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Data Pipeline Development just got easier ...

Data Vault generation with FastFlow for DBT : save the time you spend on basic set-up and monitoring. Shift your focus on modeling to drastically increase your return on data.


In a modern data environment, the data runs through various stops. To still provide continuous data quality, it must always be clear where data has come from. Data Vault has made it easier to do so by inherently enabling auditing, as load times and record sources are required for every row. It also tracks all historical changes as satellites, including the load time as part of the primary key. When an attribute is updated, a new record is created. 

All of this auditing enables you to easily provide auditability for both regulatory and data governance purposes. And because you store all of your history, you can access data from any point in time.

Data Vault enables quicker data loading simply because a number of tables can be loaded at the same time in parallel. The model decreases dependencies between tables during the load process and simplifies the ingestion process by leveraging inserts only, which load quicker than upserts or merges. This also leads to less complexity.

FastFlow for DBT

A perfect fit for multi-source systems or those that have constantly changing relationships.

The reason why it works well for these systems is its ability to make adding attributes simple. If there is a change to one source system, that change does not need to show up within all source systems. Similarly, you can limit the number of places changes are made, as attributes are stored separately from structural data in satellites.

Additionally, it is easier to account for new and changing relationships by closing off one link and creating another. You don’t have to change the historical data to account for a new relationship or update an existing schema. You only need to account for the changes going forward. This brings enormous flexibility and scalability into your enterprise data warehouse.

FastFlow for DBT Highlights

Decrease Complexity with Insert Only Architecture optimal for the cloud.
Platform Independent & dynamic using Python Code: simple, free & easy to use.
Track all historical changes as satellites, including the load time.
Providing continuous data quality. Logging to Constantly measure succes.

Want to know more about our approach or our framework?

Talk to us about your data challenges and together we will draft your roadmap.