Use an IDE

We love our UI, but here’s how to develop in your favorite IDE, too.

Create a new pipeline

Each pipeline is represented by a YAML file in a folder named pipelines/ under the Mage project directory.

For example, if your project is named demo_project and your pipeline is named etl_demo then you’ll have a folder structure that looks like this:

demo_project/
|   pipelines/
|   |   etl_demo/
|   |   |   __init__.py
|   |   |   metadata.yaml

Create a new folder in the demo_project/pipelines/ directory. Name this new folder after the name of your pipeline.

Add 2 files in this new folder:

1. __init__.py
2. metadata.yaml

In the metadata.yaml file, add the following content:

blocks: []
name: etl_demo
type: python
uuid: etl_demo

Change etl_demo to whatever name you’re using for your new pipeline.

Sample pipeline metadata content

This sample pipeline metadata.yaml will produce the following block dependencies:

blocks:
- downstream_blocks:
    - select_columns
  executor_type: local_python
  language: python
  name: load_data_from_file
  type: data_loader
  upstream_blocks: []
  uuid: load_data_from_file
- downstream_blocks:
    - export_to_file
  executor_type: local_python
  language: python
  name: select_columns
  type: transformer
  upstream_blocks:
    - load_data_from_file
  uuid: select_columns
- downstream_blocks: []
  executor_type: local_python
  language: python
  name: export_to_file
  type: data_exporter
  upstream_blocks:
    - select_columns
  uuid: export_to_file
name: etl_demo
type: python
uuid: etl_demo

metadata.yaml sections

Pipeline attributes

blocks · array of objects

An array of blocks that are in the pipeline.

name · string

Unique name of the pipeline.

type · string enum

The type of pipeline. Currently available options are:

databricks, integration, pyspark, python (most common), streaming.

uuid · string

Unique identifier of the pipeline. This UUID must be unique across all pipelines.

Block attributes

downstream_blocks · array of strings

An array of block UUIDs that depend on this current block. These downstream blocks will have access to this current block’s data output.

executor_type · string enum

The method for running this block of code. Currently available options are:

ecs, gcp_cloud_run, azure_container_instance, k8s, local_python (most common), pyspark.

language · string enum

The code language the block is using. Currently available options are:

python (most common), r, sql, yaml.

name · string

Unique name of the block.

type · string enum

The type of block. Currently available options are:

chart, custom (most common), data_exporter, data_loader, dbt, scratchpad, sensor, transformer.

The type of block will determine which folder it needs to be in. For example, if the block type is data_loader, then the file must be in the [project_name]/data_loaders/ folder. It can be nested in any number of subfolders.

upstream_blocks · array of strings

An array of block UUIDs that this current block depends on. These upstream blocks will pass its data output to this current block.

uuid · string

Unique identifier of the block. This UUID must be unique within the current pipeline. The UUID corresponds to the name of the file for this block.

For example, if the UUID is load_data and the language is python, then the file name will be load_data.py.