dbt Core
Connect your decube platform to dbt Core to see all data jobs in the Catalog and see end-to-end lineage.
Last updated
Connect your decube platform to dbt Core to see all data jobs in the Catalog and see end-to-end lineage.
Last updated
This documentation is on how to add a data source connection to dbt Core, which is the open source framework for dbt. If you are interested to connect to your dbt Cloud instance instead, please check out this documentation for dbt Core.
Integrating DBT Core with Decube involves reading files from an AWS S3 bucket, which shares similarities with how AWS S3 itself connects to the platform.
A summary of steps to set up dbt core:
Set up an S3 bucket following the same procedure outlined in our documentation for AWS S3.
Define folder partitions (details will be provided in the following section).
Upload the necessary files to those partitions.
Following these steps, the metadata collector will connect to the S3 bucket and retrieve the data.
decube supports ingesting information from multiple dbt projects. You would need to structure the bucket using a format that we define based on the current date.
Given that base_path for a single project uses the following format:
base_path = ”${year}/${month}/${day}” where:
year = $(date +%Y)
month = $(date +%B)
day = $(date +%d)
Example of a folder partition on your S3 - s3://your-bucket/${base_path}
Where the full path of the folder could be s3://your-bucket/2024/May/01/
After setting up the format based on the current date partition, you can proceed to define your own structure.
decube currently supports reading two-level deep bucket structure. You could define how you would want to upload project files into separate directories.
Basically, all of the following are valid bucket path and you can refer to the examples below:
project_a
year=2024
month=May
day=01
[location of project files]
project_b
Same as project_a
project_c
Same as project_a
dev
project_a
year=2024
month=May
day=01
[location of project files]
project_b
Same as project_a
project_c
Same as project_a
prod
project_a_prod
project_b_prod
…
project_a
year=2024
month=May
day=01
[location of project files]
year=2024
month=May
day=01
[location of project files]
You would need to upload specific files from the target/ directory into the bucket after your dbt command has concluded.
manifest.json, which is generated by any command that parses your project. Here is an example of a command that generates the file:
dbt run —full-refresh
This single file contains a full representation of your dbt project's resources (models, tests, macros, etc), including all node configurations and resource properties.
run_results.json, which is generated by a few commands such as build, compile, and run just to name a few (you can refer to the documentation). Here is an example of a command that generates the file:
dbt build
This file contains information about a completed invocation of dbt, including timing and status info for each node (model, test, etc) that was executed.
catalog.json, which is only produced by docs generates and is optional. This is required if you want to acquire column metadata. The command can be run like so:
dbt docs generate
This file contains information from your data warehouse about the tables and views produced and defined by the resources in your project.
To ensure the collector runs successfully, you will need to upload in the following manner:
(in pair) manifest.json and run_results.json or
(in triplets) manifest.json and run_results.json and catalog.json.
Please be aware in order for the lineage to connect successfully with accuracy, you would need to configure the source tables on your dbt project.
For uploading the project files, you may choose to do the following:
Only upload the latest project files to the specified bucket where there is only one set of manifest.json and run_results.json in that bucket for that folder partition at any time.
Caution: If you were to do it this way, you may lose out information of the runs before the latest project files are processed.
Retain a series of project files based on the timestamp of when it was run. For example, for each run append a timestamp after the filename:
Do: manifest_20240503142827.json
Do not: 20240503142827_manifest.json
Timestamped project file in this example was generated using the following commands:
Using timestamp=$(date +%Y%m%d%H%M%S) to create manifest_${timestamp}.json
Note: To ensure that each project is successfully collected by our metadata collector, we recommend uploading the manifest.json and run_results.json in the same folder. If you want to include column metadata, make sure you include catalog.json as well.
Here is a sample script for uploading the project files:
You may modify and integrate this into your existing workflows.
After following the above steps, you may start ingesting the metadata from your DBT Core bucket into decube by navigating to My Account > Data Sources Tab > Connect A New Data Source > DBT Core.
where 'Path' follows these format: s3://some-bucket s3://some-bucket/path-to-dbt-core
Please provide the required credentials and click "Test This Connection" to verify their validity. Afterward, assign a name to your data source, and by selecting the "Connect This Data Source" option, your connection between DBT Core and Decube will be successfully established.
Currently, only S3 storage is supported for DBT Core under the "Storage" dropdown.
Once you have connected your dbt core, you will then need to map the connection sources to the data sources on the decube platform. Refer how to do that in this documentation.
