Silver Fanout Demo

Apply Changes From Snapshot Demo

• This demo will perform following steps
  • Showcase onboarding process for apply changes from snapshot pattern
  • Run onboarding for the bronze stores and products tables, which contains data snapshot data in csv files.
  • Run Bronze Lakeflow Declarative Pipeline to load initial snapshot (LOAD_1.csv)
  • Upload incremental snapshot LOAD_2.csv version=2 for stores and product
  • Run Bronze Lakeflow Declarative Pipeline to load incremental snapshot (LOAD_2.csv). Stores is scd_type=2 so updated records will expired and added new records with version_number. Products is scd_type=1 so in case records missing for scd_type=1 will be deleted.
  • Upload incremental snapshot LOAD_3.csv version=3 for stores and product
  • Run Bronze Lakeflow Declarative Pipeline to load incremental snapshot (LOAD_3.csv). Stores is scd_type=2 so updated records will expired and added new records with version_number. Products is scd_type=1 so in case records missing for scd_type=1 will be deleted.

Steps:

1. Launch Command Prompt

2. Install Databricks CLI

   • Once you install Databricks CLI, authenticate your current machine to a Databricks Workspace:

   databricks auth login --host WORKSPACE_HOST

3. Install Python package requirements:

   # Core requirements
   pip install "PyYAML>=6.0" setuptools databricks-sdk
   
   # Development requirements
   pip install flake8==6.0 delta-spark==3.0.0 pytest>=7.0.0 coverage>=7.0.0 pyspark==3.5.5

4. Clone dlt-meta:

   git clone https://github.com/databrickslabs/dlt-meta.git 

5. Navigate to project directory:

   cd dlt-meta

6. Set python environment variable into terminal

   dlt_meta_home=$(pwd)

   export PYTHONPATH=$dlt_meta_home

7. Run the command:

   python demo/launch_acfs_demo.py --uc_catalog_name=<<uc catalog name>>

   • uc_catalog_name : Unity catalog name
   • you can provide --profile=databricks_profile name in case you already have databricks cli otherwise command prompt will ask host and token.