Getting Started

We're going to walk through setting up Zenlytic from scratch using your local development environment. You should have received a login to your workspace to begin the setup process.

Defining your data model.

Documentation on defining your data model can be found here. You'll first need to create a GitHub repo, if one has not been defined already, then in that repo define the models and views you want.

Zenlytic UI

To avoid messing around with your local python, you can use the Zenlytic UI for all tasks listed below. The UI has error tracking built in, so you'll know if something isn't right.

Once you create your repo, you can install the most recent version of the metrics layer with the database option of your choice.

pip install metrics-layer[snowflake]

This will install the metrics-layer package with the connector for Snowflake. It will also give you access to the ml command line interface, which you'll use throughout the setup process.

Then you'll run the init command to create your project structure. This will create folders and the zenlytic_project.yml file.

ml init

Then you'll need to define your local connection credentials. You'll do this in exactly the same way as you would for dbt. Make sure that profile in your zenlytic_project.yml file is set to the same name as the dbt profile you just created.

Now you can use the seeding capability to make setup of the data model much easier. To seed all view files for a database schema run

ml seed --schema <YOUR_SCHEMA_NAME>

To seed a specific table run

ml seed --schema <YOUR_SCHEMA_NAME> --table <YOUR_TABLE_NAME>

To ensure your data model is correct you can run validation in the root of your repo and it will give you any warnings or errors associated with your project.

ml validate

There are a some example repos to help you as well! Here's one for our standard yaml syntax.