Data teams spend weeks or months understanding source system data, how it's connected & how it can be used — a task that can be accelerated using domain knowledge, data models & genAI assistance.
Business experts have to spend far too much time trying to connect with data teams. What if they could create a conceptual ER-diagram that explains their data requirements without having to work with any data experts?
The advancements in GenAI make this easy and fast. A domain expert can use Ellie to create ER diagrams instantly through text, diagrams, or via a chatbot.
What if your “semantic layer” isn’t only about gathering business requirements? What if you could convert it directly into a physical data model and SQL databases?
You can use Ellie to gather requirements and then model the data that’s required to meet those requirements on one platform.
Our semantic layer — conceptual ER-diagrams — can be converted into logical or physical data models, providing a direct connection between business and data.
A data team member, say an analytics engineer, can go from semantic or business understanding of a data problem to actually creating an implementable database on Ellie.
Our physical modeling canvas combines the ease of use of our conceptual canvas while closely mimicking the structure of an actual database.
When possible, you can continue to keep the connection between the tables an engineer has created and the entities used in an ER diagram that is easily understood by domain experts.
You can bring in a database schema from a modern cloud database solution like Snowflake, and Ellie will automatically draw ER diagrams as well as logical and physical data models.
This enables you to understand and explain what a database or data warehouse is using a conceptual ER-diagram.
In addition, each entity in the diagram has its own description apart from all the metadata that was brought in from your cloud database.
You can then reuse the tables to create new data products.
Your data catalog is a tightly integrated dictionary of business and data terms, but it’s rarely used. Connecting it to Ellie ensures that a data catalog is part of gathering requirements (ER diagrams) and connected to physical data models.
Your data team — from engineers to architects — now have an easier way to design and build data products.
You can also create approval workflows to update information in a data catalog if changes are made to definitions in Ellie.
Business experts have to spend far too much time trying to connect with data teams. What if they could create a conceptual ER-diagram that explains their data requirements without having to work with any data experts?
The advancements in GenAI make this easy and fast. A domain expert can use Ellie to create ER diagrams instantly through text, diagrams, or via a chatbot.
What if your “semantic layer” isn’t only about gathering business requirements? What if you could convert it directly into a physical data model and SQL databases?
You can use Ellie to gather requirements and then model the data that’s required to meet those requirements on one platform.
Our semantic layer — conceptual ER-diagrams — can be converted into logical or physical data models, providing a direct connection between business and data.
A data team member, say an analytics engineer, can go from semantic or business understanding of a data problem to actually creating an implementable database on Ellie.
Our physical modeling canvas combines the ease of use of our conceptual canvas while closely mimicking the structure of an actual database.
When possible, you can continue to keep the connection between the tables an engineer has created and the entities used in an ER diagram that is easily understood by domain experts.
You can bring in a database schema from a modern cloud database solution like Snowflake, and Ellie will automatically draw ER diagrams as well as logical and physical data models.
This enables you to understand and explain what a database or data warehouse is using a conceptual ER-diagram.
In addition, each entity in the diagram has its own description apart from all the metadata that was brought in from your cloud database.
You can then reuse the tables to create new data products.
Your data catalog is a tightly integrated dictionary of business and data terms, but it’s rarely used. Connecting it to Ellie ensures that a data catalog is part of gathering requirements (ER diagrams) and connected to physical data models.
Your data team — from engineers to architects — now have an easier way to design and build data products.
You can also create approval workflows to update information in a data catalog if changes are made to definitions in Ellie.
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