.jpg)
What makes analysts agile is having a solid base of cleaned and structured data, and with reconfigured, you can build this type of domain layer iteratively. Swiftly add a new column to your data set when you need it more than twice, and your data gets more organized, not messier, for the recurring analysis and dashboards in BI tools.
You can skip presentations and additional documentation with a visual interface communicating your data structure and dependencies for the stakeholders. SQL is excellent for explorative analysis, but scattered SQL files make it hard for stakeholders to understand the business logic powering the reports.
We guide the business user to focus on the desired output table while embedding the data modeling best practices and removing the complexity of developer workflows. Your company's business logic is unique, but analysts can define it without knowing data engineering best practices - we cover this part.
The code we build is exposed for you to review and evaluate.
We do not process any of your actual data - all we need to know are your warehouse schema, table, column & data type combinations.
Any changes made to your models are instantly reflected and propagated across all relevant files.
We do not modify your existing data models ever. All reconfigured models live within a container directory in your project.
You know more agility would come from graduating one-off queries into reusable data sets, but you don't have the time and capacity to work on data infrastructure.
Your data engineering team serves a large analyst community. You want to share data modeling ownership, but analysts have no time to learn developer workflows and dbt.
You joined the company early and understand the business, so the requests to power analytics are piling on your backlog - eating time from other projects.
.png)
PRESENTATION LAYER:
Quickly enable visualizations with BI tools, automate product-led growth processes, and power feature development due to the robust domain layer powering all use cases.
DOMAIN LAYER:
The platform guides you in designing conceptual and logical data models and compiles the physical data model with every change you make. You can use any dbt or SQL macro to transform edge cases.
STAGING LAYER:
The first layer ensures incremental loading and historicization for your reports. Engineers can define guardrails for the allowed joins and join paths between sources.
We're in early-stages of development and always looking to hear from analytics professionals. Catch us via our calendar below.
