Overview
Data Sources
Customer Stories
Learn
Engage
Books
Holistics fully integrates with your dbt project, allows you to perform data modeling and transformation at dbt layer, and push those definitions to Holistics BI layer.
Read Docs
If you're using dbt and a BI tool, you may face these problems:
Holistics BI fixes these problems by integrating deeply with dbt, allowing data and metadata to flow between the 2 systems seamlessly, and also allow your BI code to be version-controlled in the same code repository with your dbt code.
That way, you can leverage dbt as a central place for data documentation, what can be synced with Holistics, and use Holistics as the the central entrypoint for data consumers.
Use dbt to transform and materialize into data tables inside data warehouse.
dbt models and data are loaded into Holistics for further manipulation.
Define custom measures and formulas in Holistics for exploration.
End users perform self-service exploration based on predefined datasets and reports.
You use dbt for the transformation of data into pre-aggregated tables. You model those tables (i.e materialized views) into Holistics. In Holistics, you define logical metrics and formulas, and turn those into datasets and reports for end consumers.
When your dbt models' data are refreshed, Holistics automatically triggers refreshes to your BI reports. Changes to your dbt medata are also pushed over to Holistics.
Both your dbt logic and BI logic are stored in the same Git repository, making it easy to manage and maintain.
When you change metadata in dbt model, Holistics automatically picks those up and reflect into the BI layer. Relevant datasets and reports will be updated.
Business users can get access to schema metadata that data teams define in dbt docs.
When dbt runs and underlying table data is updated, trigger will inform Holistics. Holistics can refresh data in relevant reports using that model.
You can maintain a single Github repository with both dbt and Holistics (AML) code. This represents the customer's analytics pipeline as code.
No you do not need to, and you're not meant to. Holistics is designed to complement dbt.
dbt is great for doing most of your central definitions modeling, but you will face some constraints when it comes to modeling metrics. For example, in dbt you cannot define non-additive metrics (unique calculating metrics like DAUs/MAUs), this has to be done outside of the database.
That's where Holistics modeling comes in, you define logical metrics in Holistics, and only resolve to SQL query when the end-users explore the data.
You use dbt as dimensional modeling layer, while Holistics as metrics modeling layer. In short: "Metrics for Holistics, Dimensions for dbt".
Learn more in docs.
What are the things should be done in dbt, and what should be done in Holistics?
What if I don't use dbt? Can I still do dimensional modeling?