Data warehousing Books
Pearson Education (US) Big Data Fundamentals
Book SynopsisThomas Erl is a top-selling IT author, founder of Arcitura Education and series editor of the Prentice Hall Service Technology Series from Thomas Erl. With more than 200,000 copies in print worldwide, his books have become international bestsellers and have been formally endorsed by senior members of major IT organizations, such as IBM, Microsoft, Oracle, Intel, Accenture, IEEE, HL7, MITRE, SAP, CISCO, HP and many others. As CEO of Arcitura Education Inc., Thomas has led the development of curricula for the internationally recognized Big Data Science Certified Professional (BDSCP), Cloud Certified Professional (CCP) and SOA Certified Professional (SOACP) accreditation programs, which have established a series of formal, vendor-neutral industry certifications obtained by thousands of IT professionals around the world. Thomas has toured more than 20 countries as a speaker and instructor. More than 100 articles and interviews by Thomas have been published in numerous publicaTable of ContentsAcknowledgments xviiReader Services xviiiPART I: THE FUNDAMENTALS OF BIG DATAChapter 1: Understanding Big Data 3 Concepts and Terminology 5 Datasets 5 Data Analysis 6 Data Analytics 6 Descriptive Analytics 8 Diagnostic Analytics 9 Predictive Analytics 10 Prescriptive Analytics 11 Business Intelligence (BI) 12 Key Performance Indicators (KPI) 12 Big Data Characteristics 13 Volume 14 Velocity 14 Variety 15 Veracity 16 Value 16 Different Types of Data 17 Structured Data 18 Unstructured Data 19 Semi-structured Data 19 Metadata 20 Case Study Background 20 History 20 Technical Infrastructure and Automation Environment 21 Business Goals and Obstacles 22 Case Study Example 24 Identifying Data Characteristics 26 Volume 26 Velocity 26 Variety 26 Veracity 26 Value 27 Identifying Types of Data 27 Chapter 2: Business Motivations and Drivers for Big Data Adoption 29 Marketplace Dynamics 30 Business Architecture 33 Business Process Management 36 Information and Communications Technology 37 Data Analytics and Data Science 37 Digitization 38 Affordable Technology and Commodity Hardware 38 Social Media 39 Hyper-Connected Communities and Devices 40 Cloud Computing 40 Internet of Everything (IoE) 42 Case Study Example 43 Chapter 3: Big Data Adoption and Planning Considerations 47 Organization Prerequisites 49 Data Procurement 49 Privacy 49 Security 50 Provenance 51 Limited Realtime Support 52 Distinct Performance Challenges 53 Distinct Governance Requirements 53 Distinct Methodology 53 Clouds 54 Big Data Analytics Lifecycle 55 Business Case Evaluation 56 Data Identification 57 Data Acquisition and Filtering 58 Data Extraction 60 Data Validation and Cleansing 62 Data Aggregation and Representation 64 Data Analysis 66 Data Visualization 68 Utilization of Analysis Results 69 Case Study Example 71 Big Data Analytics Lifecycle 73 Business Case Evaluation 73 Data Identification 74 Data Acquisition and Filtering 74 Data Extraction 74 Data Validation and Cleansing 75 Data Aggregation and Representation 75 Data Analysis 75 Data Visualization 76 Utilization of Analysis Results 76 Chapter 4: Enterprise Technologies and Big Data Business Intelligence 77 Online Transaction Processing (OLTP) 78 Online Analytical Processing (OLAP) 79 Extract Transform Load (ETL) 79 Data Warehouses 80 Data Marts 81 Traditional BI 82 Ad-hoc Reports 82 Dashboards 82 Big Data BI 84 Traditional Data Visualization 84 Data Visualization for Big Data 85 Case Study Example 86 Enterprise Technology 86 Big Data Business Intelligence 87 PART II: STORING AND ANALYZING BIG DATAChapter 5: Big Data Storage Concepts 91 Clusters 93 File Systems and Distributed File Systems 93 NoSQL 94 Sharding 95 Replication 97 Master-Slave 98 Peer-to-Peer 100 Sharding and Replication 103 Combining Sharding and Master-Slave Replication 104 Combining Sharding and Peer-to-Peer Replication 105 CAP Theorem 106 ACID 108 BASE 113 Case Study Example 117 Chapter 6: Big Data Processing Concepts 119 Parallel Data Processing 120 Distributed Data Processing 121 Hadoop 122 Processing Workloads 122 Batch 123 Transactional 123 Cluster 124 Processing in Batch Mode 125 Batch Processing with MapReduce 125 Map and Reduce Tasks 126 Map 127 Combine 127 Partition 129 Shuffle and Sort 130 Reduce 131 A Simple MapReduce Example 133 Understanding MapReduce Algorithms 134 Processing in Realtime Mode 137 Speed Consistency Volume (SCV) 137 Event Stream Processing 140 Complex Event Processing 141 Realtime Big Data Processing and SCV 141 Realtime Big Data Processing and MapReduce 142 Case Study Example 143 Processing Workloads 143 Processing in Batch Mode 143 Processing in Realtime 144 Chapter 7: Big Data Storage Technology 145 On-Disk Storage Devices 147 Distributed File Systems 147 RDBMS Databases 149 NoSQL Databases 152 Characteristics 152 Rationale 153 Types 154 Key-Value 156 Document 157 Column-Family 159 Graph 160 NewSQL Databases 163 In-Memory Storage Devices 163 In-Memory Data Grids 166 Read-through 170 Write-through 170 Write-behind 172 Refresh-ahead 172 In-Memory Databases 175 Case Study Example 179 Chapter 8: Big Data Analysis Techniques 181 Quantitative Analysis 183 Qualitative Analysis 184 Data Mining 184 Statistical Analysis 184 A/B Testing 185 Correlation 186 Regression 188 Machine Learning 190 Classification (Supervised Machine Learning) 190 Clustering (Unsupervised Machine Learning) 191 Outlier Detection 192 Filtering 193 Semantic Analysis 195 Natural Language Processing 195 Text Analytics 196 Sentiment Analysis 197 Visual Analysis 198 Heat Maps 198 Time Series Plots 200 Network Graphs 201 Spatial Data Mapping 202 Case Study Example 204 Correlation 204 Regression 204 Time Series Plot 205 Clustering 205 Classification 205 Appendix A: Case Study Conclusion 207About the Authors 211 Thomas Erl 211 Wajid Khattak 211 Paul Buhler 212 Index 213
£28.02
Pearson Education (US) Foundational Python for Data Science
Book Synopsis Kennedy Behrman is a veteran software and data engineer. He first used Python writing asset management systems in the Visual Effects industry. He then moved into the startup world, using Python at startups using machine learning to characterize videos and predict the social media power of athletes. Table of ContentsPreface xiii I: Learning Python in a Notebook Environment 1 1 Introduction to Notebooks 3 2 Fundamentals of Python 13 3 Sequences 25 4 Other Data Structures 37 5 Execution Control 55 6 Functions 67 II: Data Science Libraries 83 7 NumPy 85 8 SciPy 103 9 Pandas 113 10 Visualization Libraries 135 11 Machine Learning Libraries 153 12 Natural Language Toolkit 159 III: Intermediate Python 171 13 Functional Programming 173 14 Object-Oriented Programming 187 15 Other Topics 201 A Answers to End-of-Chapter Questions 215 Index 221
£42.74
Pearson Education (US) Product Analytics
Book SynopsisJoanne Rodrigues is an experienced data scientist with master's degrees in mathematics, political science, and demography. She has six years of experience in statistical computing and R programming, as well as experience with Python for data science applications. Her management experience at enterprise companies leverages her ability to understand human behavior by using economic and sociological theory in the context of complex mathematical models.Table of Contents Part I: Qualitative Methodology Chapter 1: Data in Action: A Model of a Dinner Party Chapter 2: Building a Theory of the Universe–The Social Universe Chapter 3: The Coveted Goal Post: How to Change User Behavior Part II: Basic Statistical Methods Chapter 4: Distributions in User Analytics Chapter 5: Retained? Metric Creation and Interpretation Chapter 6: Why Are My Users Leaving? The Ins and Outs of A/B Testing Part III: Predictive Methods Chapter 7: Modeling the User Space: k-Means and PCA Chapter 8: Predicting User Behavior: Regression, Decision Trees, and Support Vector Machines Chapter 9: Forecasting Population Changes in Product: Demographic Projections Part IV: Causal Inference Methods Chapter 10: In Pursuit of the Experiment: Natural Experiments and the Difference-in-Difference Design Chapter 11: In Pursuit of the Experiment Continued: Regression Discontinuity, Time Series Modelling, and Interrupted Time Series Approaches Chapter 12: Developing Heuristics in Practice: Statistical Matching and Hill’s Causality Conditions Chapter 13: Uplift Modeling Part V: Basic, Predictive, and Causal Inference Methods in R Chapter 14: Metrics in R Chapter 15: A/B Testing, Predictive Modeling, and Population Projection in R Chapter 16: Regression Discontinuity, Matching, and Uplift in R Conclusion
£36.09
O'Reilly Media Data Pipelines Pocket Reference
Book SynopsisData 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.
£20.39
O'Reilly Media Efficient MySQL Performance
Book SynopsisThis practical book bridges the gap by teaching software engineers mid-level MySQL knowledge beyond the fundamentals, but well shy of deep-level internals required by database administrators (DBAs).
£39.74
O'Reilly Media Fundamentals of Data Observability
Book Synopsis
£39.74
Elsevier Science Data Architecture A Primer for the Data Scientist
Book SynopsisTable of Contents1. An Introduction to Data Architecture2. The End-State Architecture - The "World Map"3. Transformations in the End-State Architecture4. A Brief History of Big Data5. The Siloed Application Environment6. Introduction to Data Vault 2.07. The Operational Environment: A Short History8. A Brief History of Data Architecture9. Repetitive Analytics: Some Basics10. Nonrepetitive Data11. Operational Analytics: Response Time12. Operational Analytics13. Personal Analytics14. Data Models Across the End-State Architecture15. The System of Record16. Business Value and the End-State Architecture17. Managing Text18. An Introduction to Data Visualizations
£52.24
BPB Publications Querying Databricks with Spark SQL: Leverage SQL
Book Synopsis
£31.34
BPB Publications Data Engineering Design Patterns
Book SynopsisKey data engineering patterns. Data ingestion and processing patterns. Modern architectures like Lambda. Explore time-tested data patterns of ETL and ELT. Modern data systems like data lake and medallion architectures.
£29.99
Pearson Education (US) Pandas for Everyone
Book SynopsisDaniel Chen is a graduate student in the Interdisciplinary PhD program in Genetics, Bioinformatics & Computational Biology (GBCB) at Virginia Polytechnic Institute and State University (Virginia Tech). He is involved with Software Carpentry as an instructor, Mentoring Committee Member, and currently serves as the Assessment Committee Chair. He completed his Masters in Public Health at Columbia University Mailman School of Public Health in Epidemiology with a certificate in Advanced Epidemiology and currently extending his Master's thesis work in the Social and Decision Analytics Laboratory under the Virginia Bioinformatics Institute on attitude diffusion in social networks.Table of ContentsForeword by Anne M. Brown xxiii Foreword by Jared Lander xxv Preface xxvii Changes in the Second Edition xxxix Part I: Introduction 1 Chapter 1. Pandas DataFrame Basics 3 Learning Objectives 3 1.1 Introduction 3 1.2 Load Your First Data Set 4 1.3 Look at Columns, Rows, and Cells 6 1.4 Grouped and Aggregated Calculations 23 1.5 Basic Plot 27 Conclusion 28 Chapter 2. Pandas Data Structures Basics 31 Learning Objectives 31 2.1 Create Your Own Data 31 2.2 The Series 33 2.3 The DataFrame 42 2.4 Making Changes to Series and DataFrames 45 2.5 Exporting and Importing Data 52 Conclusion 63 Chapter 3. Plotting Basics 65 Learning Objectives 65 3.1 Why Visualize Data? 65 3.2 Matplotlib Basics 66 3.3 Statistical Graphics Using matplotlib 72 3.4 Seaborn 78 3.5 Pandas Plotting Method 111 Conclusion 115 Chapter 4. Tidy Data 117 Learning Objectives 117 Note About This Chapter 117 4.1 Columns Contain Values, Not Variables 118 4.2 Columns Contain Multiple Variables 122 4.3 Variables in Both Rows and Columns 126 Conclusion 129 Chapter 5. Apply Functions 131 Learning Objectives 131 Note About This Chapter 131 5.1 Primer on Functions 131 5.2 Apply (Basics) 133 5.3 Vectorized Functions 138 5.4 Lambda Functions (Anonymous Functions) 141 Conclusion 142 Part II: Data Processing 143 Chapter 6. Data Assembly 145 Learning Objectives 145 6.1 Combine Data Sets 145 6.2 Concatenation 146 6.3 Observational Units Across Multiple Tables 154 6.4 Merge Multiple Data Sets 160 Conclusion 167 Chapter 7. Data Normalization 169 Learning Objectives 169 7.1 Multiple Observational Units in a Table (Normalization) 169 Conclusion 173 Chapter 8. Groupby Operations: Split-Apply-Combine 175 Learning Objectives 175 8.1 Aggregate 176 8.2 Transform 184 8.3 Filter 188 8.4 The pandas.core.groupby.DataFrameGroupBy object 190 8.5 Working with a MultiIndex 195 Conclusion 199 Part III: Data Types 203 Chapter 9. Missing Data 203 Learning Objectives 203 9.1 What Is a NaN Value? 203 9.2 Where Do Missing Values Come From? 205 9.3 Working with Missing Data 210 9.4 Pandas Built-In NA Missing 216 Conclusion 218 Chapter 10. Data Types 219 Learning Objectives 219 10.1 Data Types 219 10.2 Converting Types 220 10.3 Categorical Data 225 Conclusion 227 Chapter 11. Strings and Text Data 229 Introduction 229 Learning Objectives 229 11.1 Strings 229 11.2 String Methods 233 11.3 More String Methods 234 11.4 String Formatting (F-Strings) 236 11.5 Regular Expressions (RegEx) 239 11.6 The regex Library 247 Conclusion 247 Chapter 12. Dates and Times 249 Learning Objectives 249 12.1 Python's datetime Object 249 12.2 Converting to datetime 250 12.3 Loading Data That Include Dates 253 12.4 Extracting Date Components 254 12.5 Date Calculations and Timedeltas 257 12.6 Datetime Methods 259 12.7 Getting Stock Data 261 12.8 Subsetting Data Based on Dates 263 12.9 Date Ranges 266 12.10 Shifting Values 270 12.11 Resampling 276 12.12 Time Zones 278 12.13 Arrow for Better Dates and Times 280 Conclusion 280 Part IV: Data Modeling 281 Chapter 13. Linear Regression (Continuous Outcome Variable) 283 13.1 Simple Linear Regression 283 13.2 Multiple Regression 287 13.3 Models with Categorical Variables 289 13.4 One-Hot Encoding in scikit-learn with Transformer Pipelines 294 Conclusion 296 Chapter 14. Generalized Linear Models 297 About This Chapter 297 14.1 Logistic Regression (Binary Outcome Variable) 297 14.2 Poisson Regression (Count Outcome Variable) 304 14.3 More Generalized Linear Models 308 Conclusion 309 Chapter 15. Survival Analysis 311 15.1 Survival Data 311 15.2 Kaplan Meier Curves 312 15.3 Cox Proportional Hazard Model 314 Conclusion 317 Chapter 16. Model Diagnostics 319 16.1 Residuals 319 16.2 Comparing Multiple Models 324 16.3 k-Fold Cross-Validation 329 Conclusion 334 Chapter 17. Regularization 335 17.1 Why Regularize? 335 17.2 LASSO Regression 337 17.3 Ridge Regression 338 17.4 Elastic Net 340 17.5 Cross-Validation 341 Conclusion 343 Chapter 18. Clustering 345 18.1 k-Means 345 18.2 Hierarchical Clustering 351 Conclusion 356 Part V. Conclusion 357 Chapter 19. Life Outside of Pandas 359 19.1 The (Scientific) Computing Stack 359 19.2 Performance 360 19.3 Dask 360 19.4 Siuba 360 19.5 Ibis 361 19.6 Polars 361 19.7 PyJanitor 361 19.8 Pandera 361 19.9 Machine Learning 361 19.10 Publishing 362 19.11 Dashboards 362 Conclusion 362 Chapter 20. It's Dangerous To Go Alone! 363 20.1 Local Meetups 363 20.2 Conferences 363 20.3 The Carpentries 364 20.4 Podcasts 364 20.5 Other Resources 365 Conclusion 365 Appendices 367 A. Concept Maps 369B. Installation and Setup 373C. Command Line 377D. Project Templates 379E. Using Python 381F. Working Directories 383G. Environments 385H. Install Packages 389I. Importing Libraries 391J. Code Style 393K. Containers: Lists, Tuples, and Dictionaries 395L. Slice Values 399M. Loops 401N. Comprehensions 403O. Functions 405P. Ranges and Generators 409Q. Multiple Assignment 413R. Numpy ndarray 415S. Classes 417T. SettingWithCopyWarning 419U. Method Chaining 423V. Timing Code 427W. String Formatting 429X. Conditionals (if-elif-else) 433Y. New York ACS Logistic Regression Example 435Z. Replicating Results in R 443 Index 451
£34.19
Manning Publications Data Pipelines with Apache Airflow Second Edition
a huge range and FREE tracked UK delivery on ALL orders.
£41.39
O'Reilly Media The Cloud Data Lake
Book SynopsisAuthor Rukmani Gopalan, a product management leader and data enthusiast, guides data architects and engineers through the major aspects of working with a cloud data lake, from design considerations and best practices to data format optimizations, performance optimization, cost management, and governance.
£39.74
O'Reilly Media Learning Google Analytics
Book SynopsisAuthor Mark Edmondson, Google Developer Expert for Google Analytics and Google Cloud, provides a concise yet comprehensive overview of GA4 and its cloud integrations.
£39.74
Elsevier Science Data Governance
Book SynopsisTrade Review"John Ladley’s latest book is the second edition of his Data Governance book, first published in 2012. If you know the first edition of this book, you’ll certainly appreciate this updated second edition. Reading this book will provide you with a comprehensive overview of why data governance is needed, how to design, initiate, and execute a data governance program, and how to keep the program sustainable. There is a ton of new content in this second edition, including new case studies, updated industry details, and updated coverage of the available data governance tools that can help.The book will be useful to you whether you are a novice or a seasoned professional. At the heart of this book is the framework that Ladley communicates that you can follow to build and maintain successful data governance at your organization. In combination with the use cases that he walks through in the book, you have a powerful guide for launching your data governance program. Useful for both small and large organizations, be sure to pick up a copy of this book if you are charged with any aspect of data management and data governance within your shop." --Data and Technology TodayTable of Contents1. Prologue – An Executive Overview 2. Introduction 3. Data Literacy and Concepts 4 Overview – A Day in the Life of a data governance program and its capabilities 5. The data governance business case 6. Process overview for deploying data governance 7. Scope and initiation 8. Assess 9. Vision 10. Align and business value 11. Functional design 12. Governing framework design 13. Road map 14. Rollout and sustain 15. Data governance artifacts and tools 16. Final remarks Appendix Data Governance Charters Template Data Governance Orientation and ongoing knowledge transfer template Stakeholder Analysis Leadership Alignment Assessment Communications Plan Training Plan Example Post-rollout Checklist
£44.64
Pearson Education Exam Ref DP600 Implementing Analytics Solutions
Book SynopsisDaniil Maslyuk is an independent business intelligence consultant, trainer, and speaker who specializes in Microsoft Power BI. Daniil blogs at xxlbi.com and tweets as @DMaslyuk. Johnny Winter is a data and analytics consultant who has been working with business intelligence software since 2007, specializing in the Microsoft data platform since 2016. He's a self-confessed business intelligence geek, and in his spare time runs the website and YouTube channel Greyskull Analytics, where he likes to nerd out about all things analytics. Štepán Rešl is a lead technical consultant and a Microsoft MVP in the Data Platform category. As a technical consultant, Štepán focuses on assisting medium and large organizations in deploying and maintaining their data solutions. He is also a speaker and co-organizer of conferences. In his spare time, he runs a blog called DataMeerkat, where he focuses on topics related to
£35.99
John Wiley & Sons Inc Kimballs Data Warehouse Toolkit Classics 3 Volume
Book Synopsis
£104.50
Springer Principles of Data Mining
Book SynopsisIntroduction to Data Mining.- Data for Data Mining.- Introduction to Classification: Naïve Bayes and Nearest Neighbour.- Using Decision Trees for Classification.- Decision Tree Induction: Using Entropy for Attribute Selection.- Decision Tree Induction: Using Frequency Tables for Attribute Selection.- Estimating the Predictive Accuracy of a Classifier.- Continuous Attributes.- Avoiding Overfitting of Decision Trees.- More About Entropy.- Inducing Modular Rules for Classification.- Measuring the Performance of a Classifier.- Dealing with Large Volumes of Data.- Ensemble Classification.- Comparing Classifiers.- Associate Rule Mining I.- Associate Rule Mining II.- Associate Rule Mining III.- Clustering.- Mining.- Classifying Streaming Data.- Classifying Streaming Data II: Time-dependent Data.- An Introduction to Neural Networks.- Appendix A Essential Mathematics.- Appendix B Datasets.- Appendix C Sources of Further Information.- Appendix D Glossary and Notation.- Appendix E SolutioTable of ContentsIntroduction to Data Mining.- Data for Data Mining.- Introduction to Classification: Naïve Bayes and Nearest Neighbour.- Using Decision Trees for Classification.- Decision Tree Induction: Using Entropy for Attribute Selection.- Decision Tree Induction: Using Frequency Tables for Attribute Selection.- Estimating the Predictive Accuracy of a Classifier.- Continuous Attributes.- Avoiding Overfitting of Decision Trees.- More About Entropy.- Inducing Modular Rules for Classification.- Measuring the Performance of a Classifier.- Dealing with Large Volumes of Data.- Ensemble Classification.- Comparing Classifiers.- Associate Rule Mining I.- Associate Rule Mining II.- Associate Rule Mining III.- Clustering.- Mining.- Classifying Streaming Data.- Classifying Streaming Data II: Time-dependent Data.- An Introduction to Neural Networks.- Appendix A – Essential Mathematics.- Appendix B – Datasets.- Appendix C – Sources of Further Information.- Appendix D – Glossary and Notation.- Appendix E – Solutions to Self-assessment Exercises.- Index.
£37.99
APress Oracle Database Upgrade and Migration Methods
Book Synopsis Learn all of the available upgrade and migration methods in detail to move to Oracle Database version 12c. You will become familiar with database upgrade best practices to complete the upgrade in an effective manner and understand the Oracle Database 12c patching process. So it''s time to upgrade Oracle Database to version 12c and you need to choose the appropriate method while considering issues such as downtime. This book explains all of the available upgrade and migration methods so you can choose the one that suits your environment. You will be aware of the practical issues and proactive measures to take to upgrade successfully and reduce unexpected issues. With every release of Oracle Database there are new features and fixes to bugs identified in previous versions. As each release becomes obsolete, existing databases need to be upgraded. Oracle Database Upgrade and Migration Methods explains each method along Table of Contents PART I: packetC Background 1 CHAPTER 1: Getting Started 3 Introduction to Database upgrade Necessities of Database upgrade Benefits of Database upgrade Hurdles that affect Database upgrade decision Types of Database upgrade Things to consider before upgrade Engineers involved in upgrade activity Upgrade compatibility matrix Best practices of Database upgrade Database Migration Situations demand Migration Things to consider before migration summary PART II: Language Reference 53 n CHAPTER 2: Database Upgrade methods 55 DBUA Manual/Command line upgrade Export/Import Datapump Transportable Tablespace Golden Gate Create Table as Select (CTAS) Transient Logical Standby Full Transportable Tablespace Summary n CHAPTER 3: Comparison between upgrade methods 151 Comparison between methods 9 Real Application testing (RAT) 10 How to choose best Upgrade method 11 Summary n CHAPTER 4: Upgrade using Database backup 159 Cold backup Hot backup (User-Managed) Logical backup (expdp/impdp) RMAN backup (using duplicate option) Summary n CHAPTER 5: Database Migration methods 171 Export/Import Datapump Transportable Tablespace (TTS) Golden Gate Copy table as select (CTAS) Transport Database Heterogenous Standby database Oracle Streams Summary n CHAPTER 6: Migration of Oracle database from Non-ASM to ASM 175 Introduction Moving Datafiles Online from NON-ASM < to ASM Migrating Oracle 12c CDB with PDBs from NON ASM to ASM using EM Cloud Control 13c ............... Migrating Oracle 12c CDB with PDBs from NON ASM to ASM using RMAN Summary n CHAPTER 7: GI and DB upgrade in RAC environment 205 Introduction CVU Pre-Upgrade Check tool Execution Steps for ORAchk Rolling upgrade for Oracle GI Upgrading 11g RAC to 12c RAC using DBUA Upgrading 11g RAC to 12c RAC Manual Upgrading 11g RAC to 12c RAC using EM 13c Summary PART III: Developing Applications 215 n CHAPTER 8: Database upgrade in DG environment 217 Dummy Text Dummy Text Virtual Dummy Text Dummy Text Dummy Text Dummy Text Flow Dummy Text Summary n CHAPTER 9: Database upgrade in EBS environment 223 Prerequisite steps Preupgrade steps Upgrade steps Post upgrade steps Summary CHAPTER 10: Database upgrade in 12c Multitenant environment Migrate lower version database to Multitenant architecture Container database upgrade Pluggable database upgrade Summary n CHAPTER 11: Databases migrate in Multitenant environment 237 Pluggable database migrate Need for Migrate Migration steps Summary n CHAPTER 12: Oracle Database Patching Stratergies 245 Patching Introduction Opatch tool Types of patches Patch apply stratergies (online and offline patching).... PSU and SPU patching Patch apply in RAC and DG environment Datapatch Queryable patch inventory Summary n CHAPTER 13: Database Downgrade 263 Introduction Limitations of Oracle database downgrade Database downgrade steps Downgrade using database flashback Summary n CHAPTER 14: Database upgrade in 12.2 281 Preupgrade checks Upgrade Emulation DBUA Manual Database upgrade Pluggable database upgrade Downgrade 12.2 database to earlier version............... Summary n n APPENDIX A: Reference Tables 383 n APPENDIX B: Dummy Text 395 INDEX 433
£44.99
O'Reilly Media 97 Things Every Data Engineer Should Know
Book SynopsisWith 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, StitchFix, Microsoft and Capital One share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges.
£29.99
Manning Publications Graph Databases in Action
Book SynopsisGraph Databases in Action teaches readers everything they need to know to begin building and running applications powered by graph databases. Right off the bat, seasoned graph database experts introduce readers to just enough graph theory, the graph database ecosystem, and a variety of datastores. They also explore modelling basics in action with real-world examples, then go hands-on with querying, coding traversals, parsing results, and other essential tasks as readers build their own graph-backed social network app complete with a recommendation engine! Key Features · Graph database fundamentals · An overview of the graph database ecosystem · Relational vs. graph database modelling · Querying graphs using Gremlin · Real-world common graph use cases For readers with basic Java and application development skills building in RDBMS systems such as Oracle, SQL Server, MySQL, and Postgres. No experience with graph databases is required. About the technology Graph databases store interconnected data in a more natural form, making them superior tools for representing data with rich relationships. Unlike in relational database management systems (RDBMS), where a more rigid view of data connections results in the loss of valuable insights, in graph databases, data connections are first priority. Dave Bechberger has extensive experience using graph databases as a product architect and a consultant. He’s spent his career leveraging cutting-edge technologies to build software in complex data domains such as bioinformatics, oil and gas, and supply chain management. He’s an active member of the graph community and has presented on a wide variety of graph-related topics at national and international conferences. Josh Perryman is technologist with over two decades of diverse experience building and maintaining complex systems, including high performance computing (HPC) environments. Since 2014 he has focused on graph databases, especially in distributed or big data environments, and he regularly blogs and speaks at conferences about graph databases.
£37.99
Springer International Publishing AG New Horizons for a Data-Driven Economy: A Roadmap
Book SynopsisIn 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. Trade Review“The book provides rich information on the different processes involved in big data value chain and explains each process with case studies in diverse industrial sectors. … the book can help academic researchers, undergraduate students, and graduate students because it contains information about big data and its recent development and generates some research ideas. This book can also facilitate government officials and executives of different organizations to consider future roadmap by taking advantage of big data.” (Sunny Sun and Rob Law, Information Technology & Tourism, Vol. 17, 2017)Table of Contents
£33.74
Springer International Publishing AG The Semantic Web: ESWC 2017 Satellite Events: ESWC 2017 Satellite Events, Portorož, Slovenia, May 28 – June 1, 2017, Revised Selected Papers
Book SynopsisThis book constitutes the thoroughly refereed post-conference proceedings of the Satellite Events of the 14th European Conference on the Semantic Web, ESWC 2017, held in Portoroz, Slovenia, in May/June2017.The volume contains 8 poster and 24 demonstration papers, selected from 105 submissions. Additionally, this book includes a selection of 13 best workshop papers. The papers cover various aspects of the semantic web.The chapter 'Scholia, Scientometrics and Wikidata' is available open access under a CC BY 4.0 license via link.springer.com.Table of ContentsQuerying the Web of Data.- Semantic Web Solutions for Large-Scale Biomedical Data Analytics.- Scientometrics.- RDF Stream Processing.- Emotions, Modality, Sentiment Analysis and the Semantic Web.- Applications of Semantic Web Technologies in Robotics.- Linked Data and Distributed Ledgers.- Linked Data Quality.- Semantic Deep Learning.- Humanities in the Semantic Web.
£49.49
Unknown SQL Crash Course
£28.49
Independently Published SQL Made Easy: Tips and Tricks to Mastering SQL
Book Synopsis
£13.17
Princeton University Press Dark Data
Book SynopsisTrade Review"[A] penetrating study of missing (‘dark’) data and its impacts on decisions—skewing stats, enabling fraud, embedding inequity and triggering preventable catastrophes. Advocating ‘data science judo,’ Hand offers expert training, from recognizing when facts are being cherry-picked to designing randomized trials. A book illuminating shadowed corners in science, medicine and policy."---Barbara Kiser, Nature"A tour de force. . . . Hand is a good and able guide to take us through the many aspects of dark data that are potentially skewing our understanding of real world observations and potential scientific breakthroughs. He writes in an accessible and understandable way too."---Simon Cocking, Irish Tech News"Well-written and accessible."---Tim Harford, Undercover Economist"You need to read [Dark Data], and be convinced by David’s reasoning and his examples of cases in which unseen or unreported data play a critical and sometimes even a fatal role. You are likely to walk away with the feeling that the term dark data is indeed a very effective one to arouse both curiosity and suspicion, mixed with happiness that finally a great term was coined by a statistician—and sadness that the statistician is not you."---Xiao-Li Meng, IMS Bulletin"An exploration of a major problem in data analysis with an attempt of classification, analysing causes, mechanisms, and to some extent also suggest mitigations."---Adhemar Bultheel, European Mathematical Society"An excellent guide to the many reasons for caution in interpreting data."---Diane Coyle, Enlightened Economist
£15.29
O'Reilly Media Streaming Databases
Book Synopsis
£47.99
John Wiley & Sons Inc Data Smart
Book SynopsisTable of ContentsIntroduction xix 1 Everything You Ever Needed to Know About Spreadsheets but Were Too Afraid to Ask 1 Some Sample Data 2 Accessing Quick Descriptive Statistics 3 Excel Tables 4 Filtering and Sorting 5 Table Formatting 7 Structured References 7 Adding Table Columns 10 Lookup Formulas 11 VLOOKUP 11 INDEX/MATCH 13 XLOOKUP 15 PivotTables 16 Using Array Formulas 19 Solving Stuff with Solver 20 2 Set It and Forget It: An Introduction to Power Query 27 What Is Power Query? 27 Sample Data 28 Starting Power Query 29 Filtering Rows 32 Removing Columns 33 Find & Replace 34 Close & Load to Table 35 3 Naïve Bayes and the Incredible Lightness of Being an Idiot 39 The World's Fastest Intro to Probability Theory 39 Totaling Conditional Probabilities 40 Joint Probability, the Chain Rule, and Independence 40 What Happens in a Dependent Situation? 41 Bayes Rule 42 Separating the Signal and the Noise 43 Using the Bayes Rule to Create an AI Model 44 High-Level Class Probabilities Are Often Assumed to Be Equal 45 A Couple More Odds and Ends 46 Let's Get This Excel Party Started 47 Cleaning the Data with Power Query 48 Splitting on Spaces: Giving Each Word Its Due 50 Counting Tokens and Calculating Probabilities 55 We Have a Model! Let's Use It 58 4 Cluster Analysis Part 1: Using K-Means to Segment Your Customer Base 65 Dances at Summer Camp 65 Getting Real: K-Means Clustering Subscribers in Email Marketing 70 The Initial Dataset 71 Determining What to Measure 72 Start with Four Clusters 75 Euclidean Distance: Measuring Distances as the Crow Flies 76 Solving for the Cluster Centers 80 Making Sense of the Results 82 Getting the Top Deals by Cluster 83 The Silhouette: A Good Way to Let Different K Values Duke It Out 86 How About Five Clusters? 95 Solving for Five Clusters 96 Getting the Top Deals for All Five Clusters 96 Computing the Silhouette for 5-Means Clustering 99 K-Medians Clustering and Asymmetric Distance Measurements 100 Using K-Medians Clustering 100 Getting a More Appropriate Distance Metric 100 Putting It All in Excel 102 The Top Deals for the 5-Medians Clusters 104 5 Cluster Analysis Part II: Network Graphs and Community Detection 109 What Is a Network Graph? 110 Visualizing a Simple Graph 110 Beyond GiGraph and Adjacency Lists 115 Building a Graph from the Wholesale Wine Data 117 Creating a Cosine Similarity Matrix 118 Producing an R-Neighborhood Graph 121 Introduction to Gephi 123 Creating a Static Adjacency Matrix 124 Bringing in Your R-Neighborhood Adjacency Matrix into Gephi 124 Node Degree 128 Touching the Graph Data 130 How Much Is an Edge Worth? Points and Penalties in Graph Modularity 132 What's a Point, and What's a Penalty? 133 Setting Up the Score Sheet 136 Let's Get Clustering! 138 Split Number 1 138 Split 2: Electric Boogaloo 143 And. . .Split3: Split with a Vengeance 145 Encoding and Analyzing the Communities 146 There and Back Again: A Gephi Tale 151 6 Regression: The Granddaddy of Supervised Artificial Intelligence 157 Predicting Pregnant Customers at RetailMart Using Linear Regression 158 The Feature Set 159 Assembling the Training Data 161 Creating Dummy Variables 163 Let's Bake Our Own Linear Regression 165 Linear Regression Statistics: R-Squared, F-Tests, t-Tests 173 Making Predictions on Some New Data and Measuring Performance 182 Predicting Pregnant Customers at RetailMart Using Logistic Regression 192 First You Need a Link Function 192 Hooking Up the Logistic Function and Reoptimizing 193 Baking an Actual Logistic Regression 196 7 Ensemble Models: A Whole Lot of Bad Pizza 203 Getting Started Using the Data from Chapter 6 203 Bagging: Randomize, Train, Repeat 204 Decision Stump is Another Name for a Weak Learner 204 Doesn't Seem So Weak to Me! 204 You Need More Power! 207 Let's Train It 208 Evaluating the Bagged Model 220 Boosting: If You Get It Wrong, Just Boost and Try Again 223 Training the Model—Every Feature Gets a Shot 224 Evaluating the Boosted Model 231 8 Forecasting: Breathe Easy: You Can't Win 235 The Sword Trade Is Hopping 236 Getting Acquainted with Time-Series Data 236 Starting Slow with Simple Exponential Smoothing 238 Setting Up the Simple Exponential Smoothing Forecast 240 You Might Have a Trend 249 Holt's Trend-Corrected Exponential Smoothing 250 Setting Up Holt's Trend-Corrected Smoothing in a Spreadsheet 252 So Are You Done? Looking at Autocorrelations 258 Multiplicative Holt-Winters Exponential Smoothing 266 Setting the Initial Values for Level, Trend, and Seasonality 268 Getting Rolling on the Forecast 274 And. . .Optimize! 280 Putting a Prediction Interval Around the Forecast 283 Creating a Fan Chart for Effect 287 Forecast Sheets in Excel 289 9 Optimization Modeling: Because That "Fresh-Squeezed" Orange Juice Ain't Gonna Blend Itself 293 Wait Is This Data Science? 294 Starting with a Simple Trade-Off 295 Representing the Problem as a Polytope 296 Solving by Sliding the Level Set 297 The Simplex Method: Rooting Around the Corners 298 Working in Excel 300 Fresh from the Grove to Your Glass with a Pit Stop Through a Blending Model 305 Let's Start with Some Specs 307 Coming Back to Consistency 308 Putting the Data into Excel 309 Setting Up the Problem in Solver 311 Lowering Your Standards 314 Dead Squirrel Removal: the Minimax Formulation 317 If-Then and the "Big M" Constraint 320 Multiplying Variables: Cranking Up the Volume to 11,000 324 Modeling Risk 330 Normally Distributed Data 331 10 Outlier Detection: Just Because They're Odd Doesn't Mean They're Unimportant 339 Outliers Are (Bad?) People, Too 340 The Fascinating Case of Hadlum v Hadlum 340 Tukey's Fences 341 Applying Tukey's Fences in a Spreadsheet 342 The Limitations of This Simple Approach 345 Terrible at Nothing, Bad at Everything 346 Preparing Data for Graphing 347 Creating a Graph 350 Getting the k-Nearest Neighbors 351 Graph Outlier Detection Method 1: Just Use the Indegree 352 Graph Outlier Detection Method 2: Getting Nuanced with k-Distance 355 Graph Outlier Detection Method 3: Local Outlier Factors Are Where It's At 358 11 Moving on From Spreadsheets 363 Getting Up and Running with R 364 A Crash Course in R-ing 366 Show Me the Numbers! Vector Math and Factoring 367 The Best Data Type of Them All: the Dataframe 370 How to Ask for Help in R 371 It Gets Even Better Beyond Base R 372 Doing Some Actual Data Science 374 Reading Data into R 374 Spherical K-Means on Wine Data in Just a Few Lines 375 Building AI Models on the Pregnancy Data 381 Forecasting in R 389 Looking at Outlier Detection 393 12 Conclusion 397 Where Am I? What Just Happened? 397 Before You Go-Go 397 Get to Know the Problem 398 We Need More Translators 398 Beware the Three-Headed Geek-Monster: Tools, Performance, and Mathematical Perfection 399 You Are Not the Most Important Function of Your Organization 401 Get Creative and Keep in Touch! 402 Index 403
£30.39
Kogan Page Ltd The Enterprise Big Data Framework
Book SynopsisJan-Willem Middelburg is a Dutch entrepreneur and author with a passion for technology and innovation. He is the CEO and co-founder of Cybiant, a global technology that company that helps to create a more sustainable world through analytics, big data and automation. He is also President and Chief Examiner of the Enterprise Big Data Framework, an independent organization dedicated to upskilling individuals with expertise in Big Data. In partnership with APMG-International, the Enterprise Big Data Framework offers vendor-neutral certifications for individuals.Trade Review"The Enterprise Big Data Framework is relevant for everybody within an organisation engaged in driving maximum benefits from data. There is something for everybody; from the board considering governance and ethical behaviour to individuals within the organisation knowing where they fit and the value they can get from better use of their organisation's data. If you are considering a transformation project, this is an excellent guide for your project team." * Richard Pharro, CEO, The APM Group Limited *"If you are looking for a good guide to empower your knowledge on big data and to find a framework to help you on your big data journey, then this book is for you. From learning what big data is to defining a big data strategy, Jan-Willem has built a book to empower the learner on the topic of big data." * Jordan Morrow, Chief Strategy & Transformation Officer, DataPrime and Author of Be Data Literate *"This book is a master piece for those who are familiar and those who discover the world of data. It provides an "a la carte framework" starting with a (big) data strategy and the supporting aspects such as big data functions, architecture and algorithms. It covers in depth data platforms architectures, its management as well as data governance, data catalogue and all the required security considerations associated to the various data classifications. You will find details of data life cycle management, of various machine learning algorithms and an important chapter covering AI ethics when building and deploying sophisticated algorithms using data. The concepts covered in this book apply to on-premises and in the (public) cloud environments, making this book a must read." * Jean-Michel Coeur, APAC Technology Practice Lead, Data Analytics, Google Cloud *Table of Contents Section - ONE: Introduction to Big Data; Chapter - 01: Introduction to Big Data; Chapter - 02: The Big Data framework; Chapter - 03: Big Data strategy; Chapter - 04: Big Data architecture; Chapter - 05: Big Data algorithms; Chapter - 06: Big Data processes; Chapter - 07: Big Data functions; Chapter - 08: Artificial intelligence; Section - TWO: Enterprise Big Data analysis; Chapter - 09: Introduction to Big Data analysis; Chapter - 10: Defining the business objective; Chapter - 11: Data ingestion – importing and reading data sets; Chapter - 12: Data preparation – cleaning and wrangling data; Chapter - 13: Data analysis – model building; Chapter - 14: Data presentation; Section - THREE: Enterprise Big Data engineering; Chapter - 15: Introduction to Big Data engineering; Chapter - 16: Data modelling; Chapter - 17: Constructing the data lake; Chapter - 18: Building an enterprise Big Data warehouse; Chapter - 19: Design and structure of Big Data pipelines; Chapter - 20: Managing data pipelines; Chapter - 21: Cluster technology; Section - FOUR: enterprise Big Data algorithm design; Chapter - 22: Introduction to Big Data algorithm design; Chapter - 23: Algorithm design – fundamental concepts; Chapter - 24: Statistical machine learning algorithms; Chapter - 25: The data science roadmap; Chapter - 26: Programming languages 26 visualization and simple metrics; Chapter - 27: Advanced machine learning algorithms; Chapter - 28: Advanced machine learning classification algorithms; Chapter - 29: Technical communication and documentation; Section - FIVE: Enterprise Big Data architecture; Chapter - 30: Introduction to the Big Data architecture; Chapter - 31: Strength and resilience – the Big Data platform; Chapter - 32: Design principles for Big Data architecture; Chapter - 33: Big Data infrastructure; Chapter - 34: Big Data platforms; Chapter - 35: The Big Data application provider; Chapter - 36: System orchestration in Big Data
£148.50
MC Press, LLC The Chief Data Officer Handbook for Data
Book SynopsisA practical guide for today’s chief data officers to define and manage data governance programs The relatively new role of chief data officer (CDO) has been created to address the issue of managing a company’s data as a strategic asset, but the problem is that there is no universally accepted “playbook” for this role. Magnifying the challenge is the rapidly increasing volume and complexity of data, as well as regulatory compliance as it relates to data. In this book, Sunil Soares provides a practical guide for today’s chief data officers to manage data as an asset while delivering the trusted data required to power business initiatives, from the tactical to the transformative. The guide describes the relationship between the CDO and the data governance team, whose task is the formulation of policy to optimize, secure, and leverage information as an enterprise asset by aligning the objectives of multiple functions. Soares provides unique insight into the role of the CDO and presents a blueprint for implementing data governance successfully within the context of the position. With practical advice CDOs need, this book helps establish new data governance practices or mature existing practices.
£13.46
Whittles Publishing 3D Recording, Documentation and Management of
Book SynopsisDocumentation of our cultural heritage is experiencing an explosion of innovation. New tools have appeared in recent decades including laser scanning, rapid prototyping, high dynamic range spherical and infrared imagery, drone photography, augmented and virtual reality and computer rendering in multiple dimensions. These give us visualisations and data that are at once interesting, intriguing and yet sometimes deceptive. This text provides an objective and integrated approach to the subject, bringing together the techniques of conservation with management, photographic methods, various modelling techniques and the use of unmanned aerial systems. This interdisciplinary approach addresses the need for knowledge about deploying advanced digital technologies and the materials and methods for the assessment, conservation, rehabilitation and maintenance of the sustainability of existing structures and designated historic buildings. Furthermore, this book actively provides the knowhow to facilitate the creation of heritage inventories, assessing risk, and addressing the need for sustainability.In so doing it becomes more feasible to mitigate the threats from inherent and external causes, not only for the built heritage but also for moveable objects and intangible heritage that suffer abandonment and negligence as well as looting and illegal trafficking. The book is written by a team of international experts based upon their practical experience and expertise. It therefore creates a unique book that encapsulates the knowledge of this discipline required by anyone working in this field.Trade Review`...this new publication is a welcome addition, highlighting how these 3D techniques can be utilised... ...this well-illustrated volume represents a useful contribution for scholars wishing to gain a better understanding of the underpinnings of 3D recording and documentation’. Medieval Archaeology -------------------- `...I found this book very valuable. It can reach an eclectic audience in providing a broad spectrum of the subject. This book is of major importance for Cultural Heritage 3D recording and management and...an important resource handbook’. International Institute for Conservation of Historic and Artistic Works -------------------- '...this new, richly illustrated reference publication on recording and documenting cultural heritage. ... For anyone considering a digital camera for survey purposes ... this chapter [4] is essential reading, and is rightfully one of the best references currently available on the science behind imaging. ...manages to provide what is probably the most up-to-date reference book on 3D recording, documentation and management of cultural heritage. For any heritage professional, academic, student or interested individual considering applying, acquiring, undertaking or researching digital imaging, photogrammetry, Structure-from-Motion, laser scanning, GIS, BIM or RPAS/UAV within a conservation context, this book should be essential reading before embarking down any one of these rapidly developing technological routes'. Conservation and Management of Archaeological Sites -------------------- '...the images in this book, both in colour and high-resolution, play a critical role along with the text. This is a well produced book that is wonderful to read and view. ...I find this book exceptional for its publishing quality, content and production. It clearly includes cutting-edge knowledge, awareness and experience from many contributors involved in cultural heritage processes around the globe...would be very useful to anyone involved in cultural heritage, documentation of history and site preservation and conservation. It can readily serve as a course text in addition to being a reference text. ... I've nothing but positive things to say about this book - I think you will too'. 3D Visualization WorldTable of ContentsIntroduction - current trends in cultural heritage and documentation; Conservation techniques in cultural heritage; Cultural heritage management tools: The role of GIS and BIM; Basics of photography for cultural heritage imaging; Basics of image-based modelling techniques in cultural heritage 3D recording; Basics of range-based modelling techniques in cultural heritage 3D recording; Cultural heritage documentation with RPAS/UAV
£80.75
Elsevier Science Database Modeling and Design
Book SynopsisHow do you model and design your database application in consideration of new technology or new business needs? This title is loaded with design rules and case studies that are applicable to any SQL, UML, or XML-based system. It is useful to those tasked with the creation of data models for the integration of large-scale enterprise data.Trade Review"Database Modeling and Design is one of the best books that I have seen for explaining how to build database applications. The book is informative, well-written, and concise." --Michael Blaha, DSc., Consultant, Modelsoft Consulting Corp"This book book is by far the best book available on classic database design. Topics like normalization and many-to-many and n-ary association semantics are without peer in teaching you how to model real-world complexities. This latest edition extends the classic material with extensive discussion of modern tools and other aspects of logical database design. Every database architect should have this book at hand." --Bob Muller, Data Analyst, Poesys Associates“The book is not only good for beginners, but it also provides greater insight for experienced learners. Perhaps this is why it has evolved into its fifth edition. The book is generally well organized. It starts with the first step in the database life cycle, and progresses in a chronological order to more advanced concepts such as object relational design, Extensible Markup Language (XML), and Web databases. The writing style of the book is simple and straightforward, and the use of database terminology is very concise…In my opinion, the book could be used as a course text, with some help from other sources to cover SQL query-related concepts. However, I would have liked a chapter on SQL that covered simple and complex query design, as well as optimization." --Computing ReviewsTable of Contents1. Introduction2. The Entity-Relationship Model3. Unified Modeling Language (UML)4. Requirements Analysis and Conceptual Modeling5. Transforming the Conceptual Data Model to SQL6. Normalization7. An Example of Logical Database Design8. Object Relational Design9. XML and Web Databases10. Business Intelligence11. CASE ToolsAppendix: The Basics of SQL
£57.12
Springer-Verlag New York Inc. The Herschel Objects and How to Observe Them
Book SynopsisAmateur astronomers are always on the lookout for new observing challenges. This is a practical guide to locating and viewing the most impressive of Herschel’s star clusters, nebulae and galaxies, cataloging more than 600 of the brightest objects, and offering detailed descriptions and images of 150 to 200 of the best.Trade ReviewFrom the reviews: "Mullaney packs an incredible amount of information into this 166-page book. … All in all, The Herschel Objects, and how to observe them is engaging, challenging, well-written, and comprehensive. So, if you love deep-sky observing – and even if you’ve observed the Astronomical League’s Herschel 400 – Mullaney’s book offers a new list with several hundred additional objects you’ll enjoy." (Michael Bakich, Astronomy Magazine, October, 2007) "The Herschel Objects and How to Observe Them is a fine addition to the Springer series of observing guides. Mullaney has been observing the Herschel objects for many years and his passion for them clearly comes across. … Overall though, this is a book that will be a useful addition to any deep-sky observer’s library." (Paul Money, BBC Sky at Night, February, 2008) "Mullaney begins with a well-written biographical sketch of Herschel and his family, and explains the significance of the work of this great observational astronomer. … the objects are illustrated with excellent images obtained using a modern charge-coupled device (CCD) system. The book concludes with a list of 618 targets that would provide for a lifetime of study. The book will be of greatest interest to experienced observers who wish to push on to the most challenging deep sky objects. … Summing Up: Recommended. General readers." (D. E. Hogg, CHOICE, Vol. 45 (6), February, 2008) "The book opens with a few short chapters on Herschel himself together with a brief introduction to observing techniques … . rounded out with some objects that the author regards as showpieces that were not discovered by Herschel. Any collection of these will of course be very subjective. … I found the book’s reproductions to be a cut above the usual Springer ones and the book does offers something sufficiently different … and the Astronomical League guides to make it worth adding to your collection." (Owen Brazell, The Observatory, Vol. 128 (1203), 2008)Table of ContentsWilliam Herschel's Life, Telescopes and Catalogs.- Herschel's Telescopes.- Herschel's Catalogs and Classes.- Observing Techniques.- Exploring The Herschel Showpieces.- Showpieces of Class I.- Showpieces of Class IV.- Showpieces of Class V.- Showpieces of Class VI.- Showpieces of Class VII.- Showpieces of Class VIII.- Samples of Classes II & III.- Showpieces Missed by Herschel.- The “Missing” Herschel Objects.- Conclusion.
£23.74
Springer Adaptive Hypertext and Hypermedia
a huge range and FREE tracked UK delivery on ALL orders.
£85.49
Springer Syntactic Wordclass Tagging 9 Text Speech and Language Technology
a huge range and FREE tracked UK delivery on ALL orders.
£123.49
Springer Advances in Intelligent Systems
a huge range and FREE tracked UK delivery on ALL orders.
£237.49
Springer Unconventional Optical Elements for Information Storage Processing and Communications Proceedings of the NATO Advanced Research Workshop Tel Aviv Tel Aviv Israel October 1921 1999 75
a huge range and FREE tracked UK delivery on ALL orders.
£123.49
Springer Unconventional Optical Elements for Information Storage Processing and Communications Proceedings of the NATO Advanced Research Workshop Tel Aviv 75 Nato Science Partnership Subseries 3
a huge range and FREE tracked UK delivery on ALL orders.
£123.49
Springer Mathematical Foundations of Information Retrieval
a huge range and FREE tracked UK delivery on ALL orders.
£44.99
Springer Information Storage and Retrieval Systems
a huge range and FREE tracked UK delivery on ALL orders.
£265.99
LEGARE STREET PR Using Temporal Logic and Datalog to Query Databases Evolving in Time
a huge range and FREE tracked UK delivery on ALL orders.
£21.80
Legare Street Press Data Quality Requirements Analysis and Modeling
a huge range and FREE tracked UK delivery on ALL orders.
£21.80
O'Reilly Media How To Make Things Faster
Book SynopsisThis book explains in a clear and thoughtful voice why systems perform the way they do. It's for anybody who's curious about how computer programs and other processes use their time and about what you can do to improve them.
£33.74
£13.92
Springer Us Managing and Mining Graph Data 40 Advances in Database Systems
Book SynopsisManaging and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy.Trade ReviewFrom the reviews:“This book provides a survey of some recent advances in graph mining. It contains chapters on graph languages, indexing, clustering, pattern mining, keyword search, and pattern matching. … The book is targeted at advanced undergraduate or graduate students, faculty members, and researchers from both industry and academia. … I highly recommend this book to someone who is starting to explore the field of graph mining or wants to delve deeper into this exciting field.” (Dimitrios Katsaros, ACM Computing Reviews, December, 2010)Table of ContentsAn Introduction to Graph Data.- Graph Data Management and Mining: A Survey of Algorithms and Applications.- Graph Mining: Laws and Generators.- Query Language and Access Methods for Graph Databases.- Graph Indexing.- Graph Reachability Queries: A Survey.- Exact and Inexact Graph Matching: Methodology and Applications.- A Survey of Algorithms for Keyword Search on Graph Data.- A Survey of Clustering Algorithms for Graph Data.- A Survey of Algorithms for Dense Subgraph Discovery.- Graph Classification.- Mining Graph Patterns.- A Survey on Streaming Algorithms for Massive Graphs.- A Survey of Privacy-Preservation of Graphs and Social Networks.- A Survey of Graph Mining for Web Applications.- Graph Mining Applications to Social Network Analysis.- Software-Bug Localization with Graph Mining.- A Survey of Graph Mining Techniques for Biological Datasets.- Trends in Chemical Graph Data Mining.
£189.99
O'Reilly Media CostEffective Data Pipelines
Book SynopsisWith this practical guide, author Sev Leonard provides a holistic approach to designing scalable data pipelines in the cloud. Intermediate data engineers, software developers, and architects will learn how to navigate cost/performance trade-offs and how to choose and configure compute and storage.
£39.74
Springer New York Cultural Synergy in Information Institutions
Book SynopsisCultural forces govern a synergistic relationship among information institutions that shapes their roles collectively and individually. The book addresses the origins of cultural information institutions, the history of the professions that run them, and the social imperative of information organization as a catalyst for semantic synergy.Table of ContentsChapter 1. Cultural Synergy and the Role of Information Institutions.- Chapter 2. The Nature of Information.- Chapter 3. What is (are) Information Studies?.- Chapter 4. The Synergistic Information Professions: Applications of the Information Process.- Chapter 5. Some History of Libraries, Library and Information Science, Information Technology.- Chapter 6. Gatekeepers: Information Dissemination.- Chapter 7. Knowledge Organization: Bibliography as Synergic Catalyst.- Chapter 8. Into the Future Boldly: The Imperative for Cultural Synergy.
£44.99
Technics Publications LLC The Elephant in the Fridge: Guided Steps to Data Vault Success through Building Business-Centered Models
Book SynopsisYou want the rigour of good data architecture at the speed of agile? Then this is the missing link - your step-by-step guide to Data Vault success. Success with a Data Vault starts with the business and ends with the business. Sure, theres some technical stuff in the middle, and it is absolutely essential -- but its not sufficient on its own. This book will help you shape the business perspective, and weave it into the more technical aspects of Data Vault modeling. You can read the foundational books and go on courses, but one massive risk still remains. Dan Linstedt, the founder of the Data Vault, very clearly directs those building a Data Vault to base its design on an enterprise ontology. And Hans Hultgren similarly stresses the importance of the business concepts model. So its important. We get that. But: What on earth is an enterprise ontology/business concept model, cause I wont know if Ive got one if I dont know what Im looking for? If I cant find one, how do I get my hands on such a thing? Even if I have one of these wonderful things, how do I apply it to get the sort of Data Vault thats recommended? Its actually not as hard as some would fear to answer all of these questions, and its certainly worth the effort. This book just might save you a world of pain. Its a supplement to other material on Data Vault modeling, but its the vital missing link to finding simplicity for Data Vault success.
£38.24
£25.49
