vedana-etl 0.6.2

Pipeline template for Vedana

Basic pipeline for all vedana projects.

This pipeline:

• Parses Grist Data & Data Model
• Ensures that Memgraph index/vector index structure is in sync with data model
• Updates Memgraph database in incremental fashion

To add steps:

1. Pass extra transformations to get_pipeline
2. Create new app configuration from app.py

Pipeline Labels Hierarchy

Pipeline

labels=("pipeline", "pipeline_name") defines a set of operations as standalone, sort of like a DAG in Airflow or a Dagster Job. Its purpose is to be able to render it as a separate tab on the ETL page of Backoffice in order to look at it independently of other transformations

Stage

labels=("stage", "stage_name") defines a stage of pipeline. Currently, stages are useful for creating and managing observability features, such as main dashboard's Ingest table, which displays DataTable's of all transformations with labels=("stage", "extract"). Stages are also useful when running the pipeline manually.

Flow

labels=("flow", "flow_name") helps execute a pipeline (or possibly several pipelines) in a nice fashion, used in defining cron jobs, etc.