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Santiago Rivera
Santiago Rivera

Building A Scalable Data Warehouse With Data Vault 2.0 Downloads 17



Because the analytical master data has been provided as reference tables to the enterprise data warehouse, it is easy to perform lookups into the analytical master data, even if the Business Vault is virtualized. The following DDL statement is based on the computed satellite created in the previous section and joins a reference table to resolve a system-wide code to an abbreviation requested by the business user:




Building a Scalable Data Warehouse with Data Vault 2.0 downloads 17



If reference data should be loaded without taking care of the history, the loading process can be drastically simplified by using SQL views to create virtual reference tables. A similar approach was described in Chapter 11, Data Extraction, when staging master data from Microsoft Master Data Services (MDS) or any other master data management solution that is under control of the data warehouse team and primarily used for analytical master data. This approach can be used under the following conditions:


If all these conditions are met, a virtual SQL view can be created in order to virtually provide the reference data to the users of the Raw Data Vault. This approach is typically used when providing reference data from an analytical MDM solution that is under control and managed by the data warehouse team. Such data is also staged virtually and centrally stored in the MDM application. The following DDL creates an example view that implements a nonhistorized reference table in the Raw Data Vault:


In many other cases, especially if the data is already staged in the staging area, it should be materialized into the data warehouse layer to ensure that data is not spread over multiple layers. This decoupling from the staging area prevents any undesired side-effects if other parties change the underlying structure of the staging area. In such cases, the reference table is created in the data warehouse layer, for example by a statement such as the following:


The structure of the reference table follows the definition for nonhistorized reference tables outlined in Chapter 6. The primary key of the reference table consists of the Code column. Because this column holds a natural key instead of a hash key, the primary key uses a clustered index. There are multiple options for loading the reference table during the loading process of the Raw Data Vault. The most commonly used adds new and unknown reference codes from the staging area into the target reference table and updates records in the target that have changed in the source table. This way, no codes that could be used in any one of the satellites is lost. While it is not recommended to use the MERGE statement in loading the data warehouse, it is possible to load the reference table this way:


Data Vault 2.0 includes an agile methodology and offers best practices and high-quality standards that are perfect for automation. While completed Data Vaults deliver many benefits, designing and developing them by hand requires significant time, effort and money. Data Vault Automation helps data warehousing teams deliver Data Vaults into production faster and with less risk.


Data Vault 2.0 is a system of business that extends beyond the enterprise data warehouse to offer a data model capable of dealing with cross-platform data persistence, multi-latency and multi-structured data, and massively parallel platforms. The Data Vault 2.0 model, invented by Dan Linstedt, simplifies data integration from multiple sources, makes it easy to evolve or add new data sources without disruption, and increases scalability and consistency in enterprise data infrastructure.


Jeff Harris and Steve Hoberman explain data modeling and how to get up and running with erwin DM. Step by step, business analysts, data professionals and project managers will learn how to build effective conceptual, logical and physical data models.


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I am seeing a HUGE uptick in interest in Data Vault around the globe. Part of the interest is the need for agility in building a modern data platform. One of the benefits of the Data Vault 2.0 method is the repeatable patterns which lend themselves to automation. I am please to pass on this great new post with details on how to automate building your Data Vault 2.0 architecture on Snowflake using erwin! Thanks to my buddy John Carter at erwin for taking this project on.


Successfully implementing a Data Vault solution requires skilled resources and traditionally entails a lot of manual effort to define the Data Vault pipeline and create ETL (or ELT) code from scratch. The entire process can take months or even years, and it is often riddled with errors, slowing down the data pipeline. Automating design changes and the code to process data movement ensures organizations can accelerate development and deployment in a timely and cost-effective manner, speeding the time to value of the data.


This book will give you a short introduction to Agile Data Engineering for Data Warehousing and Data Vault 2.0. I will explain why you should be trying to become Agile, some of the history and rationale for Data Vault 2.0, and then show you the basics for how to build a data warehouse model using the Data Vault 2.0 standards.In addition, I will cover some details about the Business Data Vault (what it is) and then how to build a virtual Information Mart off your Data Vault and Business Vault using the Data Vault 2.0 architecture.So if you want to start learning about Agile Data Engineering with Data Vault 2.0, this book is for you.


100 attendees got their minds filled and horizons broadened by an amazing slate of presentations given by great speakers from all over the world. Not only did we hear some real-life case studies from companies like Micron and Intact Financials (who have VERY large data vaults) but we even got to hear from someone at the US DoD (yes the Department of Defense!).


So, if you would like to join the elite group of 100 data vault aficionados that attended WWDVC17, you now have the chance to see and hear the same great content we all were exposed to. Then you can be the champion for brining Data Vault 2.0 to your organization.


I am a Business Intelligence (BI) Architect, so this blog skews towards a view through the analytics lens. My initial motivation for adopting the DVM was to help a customer find a way to provide analysts with immediate access to new data sources, albeit raw and dirty. That is, as opposed to waiting another few years for everyone to hammer out the complex web of competing goals. With relief to the analysts, which is much better than nothing, the rough edges could be smoothed out in a well-planned and iterative fashion.


The TL;DR for BI Architects simply seeking something more versatile than traditional star/snowflake data warehouses and want nothing to do with the OLTP side: Just read the two topics, Data Vault Methodology and Data Vault Implementation Issues.


The Domain Model and the Data Vault configuration represent types of metadata-driven concepts. For example, from data vault metadata, Metadata-driven applications generate database schemas, ETL/ETL objects, deployment scripts, etc. Changes to the system are automatically propagated downstream and human errors are minimized.


So arguments, misunderstandings, and impasses go on and on. Before you know it, the project deadline has passed, maybe even years. What if we instead backed off from providing nothing but the perfect solution and first provide relief to analysts with raw data and iteratively usher in integration and change?


The Data Vault Methodology is the recipe for the implementation of an analytics database that is more robust than the traditional star/snowflake data warehouse. As depicted in Figure 7, data vaults lie somewhere between the chaos and anarchy of an unstructured data lake and the rigidity of a highly transformed star schema.


Data is extracted and loaded into a Raw Data Vault in its raw format. This means we maintain a record of the data as it was made, without any transforms. All data changes are recorded. In DW terms, all entities are Type 2 slowly-changing dimensions. This enables:


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