The Experts below are selected from a list of 9111 Experts worldwide ranked by ideXlab platform
Daniel Linstedt - One of the best experts on this subject based on the ideXlab platform.
-
Advanced Data Vault Modeling
Data Vault 2.0, 2016Co-Authors: Daniel Linstedt, Michael OlschimkeAbstract:This chapter addresses two additional aspects of Data Vault modeling: query assistant tables and reference tables. Query assistant tables are used to reduce the complexity of queries against the Data Vault and improve the performance. Two specific types of query assistant tables are covered: point-in-time (PIT) tables and bridge tables. Reference tables are covered in detail, including no-history reference tables, history-based reference tables and code and description tables.
-
The Data Vault 2.0 Methodology
Data Vault 2.0, 2016Co-Authors: Daniel Linstedt, Michael OlschimkeAbstract:The Data Vault 2.0 Methodology introduces unique concepts to the development of Data warehouses and is based on several agile Data warehouse methodologies and techniques, including CMMI, Six Sigma, TQM, SDLC, and Function Point Analysis. This chapter introduces the basics of these standards and explains how the Data Vault 2.0 methodology brings them together. This chapter focuses on the project practices of the Data Vault 2.0 methodology.
-
loading the Data Vault
Data Vault 2.0#R##N#Implementation Guide for Microsoft SQL Server 2014, 2016Co-Authors: Daniel Linstedt, Michael OlschimkeAbstract:The Data Vault layer is loaded from the Data in the staging area. This chapter describes loading techniques using SSIS and T-SQL for loading hubs, links, and satellites, as well as advanced tables. It is fairly simple to transfer these concepts from Microsoft SQL Server to other BI toolkits. The chapter also discusses how to (soft) delete Data from hubs and links and how to deal with missing Data. It also covers how to load reference tables and ends with a discussion about truncating the staging area, which completes the loading process. The authors provide some modifications of the standard templates that are helpful when dealing with large amounts of Data.
-
Building a Scalable Data Warehouse with Data Vault 2.0
2015Co-Authors: Daniel Linstedt, Michael OlschimkeAbstract:The Data Vault was invented by Dan Linstedt at the U.S. Department of Defense, and the standard has been successfully applied to Data warehousing projects at organizations of different sizes, from small to large-size corporations. Due to its simplified design, which is adapted from nature, the Data Vault 2.0 standard helps prevent typical Data warehousing failures. "Building a Scalable Data Warehouse" covers everything one needs to know to create a scalable Data warehouse end to end, including a presentation of the Data Vault modeling technique, which provides the foundations to create a technical Data warehouse layer. The book discusses how to build the Data warehouse incrementally using the agile Data Vault 2.0 methodology. In addition, readers will learn how to create the input layer (the stage layer) and the presentation layer (Data mart) of the Data Vault 2.0 architecture including implementation best practices. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Dan Linstedt and Michael Olschimke discuss: How to load each layer using SQL Server Integration Services (SSIS), including automation of the Data Vault loading processes. Important Data warehouse technologies and practices. Data Quality Services (DQS) and Master Data Services (MDS) in the context of the Data Vault architecture. Provides a complete introduction to Data warehousing, applications, and the business context so readers can get-up and running fast Explains theoretical concepts and provides hands-on instruction on how to build and implement a Data warehouseDemystifies Data Vault modeling with beginning, intermediate, and advanced techniquesDiscusses the advantages of the Data Vault approach over other techniques, also including the latest updates to Data Vault 2.0 and multiple improvements to Data Vault 1.0
-
Introduction to Data Vault Modeling
Data Architecture: a Primer for the Data Scientist, 2015Co-Authors: William H. Inmon, Daniel LinstedtAbstract:This section covers the Data Vault 2.0 Model in brief. From a conceptual level, the Data Vault model is a hub and spoke based model, designed to focus its integration patterns around business keys. The concepts are derived from business context (or business ontologies), which are elements that make sense to the business from a master Data perspective, such as customer, product, service, and so on. A Data Vault model is a detail-oriented, historical tracking, and uniquely linked set of normalized tables that support one or more functional areas of business. In Data Vault 2.0, the model entities are keyed by hashes, where in Data Vault 1.0 the model entities are keyed by sequences.
Vladan Jovanovic - One of the best experts on this subject based on the ideXlab platform.
-
nosql document store translation to Data Vault based edw
International Convention on Information and Communication Technology Electronics and Microelectronics, 2018Co-Authors: Katerina Cernjeka, Danijela Jaksic, Vladan JovanovicAbstract:With evolution of technology and Web 2.0 tools NoSQL stores appeared as a common solution to Data storage and management demands in modern Databases and applications. Relational Databases were not designed to cope with agility challenges, scale, commodity storage and processing power demanded by modern applications. Many industries today are choosing NoSQL Database technology over relational Databases (or at least combining them) in order to gain needed flexibility and scalability. Due to the limitations of relational Databases, most researchers are oriented toward relational Database transformations and Data migration to different types of NoSQL stores. Our research goes in the opposite direction — we aim to develop a metamodel for translating a NoSQL document store (MongoDB) into a Data Vault based enterprise Data warehouse. The reason we do so is to integrate different Data sources into a Data Vault central repository and develop a new Data warehouse system catalog that will track changes in both relational and NoSQL schemas. The integration of relational Database and NoSQL store would help extract wider knowledge through BI tools, enable Data traceability, trend discovery and will accommodate auditing process. The main contributions of this paper are translation rules that accommodate the translation between NoSQL MongoDB document store and Data Vault based enterprise Data warehouse.
-
A direct approach to physical Data Vault design
Computer Science and Information Systems, 2016Co-Authors: Dragoljub Krneta, Vladan Jovanovic, Zoran MarjanovicAbstract:The paper presents a novel agile approach to large scale design of enterprise Data warehouses based on a Data Vault model. An original, simple and direct algorithm is defined for the incremental design of physical Data Vault type enterprise Data warehouses, using source Data meta-model and rules, and used in developing a prototype case tool for Data Vault design. This approach solves primary requirements for a system of record, that is, preservation of all source information, and fully addresses flexibility and scalability expectations. Our approach benefits from Data Vault dependencies minimizations and rapid loads opportunities enabling greatly simplified ETL transformations in a way not possible with traditional (i.e. non Data Vault based) Data warehouse designs. The approach is illustrated using a realistic example from the healthcare domain.
-
Data warehouse and master Data management evolution a meta Data Vault approach
IACIS 2014 International Conference, 2014Co-Authors: Danijela Subotic, Vladan Jovanovic, Patrizia PoščićAbstract:The paper presents a: a) brief overview and analysis of existing approaches to the Data warehouse (DW) evolution problem, and b) detailed description of the research idea for the DW evolution problem (primarily intended for structured Data sources and realizations with relational Database engines). We observe the DW evolution problem as a double issue - from the DW perspective, and from the master Data management (MDM) perspective. The proposed general solution will include a Data Vault (DV) model based metaData repository that will integrate the DW Data and metaData with the MDM Data and metaData. This historicized metaData repository will manage schema versions and support schema changes. We believe this integration will: a) increase the quality of Data in the Enterprise Data Warehouse (EDW), b) increase the quality of the metaData in the EDW, and c) increase the simplicity and efficiency of the DW and the MDM schema evolution.
-
Extensible Markup Language (XML) Schemas for Data Vault Models
Journal of Computer Information Systems, 2013Co-Authors: Curtis Knowles, Vladan JovanovicAbstract:With the conceptualization of next generation architecture for Data Warehousing, that is the DW 2.0, there is now an increased emphasis on using fully-temporalized Databases, in particular with approaches such as the Data Vault. In this paper we present a template XML Schema for Data Vault model concepts (at a metamodel level) and a process for creating XML Schemas for Data warehouse designs represented as Data Vault models. These templates can be used to describe and present Data from disparate systems in a structured format suitable for exchange and loading into a Data warehouse.
S Staines - One of the best experts on this subject based on the ideXlab platform.
-
Data Vault using Data science
Social Science Research Network, 2021Co-Authors: S Suprakash, Dhilip Kumar M, R M Shabarish, S StainesAbstract:Application security and Data security is a major concern in the current application development environments. This work is mainly about deploying website that secures Data on the application using the best state of the art cryptographic algorithms. The basic cryptographic algorithms used in this work are two, one is the AES algorithm and the other algorithm is DES algorithm. A combination of these two algorithms are used here for best security outcomes. In short, the work came out with an idea of double encryption by first encrypting the plain text through AES algorithm and again encrypting the cipher text using DES algorithm now this same process is done in reverse while decrypting this cipher text. In future a study will be done on RSA algorithm according to the need for the security. The kind of Data the web application stores are certificates and documents of public which they might need in various occasions and to access them from anywhere they want to. Another kind of Data are the peoples i.e. civilians their documents, bonds, certificates which might be needed in government activities, mainly focusing on people in remote places. Besides protecting the Data, this website also gives the accessibility to the Data from anywhere as a virtual copy, which can be accessed by the individuals through their individual login ids that would be provided by the website. In work also made a capacity reasoning and protecting the endorsements and properties with the assistance of the chained calculation. Any place the information is needed, the same can undoubtedly returned from the website and cab be download. In future, the work will be studied with dynamic RSA key calculation dependent on the individuals' requirements and as a result through the work it will be providing a much safer place for the people to store their documents and gives accessibility to the users from where ever and whenever they want to.
Michael Olschimke - One of the best experts on this subject based on the ideXlab platform.
-
Advanced Data Vault Modeling
Data Vault 2.0, 2016Co-Authors: Daniel Linstedt, Michael OlschimkeAbstract:This chapter addresses two additional aspects of Data Vault modeling: query assistant tables and reference tables. Query assistant tables are used to reduce the complexity of queries against the Data Vault and improve the performance. Two specific types of query assistant tables are covered: point-in-time (PIT) tables and bridge tables. Reference tables are covered in detail, including no-history reference tables, history-based reference tables and code and description tables.
-
intermediate Data Vault modeling
Data Vault 2.0#R##N#Implementation Guide for Microsoft SQL Server 2014, 2016Co-Authors: Daniel Linsted, Michael OlschimkeAbstract:Due to the complexity of Data warehouses and the underlying business requirements, more complex Data Vault entities are typically required and introduced throughout this chapter. They extend the basic entities discussed in the previous chapter. The various special types of satellites are covered, including overloaded satellites, multi-active satellites, status-tracking satellites, effectivity satellites, record tracking satellites, and computed satellites. Extended link entities are covered as well, including link-to-link relationships, same-as links, hierarchical links, nonhistorized links, nondescriptive links, computed aggregate links, and exploration links. For each link entity, the technical or business reason for adding them to the Data Vault will be explained.
-
Data Vault 2 0 modeling
Data Vault 2.0#R##N#Implementation Guide for Microsoft SQL Server 2014, 2016Co-Authors: Daniel Linsted, Michael OlschimkeAbstract:This chapter introduces the entities used in Data Vault modeling, including hubs, links and satellites. It shows how to identify business keys in the source extracts and link them to other business keys in the Data Vault using link entities. The chapter also shows how to identify additional attributes in the source extracts and how to model them as satellite entities. The discussion on satellites includes the need to split up satellites based on different aspects, for example by classification or type of Data, by rate of change, or by source system. For each entity, common attributes of these entities that should be added when modeling the Data Vault are listed and explained in detail. This includes the recommended use of hash keys, time stamps, and record source identifiers.
-
The Data Vault 2.0 Methodology
Data Vault 2.0, 2016Co-Authors: Daniel Linstedt, Michael OlschimkeAbstract:The Data Vault 2.0 Methodology introduces unique concepts to the development of Data warehouses and is based on several agile Data warehouse methodologies and techniques, including CMMI, Six Sigma, TQM, SDLC, and Function Point Analysis. This chapter introduces the basics of these standards and explains how the Data Vault 2.0 methodology brings them together. This chapter focuses on the project practices of the Data Vault 2.0 methodology.
-
loading the Data Vault
Data Vault 2.0#R##N#Implementation Guide for Microsoft SQL Server 2014, 2016Co-Authors: Daniel Linstedt, Michael OlschimkeAbstract:The Data Vault layer is loaded from the Data in the staging area. This chapter describes loading techniques using SSIS and T-SQL for loading hubs, links, and satellites, as well as advanced tables. It is fairly simple to transfer these concepts from Microsoft SQL Server to other BI toolkits. The chapter also discusses how to (soft) delete Data from hubs and links and how to deal with missing Data. It also covers how to load reference tables and ends with a discussion about truncating the staging area, which completes the loading process. The authors provide some modifications of the standard templates that are helpful when dealing with large amounts of Data.
William H. Inmon - One of the best experts on this subject based on the ideXlab platform.
-
Introduction to Data Vault Modeling
Data Architecture: a Primer for the Data Scientist, 2015Co-Authors: William H. Inmon, Daniel LinstedtAbstract:This section covers the Data Vault 2.0 Model in brief. From a conceptual level, the Data Vault model is a hub and spoke based model, designed to focus its integration patterns around business keys. The concepts are derived from business context (or business ontologies), which are elements that make sense to the business from a master Data perspective, such as customer, product, service, and so on. A Data Vault model is a detail-oriented, historical tracking, and uniquely linked set of normalized tables that support one or more functional areas of business. In Data Vault 2.0, the model entities are keyed by hashes, where in Data Vault 1.0 the model entities are keyed by sequences.
-
Introduction to Data Vault Methodology
Data Architecture: a Primer for the Data Scientist, 2015Co-Authors: William H. Inmon, Daniel LinstedtAbstract:The Data Vault 2.0 standard provides a best practice for project execution that is called “Data Vault 2.0 Methodology.” It is derived from core software engineering standards and adapts them for use in Data warehousing. The methodology for Data Vault projects is based on best practices pulled from disciplined agile delivery (DAD), automation and optimization principles (Capability Maturity Model Integration [CMMI], key process areas [KPAs], and key-performance indicators [KPIs]), Six Sigma error tracking and reduction principles, lean enterprise initiatives, and cycle-time reduction principles. In addition, Data Vault methodology takes into account a notion known as “managed self-service business intelligence (BI).” The notion of managed self-service BI is introduced in the Section “Data Vault 2.0 Architecture” of this chapter.
-
Introduction to Data Vault
Data Architecture: a Primer for the Data Scientist, 2015Co-Authors: William H. Inmon, Daniel LinstedtAbstract:This chapter introduces Data Vault 2.0 as a system of business intelligence. Data Vault 2.0 includes architecture, modeling, methodology, and implementation best practices and standards that work with Big Data, relational, hybrid, and NoSQL systems. This chapter is helpful to those who are looking for new methods to constructing their enterprise business intelligence (BI) solutions or those struggling with the transition to Big Data, unstructured or multistructured Data. As Data variety and volume grows, businesses are constantly seeking a solution for BI and Data warehousing that can grow and adapt; the topics in this chapter cover how the team can be agile, what it means to include Six Sigma, total quality management (TQM), and Scrum agility for best results, all within a framework that serves the enterprise BI efforts.
-
Introduction to Data Vault Implementation
Data Architecture: a Primer for the Data Scientist, 2015Co-Authors: William H. Inmon, Daniel LinstedtAbstract:The Data Vault system of business intelligence (BI) provides implementation guidelines, rules, and recommendations as standards. As noted in previous sections of this chapter, well-defined standards and patterns are the key to success of agile, Capability Maturity Model Integration (CMMI), Six Sigma, and total quality management (TQM) principles. Some of the objectives of managing implementation through working practices include meeting the needs of TQM, embracing master Data, and assisting in alignment across business, source systems, and the enterprise Data warehouse. Patterns make life easier. In the enterprise BI world, patterns enable automation and generation, while reducing errors and error potential. Patterns are the heartbeat of the Data Vault 2.0 BI system.
-
Introduction to Data Vault Architecture
Data Architecture: a Primer for the Data Scientist, 2015Co-Authors: William H. Inmon, Daniel LinstedtAbstract:Data Vault 2.0 Architecture is based on three-tier Data warehouse architecture. The tiers are commonly identified as staging or landing zone, Data warehouse, and information delivery layer (or Data marts). The multiple tiers allow implementers and designers to decouple the enterprise Data warehouse from both sourcing and acquisition functions and information delivery and Data provisioning functions. In turn, the team becomes more nimble and the architecture is more resilient to failure and more flexible in responding to changes. NoSQL platform implementations will vary. Some will contain SQL-like interfaces; some will contain relational Database management system (RDBMS) technology integrated with nonrelational technology. The line between the two (RDBMS and NoSQL) will continue to be blurred. Eventually, it will be a “Data management system” capable of housing both relational and nonrelational Data simply by design.
