003e \u003cp\u003e4.3.1. Business metadata 61\u003c\/p\u003e \u003cp\u003e4.3.2. Navigational integration 63\u003c\/p\u003e \u003cp\u003e4.3.3. Operational metadata 63\u003c\/p\u003e \u003cp\u003e4.4. Sources of metadata 64\u003c\/p\u003e \u003cp\u003e4.5. Metadata classification 65\u003c\/p\u003e \u003cp\u003e4.6. Why metadata are needed 70\u003c\/p\u003e \u003cp\u003e4.6.1. Selection of information (re)sources 70\u003c\/p\u003e \u003cp\u003e4.6.2. Organization of information resources 70\u003c\/p\u003e \u003cp\u003e4.6.3. Interoperability and integration 70\u003c\/p\u003e \u003cp\u003e4.6.4. Unique digital identification 71\u003c\/p\u003e \u003cp\u003e4.6.5. Data archiving and preservation 71\u003c\/p\u003e \u003cp\u003e4.7. Business value of metadata 72\u003c\/p\u003e \u003cp\u003e4.8. Metadata architecture 75\u003c\/p\u003e \u003cp\u003e4.8.1. Architecture scenario 1: point-to-point metadata architecture 75\u003c\/p\u003e \u003cp\u003e4.8.2. Architecture scenario 2: hub and spoke metadata architecture 76\u003c\/p\u003e \u003cp\u003e4.8.3. Architecture scenario 3: tool of record metadata architecture 78\u003c\/p\u003e \u003cp\u003e4.8.4. Architecture scenario 4: hybrid metadata architecture 79\u003c\/p\u003e \u003cp\u003e4.8.5. Architecture scenario 5: federated metadata architecture 80\u003c\/p\u003e \u003cp\u003e4.9. Metadata management 82\u003c\/p\u003e \u003cp\u003e4.10. Metadata and data lakes 86\u003c\/p\u003e \u003cp\u003e4.10.1. Application and workload layer 86\u003c\/p\u003e \u003cp\u003e4.10.2. Data layer 88\u003c\/p\u003e \u003cp\u003e4.10.3. System layer 90\u003c\/p\u003e \u003cp\u003e4.10.4. Metadata types 90\u003c\/p\u003e \u003cp\u003e4.11. Metadata management in data lakes 92\u003c\/p\u003e \u003cp\u003e4.11.1. Metadata directory 93\u003c\/p\u003e \u003cp\u003e4.11.2. Metadata storage 93\u003c\/p\u003e \u003cp\u003e4.11.3. Metadata discovery 94\u003c\/p\u003e \u003cp\u003e4.11.4. Metadata lineage 94\u003c\/p\u003e \u003cp\u003e4.11.5. Metadata querying 95\u003c\/p\u003e \u003cp\u003e4.11.6. Data source selection 95\u003c\/p\u003e \u003cp\u003e4.12. Metadata and master data management 96\u003c\/p\u003e \u003cp\u003e4.13. Conclusion 96\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5. A Use Case of Data Lake Metadata Management \u003c\/b\u003e\u003cb\u003e97\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eImen MEGDICHE, Franck RAVAT and Yan ZHAO\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1. Context 97\u003c\/p\u003e \u003cp\u003e5.1.1. Data lake definition 98\u003c\/p\u003e \u003cp\u003e5.1.2. Data lake functional architecture 100\u003c\/p\u003e \u003cp\u003e5.2. Related work 103\u003c\/p\u003e \u003cp\u003e5.2.1. Metadata classification 104\u003c\/p\u003e \u003cp\u003e5.2.2. Metadata management 105\u003c\/p\u003e \u003cp\u003e5.3. Metadata model 106\u003c\/p\u003e \u003cp\u003e5.3.1. Metadata classification 106\u003c\/p\u003e \u003cp\u003e5.3.2. Schema of metadata conceptual model 110\u003c\/p\u003e \u003cp\u003e5.4. Metadata implementation 111\u003c\/p\u003e \u003cp\u003e5.4.1. Relational database 112\u003c\/p\u003e \u003cp\u003e5.4.2. Graph database 115\u003c\/p\u003e \u003cp\u003e5.4.3. Comparison of the solutions 119\u003c\/p\u003e \u003cp\u003e5.5. Concluding remarks 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6. Master Data and Reference Data in Data Lake Ecosystems \u003c\/b\u003e\u003cb\u003e123\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eCédrine MADERA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1. Introduction to master data management 125\u003c\/p\u003e \u003cp\u003e6.1.1. What is master data? 125\u003c\/p\u003e \u003cp\u003e6.1.2. Basic definitions 125\u003c\/p\u003e \u003cp\u003e6.2. Deciding what to manage 126\u003c\/p\u003e \u003cp\u003e6.2.1. Behavior 126\u003c\/p\u003e \u003cp\u003e6.2.2. Lifecycle 127\u003c\/p\u003e \u003cp\u003e6.2.3. Cardinality 127\u003c\/p\u003e \u003cp\u003e6.2.4. Lifetime 128\u003c\/p\u003e \u003cp\u003e6.2.5. Complexity 128\u003c\/p\u003e \u003cp\u003e6.2.6. Value 128\u003c\/p\u003e \u003cp\u003e6.2.7. Volatility 129\u003c\/p\u003e \u003cp\u003e6.2.8. Reuse 129\u003c\/p\u003e \u003cp\u003e6.3. Why should I manage master data? 130\u003c\/p\u003e \u003cp\u003e6.4. What is master data management? 131\u003c\/p\u003e \u003cp\u003e6.4.1. How do I create a master list? 136\u003c\/p\u003e \u003cp\u003e6.4.2. How do I maintain a master list? 138\u003c\/p\u003e \u003cp\u003e6.4.3. Versioning and auditing 139\u003c\/p\u003e \u003cp\u003e6.4.4. Hierarchy management 140\u003c\/p\u003e \u003cp\u003e6.5. Master data and the data lake 141\u003c\/p\u003e \u003cp\u003e6.6. Conclusion 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7. Linked Data Principles for Data Lakes \u003c\/b\u003e\u003cb\u003e145\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAlessandro ADAMOU and Mathieu D’AQUIN\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1. Basic principles 145\u003c\/p\u003e \u003cp\u003e7.2. Using Linked Data in data lakes 148\u003c\/p\u003e \u003cp\u003e7.2.1. Distributed data storage and querying with linked data graphs 151\u003c\/p\u003e \u003cp\u003e7.2.2. Describing and profiling data sources 153\u003c\/p\u003e \u003cp\u003e7.2.3. Integrating internal and external data 156\u003c\/p\u003e \u003cp\u003e7.3. Limitations and issues 159\u003c\/p\u003e \u003cp\u003e7.4. The smart cities use case 162\u003c\/p\u003e \u003cp\u003e7.4.1. The MK Data Hub 163\u003c\/p\u003e \u003cp\u003e7.4.2. Linked data in the MK Data Hub 165\u003c\/p\u003e \u003cp\u003e7.5. Take-home message 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8. Fog Computing \u003c\/b\u003e\u003cb\u003e171\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eArnault IOUALALEN\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1. Introduction 171\u003c\/p\u003e \u003cp\u003e8.2. A little bit of context 171\u003c\/p\u003e \u003cp\u003e8.3. Every machine talks 172\u003c\/p\u003e \u003cp\u003e8.4. The volume paradox 173\u003c\/p\u003e \u003cp\u003e8.5. The fog, a shift in paradigm 174\u003c\/p\u003e \u003cp\u003e8.6. Constraint environment challenges 176\u003c\/p\u003e \u003cp\u003e8.7. Calculations and local drift 177\u003c\/p\u003e \u003cp\u003e8.7.1. A short memo about computer arithmetic 178\u003c\/p\u003e \u003cp\u003e8.7.2. Instability from within 179\u003c\/p\u003e \u003cp\u003e8.7.3. Non-determinism from outside 180\u003c\/p\u003e \u003cp\u003e8.8. Quality is everything 181\u003c\/p\u003e \u003cp\u003e8.9. Fog computing versus cloud computing and edge computing 184\u003c\/p\u003e \u003cp\u003e8.10. Concluding remarks: fog computing and data lake 185\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9. The Gravity Principle in Data Lakes \u003c\/b\u003e\u003cb\u003e187\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAnne LAURENT, Thérèse LIBOUREL, Cédrine MADERA and André MIRALLES\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1. Applying the notion of gravitation to information systems 187\u003c\/p\u003e \u003cp\u003e9.1.1. Universal gravitation 187\u003c\/p\u003e \u003cp\u003e9.1.2. Gravitation in information systems 189\u003c\/p\u003e \u003cp\u003e9.2. Impact of gravitation on the architecture of data lakes 193\u003c\/p\u003e \u003cp\u003e9.2.1. The case where data are not moved 195\u003c\/p\u003e \u003cp\u003e9.2.2. The case where processes are not moved 197\u003c\/p\u003e \u003cp\u003e9.2.3. The case where the environment blocks the move 198\u003c\/p\u003e \u003cp\u003eGlossary 201\u003c\/p\u003e \u003cp\u003eReferences 207\u003c\/p\u003e \u003cp\u003eList of Authors 217\u003c\/p\u003e \u003cp\u003eIndex 219\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-ISTE","offers":[{"title":"Brand New","offer_id":52446749491480,"sku":"9781786305855","price":105.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781786305855.jpg?v=1785112762","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/data-lakes-hardback-9781786305855","provider":"Freshly Printed Books","version":"1.0","type":"link"}