Data vault architecture

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Data Vault Basics Accelerated Business Intelligence

  • the data vault architecture offers a unique solution to business problems and technical problems alike
  • it is focused squarely at the data integration efforts across the enterprise and is built from solid foundational concepts
  • a key to understanding the data vault is understanding the business
  • once the business is mapped out and the practitioner has a firm grasp on how the business operates, then the process of …

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Snowflake Data Vault Reference Architecture

  • Data Vault Transformation Warehouse The transformation warehouse provides the compute resources for formatting and transforming the raw data in your staging tables as you move the data into your Data Vault HUB, LINK and SAT tables
  • You can execute these processes from either a standard ETL/ELT tool or a SQL script.

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What is Data Vault 2.0

  • The Data Vault 2.0 System Of Business Intelligence represents a major evolution of the already successful Data Vault architecture
  • It has been extended beyond the Data Warehouse component to include a model capable of dealing with cross-platform data persistence, multi-latency and multi-structured data and massively parallel platforms.

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Data Vault Modeling Methodology Architecture

  • The data vault modeling is a database modeling methodology designed to store long term historical data from various heterogeneous sources
  • The data vault model is relatively new approach in the data warehouse world.

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Data Quality in the Data Vault Architecture – Scalefree Blog

Data Quality in the Data Vault Architecture October 27, 2020 Scalefree Newsletter The Basis for solid decision-making In making business decisions whether daily or long term, the quality of data is a critical facet to factor into these decision-making processes.

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Vault Reference Architecture Vault

  • Vault cluster - This is the recommended architecture for a vault cluster as a single entity, but should also use replication as per the second diagram Vault replication - This is the recommended architecture for multiple vault clusters to allow for regional, performance and disaster recovery
  • Single Region Deployment (Enterprise)

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Data vault modeling

Data Vault Modeling is a database modeling method that is designed to provide long-term historical storage of data coming in from multiple operational systems.

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Deploying Data Vault on Azure SQL Data Warehouse

Azure Data Factory Hybrid data integration at enterprise scale, made easy HDInsight Provision cloud Hadoop, Spark, R Server, HBase, and Storm clusters Azure Stream Analytics Real-time analytics on fast moving streams of data from applications and devices

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Data Vault: What is it and when should it be used

  • Data Vault 2.0 methodology takes not only modeling technique, but provides an entire methodology for all Data Warehouse Projects
  • Indellient see’s the Data Vault modeling as a very viable approach to meet the needs of data warehousing projects, where both historical tracking and auditability are two important factors.
  • For many years, business intelligence (BI) projects have and continue to

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Data Vault: The Preferred “Flavor” for DW Architecture in

  • Data Vault follows a ‘ HYBRID DESIGN ’ methodology which follows ‘TOP-DOWN ARCHITECTURE WITH A BOTTOM-UP DESIGN’
  • The model is a mix of normalized modeling components with type 2 dimensional properties
  • In this model, the DW serves as a backend system that houses historical data which is integrated by the business keys.

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When should I use Data Vault

  • The diagram below shows a potential data architecture used by the Data Vault methodology
  • Data Warehouse - Data Vault Architecture
  • While at first glance, it looks very similar to the Enterprise Data Warehouse architecture above, it has a few significant differences and similarities, which include:

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What is “The Data Vault” and why do we need it

  • The Data Vault is a hybrid data modeling methodology providing historical data representation from multiple sources designed to be resilient to environmental changes
  • Originally conceived in 1990 and released in 2000 as a public domain modeling methodology, Dan Linstedt, its creator, describes a resulting Data Vault database as:

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How to Build a Modern Data Platform Utilizing Data Vault

  • In Data Vault architecture, a Satellite houses all the contextual details regarding an entity In my business, data changes very frequently
  • How can non-volatile contextual tables work for me? When there is a change in the data, a new row must be inserted with the changed data.

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Part 1: Data Vault for beginners – Simple SQL BI blogs

  • Data Vault is one of the database modelling method based on the third normal form
  • This model separates business keys, descriptive data of these business keys and the relationship between different business keys
  • Data Vault’s three main components are:

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Hybrid Architecture in Data Vault 2.0 – Scalefree Blog

LOGICAL DATA VAULT 2.0 ARCHITECTURE The Data Vault 2.0 architecture is based on three layers: the staging area which collects the raw data from the source systems, the enterprise data warehouse layer, modeled as a Data Vault 2.0 model, and the information delivery layer with information marts as star schemas and other structures.

Blog.scalefree.com   DA: 18 PA: 50 MOZ Rank: 83

Data vault modeling with ER/Studio data architect

  • Organizational data management and architectural needs have changed massively in recent decades
  • Today’s enterprise data solutions must be agile, flexible to rapid changes, scalable, reliable, and cheaper than ever
  • Dan Linstedt, creator of the Data Vault method, gives the following formal definition (Source

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Data Vault Modeling

  • Inmon, Mary Levins, in Data Architecture (Second Edition), 2019
  • The data vault modeling is a hybrid approach based on third normal form and dimensional modeling aimed at the logical enterprise data warehouse
  • The data vault model is built as a ground-up, incremental, and modular models that can be applied to big data, structured, and unstructured data sets.

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Why you really want a Persistent Staging Area in your Data

  • Following the Staging Layer architecture patterns: when records are loaded into the Staging Area they will be timestamped with the Load Date/Time Stamp (LOAD_DTS)
  • It is also the role of the Staging Area ETL to capture the Event Date/Time (explained in more detail in the post covering Data Vault ‘time’) as well as logical deletes.

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Data Warehouse vs Data Lake vs Data Vault

  • A data vault is a system made up of a model, methodology and architecture that is explicitly designed to solve a complete business problem as requirements change
  • Data Vault data is generally RAW data sets
  • So, in the case of the Data Vault, reconciling to the source system is a recommended for testing

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Data Vault Series 1 – Data Vault Overview – TDAN.com

  • The Data Vault is based on mathematical principles that allow it to be extensible and capable of handling massive volumes of information
  • The architecture and structure is designed to handle dynamic changes to relationships between information.

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What is Data Vault

  • What is Data Vault? Data Vault is a method and architecture for delivering a Data Analytics Service to an enterprise supporting its Business Intelligence, Data Warehousing, Analytics and Data Science requirements
  • At the core it is a modern, agile way of designing and building efficient, effective Data …

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Is Data Vault Modeling a Good Choice for Your Organization

  • Data Vault architecture is an innovative, hybrid approach that combines the best of 3rd Normal Form (3NF) and dimension modeling
  • This data modeling technique enables historical storage of data, integration of data from different operational systems, and tracing of the origin of all the data coming into the database.

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Reference Architecture

  • Based on the Agile Information Factory, we have developed a Reference Architecture which serves as a foundation to effectively and efficiently turn data into gold
  • We have a layered architecture which is geared for flexibility, scalability, cost efficiency and performance.

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Data Vault and DV 2.0 Education & Training – Data Vault 2

  • The Data Vault 2.0 System Of Business Intelligence represents a major evolution of the already successful Data Vault architecture
  • It has been extended beyond the Data Warehouse component to include a model capable of dealing with cross-platform data persistence, multi-latency and multi-structured data and massively parallel platforms.

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3 Reasons to Implement a Data Vault Model (and 2 Reasons

  • Aptitive has helped many organizations determine if reconsidering their architecture and implementing a data vault model is the answer for their challenges
  • If you’re interested in learning more about data modeling, data architecture, or data management in general, fill out our quick contact form to set up a free, no-obligation data

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Multiple Data Modeling Approaches with Snowflake Blog

  • For those of you have not yet heard of the Data Vault System of Business Intelligence or simply Data Vault (DV) modeling, it provides (among other things) a method and approach to modeling your enterprise data warehouse (EDW) that is agile, flexible, and scalable
  • The formal definition as written by the inventor Dan Linstedt:

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Overhauling our Data Warehouse with Data Vault by Weston

  • The Data Vault is a detail oriented, historical tracking and uniquely linked set of normalized tables that support one or more functional areas of business
  • It is a hybrid approach encompassing the

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The Data Vault Adventure

  • Data Vault is a method to model a database which is designed to provide long-term historical storage of data extracted from multiple operational systems
  • Apart from the modeling aspect, this method deals with issues such as auditing and tracing data from a historical point of view
  • Note that the Data Vault can’t track audit changes that

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Data Vault Model

  • Inmon, Daniel Linstedt, in Data Architecture: a Primer for the Data Scientist, 2015 Data Vault Model Defined
  • 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

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Data Vault Snowflake Data Warehousing Glossary

  • The Data Vault System of Business Intelligence or simply Data Vault (DV) modeling provides a method and approach to modeling your enterprise data warehouse (EDW) that is agile, flexible, and scalable
  • The formal definition as written by the inventor Dan Linstedt: The Data Vault is a detail oriented, historical tracking and uniquely linked set

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Data Vault Data Warehouse Architecture

Recorded on 19 Nov 2013 at PASS Data Warehousing and Business Intelligence Virtual Chapter (PASS DW/BI VC)Data Vault Data Warehouse Architecture Data vault i

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Data Vault Data Model for EDW – BI / DW Insider

  • However, the Data Vault model is only one part of the complete Data Vault architecture which contains three layers: 1) Data Staging Area which contains a copy of source data
  • 2) EDW which is designed using the Data Vault model
  • 3) Information Marts which are designed using a dimensional model are the source of data for end-user analysis.

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Data Vault Series 5 – Loading Practices – TDAN.com

  • As already indicated, the Data Vault is the modeling architecture for the enterprise data warehouse, and therefore becomes a system-of-record for the enterprise
  • The system of record must be reliable and consistent in all manners and aspects.

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Data vault: new weaponry in your data science toolkit

  • However, the Data Vault architecture also allows for a pretty neat extension beyond the typical data warehouse use case, where we use real-time data to augment the available structured data
  • This allows us to use the structure as provided by the Data Warehouse (either the Data Vault backend or the Kimball dimensional front end) but allowing for

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Conceptual, Canonical, and Raw Data Integration

  • i do not typically recommend the data vault model for use with “derived business data” – that job falls on data marts, and star-schema models are much more suited to business user access than data vault
  • the data vault model is suited for passive integration, scalability, flexibility, and …

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Enterprise Data Warehouse Using Data Vault

What is a Data Vault? • According to Data Vault Inventor (Dan Linstedt) –DV 1.0 - A Modeling approach • “A detail oriented, historical tracking and uniquely linked set of normalized tables that support one or more functional areas of business.” –DV 2.0 now include complete Architectural Blueprint and …

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Ch r is t o p h e r Ha y, Nim b le Data Vault architecture

  • Universal Data Vault: A case study in combining “Universal” data model patterns with the Data Vault architecture C h r i s t o p h e r A l e x a n d e r Ha y, Si lv er sto n Nim b le Lin s t e d t , p h a? ! (e t c
  • ) Kim b a ll In m o n Country Endeavours Pty Ltd “Creative solutions for Difficult Problems”

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Data Vault Automation WhereScape

  • Data Vault Automation additionally serves as a set of guardrails for teams new to Data Vault and data vault modeling ensuring they adopt the Data Vault 2.0 principles appropriately
  • Data Vault 2.0 automation can greatly accelerate an organization’s ability to implement a data vault architecture and incorporate flexibility, scalability and

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Optimizing the Data Vault architecture on Snowflake & free

  • Data Vault physical Data Model designed for the Snowflake Cloud Data Platform using SQLDBM
  • Following on from my recent presentation at the virtual Munich Data Modeling Meetup on how to optimize the Data Vault architecture on Snowflake, along with the session recording, the SQL scripts are now available.

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Tips for Optimizing the #DataVault Architecture on #

Tips for Optimizing the #DataVault Architecture on #Snowflake Data Vault is an architectural approach that includes a specific data model design pattern and methodology developed specifically to support a modern, agile approach to building an enterprise data …

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Data Vault Training

  • This class is an introductory and intermediate class that takes you through the why/what/how of Data Vault 2.0
  • It includes the coverage of the business justifications, then follows with the technical descriptions of the architecture, implementation, methodology, and modeling.

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Data Vault Next Level Data

  • An official definition states that a data vault “is a detail–oriented, historical tracking and uniquely linked set of normalized tables that support one or more functional areas of business
  • It is a hybrid approach encompassing the best of breed between 3rd normal form (3nf) and star schema.

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PerformanceG2, Inc. hiring Virtual Data Vault Architect in

Use Data Vault 2.0 system of business intelligence for the design of the acquisition, information and technical architecture for the data warehouse and related analytic data sets.

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Amazon.com: Data Architecture: A Primer for the Data

  • Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist
  • Drawing upon years of practical experience and using numerous examples and an easy to understand framework

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Amazon Redshift Performance Standards for Data Vault by

  • Amazon Redshift Performance Standards for Data Vault
  • “A long-exposure shot of a road in Ottawa with red light trails from the passing cars” by Marc-Olivier Jodoin on Unsplash
  • With the ever-increasing popularity of Amazon’s data warehousing service Redshift, there is a need to understand how this

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Vault (architecture)

  • In architecture, a vault (French voûte, from Italian volta) is a self-supporting arched form, usually of stone or brick, serving to cover a space with a ceiling or roof
  • The simplest kind of vault is the barrel vault (also called a wagon or tunnel vault), which is generally semicircular in shape
  • The barrel vault is a continuous arch, the length being greater than its diameter.

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