Contribute to Flipside Data

A guide to data curation with Flipside.
Public Data Models
Links to the public Github repos for all data model source code can be found in the table docs for each blockchain/blockchain project.
Contribute Tags & Labels
See How to Add Tags on the Tags data model page.
Contribute Models with DBT
The Flipside community uses DBT to model data. This section gives a complete overview of getting set up to contribute models with DBT. (Hint: if you know SQL, you're 95% of the way to knowing DBT.) Avalanche and Ethereum repositories are setup for community curation.
Access
Snowflake (ask in #community-curation-beta discord channel)
"Hi , I’m interested in doing data curation for Flipside, could you give me snowflake access please? I’d like my username to be:  community_<insert_username>"
dbt cloud [Optional]
Sign up on your own:  https://www.getdbt.com/signup/​
Software Setup
(Optional) Install dbt on your terminal:  https://docs.getdbt.com/dbt-cli/install/overview​
Install Git (if you don’t have it):  https://git-scm.com/book/en/v2/Getting-Started-Installing-Git​
Download Docker desktop: https://docs.docker.com/get-docker/​
Install VSCode: https://code.visualstudio.com/download​
Project Setup
1.Clone the project you want to work on from FlipsideCrypto Github Org​
Example for Ethereum: git clone https://github.com/FlipsideCrypto/ethereum-models.git
2.Open the project in VSCode
3.Create a .env file in the root directory of your project (note: the .env file will not be committed to the source)
There is a provided .env.sample in the repository you may use as reference to create .env
4.Start a new branch in the repository
Ensure you have the latest pulled down from the main branch
git checkout main
git pull
Branch name should follow convention:  community/<branch_name>
Ex:  git checkout -b community/my-new-model
5.Start dbt console (this is where you will run your models to have them deployed to snowflake)
make dbt-console
6.Within the console, run dbt debug to ensure all connections are working
You are now ready to create your first data model!
Create and contribute your first model
1.Understand the modeling structure
Data models iterate through different “layers”. Generally speaking these are bronze, silver, and core.
Bronze is raw data layer
Silver is intermediate data modeling layer
Core is the presentation layer, aka what is exposed to the public
Most of the time you will be working within the silver layer
2.Create model file within the proper layer and naming convention
Naming convention for file is <layer name>__<table name>.sql
Example for silver layer:  ./models/silver/silver__my_new_table.sql
3.Create a corresponding .yml file
This should hold any model/column descriptions and tests for the model/columns
Example for silver layer:  ./models/silver/silver__my_new_table.yml
4.Start writing SQL code
Write code in Snowflake Web UI (Snowsight):  https://app.snowflake.com/us-east-1/vna27887​
Snowsight documentation:  https://docs.snowflake.com/en/user-guide/ui-snowsight.html​
5.Turn SQL code into DBT SQL
dbt combines sql with Jinja, see more: https://docs.getdbt.com/docs/building-a-dbt-project/jinja-macros​
Copy sql code into the file created earlier
Add config section
See existing models for examples
Dbt docs for available configurations https://docs.getdbt.com/reference/resource-configs/snowflake-configs​
Convert SQL elements into Jinja
Commonly this is changing table name to references and adding incremental loading logic
Ref: https://docs.getdbt.com/reference/dbt-jinja-functions/ref​
Incremental logic: https://docs.getdbt.com/docs/building-a-dbt-project/building-models/configuring-incremental-models​
1.See existing curated models for examples on how to implement incremental logic. ethereum-models/models/silver/silver__transactions.sql
6.Run the dbt model to put it into the community dev database. You must run this command within the dbt-console. If you have exited the console run make dbt-console again
dbt run -s <path to model>
Run: https://docs.getdbt.com/reference/commands/run​
If this is your 1st time using dbt run in this docker container, you may need to run dbt deps command
Models will be deployed to <BLOCKCHAIN>_COMMUNITY_DEV database within Snowflake.  This database refreshes daily at 04:00 AM UTC, any non-production resources will be erased.
7.Run the test file
dbt test -s <path to mode>
Tests: https://docs.getdbt.com/docs/building-a-dbt-project/tests​
8.Create GitHub Pull Request and write a summary on your updates/changes, as well as attaching passing test logs
9.Request Reviewer from one of the GitHub handles below
austinFlipside
juls858
James-Mission
desmond-hui
10.Fix Review Comments
11.Re-Request Reviewer after fixing review comments
12.If your PR has been approved, merge it to production
13.Congratulations! You’ve successfully contributed a data model!
Our Data - Previous
Access Flipside Data
Next - Our Data
Tables
Last modified 2mo ago