Building A Scalable Data Warehouse With Data Vault 2.0 Downloads 17
Epickson Soto
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:
Building a Scalable Data Warehouse with Data Vault 2.0 downloads 17
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dbt offers a command-line utility developed in Python that can run on your desktop or inside a VM in your network and is free to download and use. Alternatively, you can use their SaaS offering dbt Cloudwhich functions as a dbt IDE.
The Data Vault 2.0 method uses a small set of standard building blocks to model your data warehouse (Hubs, Links and Satellites in the Raw Data Vault) and, because they are standardised, you can load these blocks with templated SQL. dbt allows for a template-driven implementation using Jinja. This leads to better quality code, fewer mistakes, and greatly improved productivity: i.e. Agility.
The AutomateDV package generates and runs Data Vault ETL code from your metadata (table names and mapping details) which is then provided to your dbt models contains calls to AutomateDV macros.The macro does the rest of the work: it processes the metadata, generates SQL and then dbt executes the load respecting any and all dependencies.
dbt even runs the load in parallel. As Data Vault 2.0 is designed for parallel load and Snowflake is highly parallelised, your ETL load will finish in rapid time. Your experience may vary form platform to platform, however we aim to be asconsistent as possible.
Michael has more than 15 years of experience in IT and has been working on business intelligence topics for the past eight years. He has consulted for a number of clients in the automotive industry, insurance industry and non-profits. In addition, he has consulted for government organizations in Germany on business intelligence topics. Michael is responsible for the Data Vault training program at Drffler + Partner GmbH, a German consulting firm specialized in data warehousing and business intelligence. He is also a lecturer at the University of Applied Sciences and Arts in Hannover, Germany. In addition, he maintains DataVault.guru, a community site on Data Vault topics.
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 Warehouses.
Data Vault might not be widely known due to its niche focus within data management. Where traditional data warehousing methods (such as Kimball and Inmon) have been around for decades, Data Vault emerged in the 2000s as a solution to modern data platform requirements.
Data Vault is designed specifically for organisations that need to run agile data projects where scalability, integration of multiple source systems, development speed and business orientation are important.
This paper describes and breaks down the pros and cons of different data modeling approaches. It explores the limitations of models such as Inmon and Kimball, and the comparative advantages of the Data Vault 2.0 model. It outlines how Data Vault 2.0 facilitates automation and speeds development and deployment through pattern-based structures and templates, explaining why it is the best approach for enterprise data warehouse (EDW) automation. It also describes how VaultSpeed tools support this model.
EDW systems are intended to process source data into useful information. The target model for the information is typically defined by the business user and is often a dimensional model, such as a star schema or snowflake schema, with facts and their dimensions. Business users select the model according to their information needs, for example when using a dashboard application.
The data model, however, is defined by the data warehouse architects and often selected based on traditional design decisions, known as bottom-up and top-down designs. These traditional models are limited in their automation capabilities.
Automation accelerates and standardizes the loading and modeling of the source data in the data warehouse data layer, which serves as a foundation for the next layer in the enterprise data warehouse, the information layer. Standardization makes it possible to deal with analysis over time and views from different data scientists.
Both options use a data mart for information delivery. Often, these data marts (also known as information marts in other architectures) are modeled using dimensional models, such as star schemas or snowflakeschemas.
Star and snowflake schemas are organized around fact entities that provide information that can be aggregated, for example, about retail transactions, call records, flights, and so on. These transactions or events provide measure values, such as, respectively, revenues, call durations and flight durations. They also provide dimensional attributes or dimension references that can be used to break down the aggregated measure values by dimensions such as products, customers or airport locations. The facts, their measures and the dimensions are defined by the business user in an information requirement.
The difference between star and snowflake schemas is quite simple. In a star schema, only fact entities can reference dimension entities, while in a snowflake schema, dimensions can also reference other dimensions.
In addition to these popular options for traditional data warehousing, organizations have adopted other approaches; in practice, they are often just an unmanaged, rampant mix of the above options with additional, free-style modeled entities.
However, the 3NF model was originally not intended for use in data warehousing. It was modified for this new purpose through the addition of effectivity timelines. But the added timeline also adds additionalcomplexities to the data model, especially the potential of having joins across timelines.
But there are bigger issues. Notably, when loading the model, the entities must be loaded in a certain order, driven by the business, creating dependencies that lead to cascading changes if any part needs to be modified. These dependencies can become quite a burden, particularly in larger enterprise models.
For example, the organization must be loaded before loading employees, because the organization is referenced by the employee to indicate the employer. If the organization is not known by the organizational entity, loading the employee will fail. If the organization entity needs to be modified, the employee entity must first be at least reviewed and tested, but potentially modified as well. But if the employee entity is touched, the salary payments entity must be reviewed, and so on.
As a result, most organizations try to prevent modifications to the model in the first place, but that often leads to a big-bang approach to the enterprise model: first, build the whole enterprise model before implementing reports on top. This approach is not very conducive to agile development and it becomes difficult to model the enterprise as the enterprise is constantly changing.
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