yetl-framework 1.0.2

yet (another spark) etl framework

yetl

Install

pip install yetl-framework

Configuration framework for databricks pipelines. Define configuration and table dependencies in yaml config then get the table mappings config model:

Define your tables.

landing: # this is the landing stage in the deltalake house
  read: # this is the type of spark asset that the pipeline needs to read
    landing_dbx_patterns:
      customer_details_1: null
      customer_details_2: null

raw: # this is the bronze stage in the deltalake house
  delta_lake: # this is the type of spark asset that the pipeline needs to read and write to
    raw_dbx_patterns: # this is the database name
      customers: # this is a table name and it's subsequent properties
        ids: id
        depends_on:
          - landing.landing_dbx_patterns.customer_details_1
          - landing.landing_dbx_patterns.customer_details_2
        warning_thresholds:
          invalid_ratio: 0.1
          invalid_rows: 0
          max_rows: 100
          min_rows: 5
        exception_thresholds:
          invalid_ratio: 0.2
          invalid_rows: 2
          max_rows: 1000
          min_rows: 0
        custom_properties:
          process_group: 1

base: # this is the silver stage in the delta lakehouse
  delta_lake: # this is the type of spark asset that the pipeline needs to read and write to
    # delta table properties can be set at stage level or table level
    delta_properties:
      delta.appendOnly: true
      delta.autoOptimize.autoCompact: true    
      delta.autoOptimize.optimizeWrite: true  
      delta.enableChangeDataFeed: false
    base_dbx_patterns: # this is a database name
      customer_details_1: # this is a table name and it's subsequent properties
        ids: id
        depends_on:
          - raw.raw_dbx_patterns.customers
        # delta table properties can be set at stage level or table level
        # table level properties will overwride stage level properties
        delta_properties:
            delta.enableChangeDataFeed: true
      customer_details_2: # this is a table name and it's subsequent properties
        ids: id
        depends_on:
          - raw.raw_dbx_patterns.customers

Define you load configuration:

version: 1.0.0
tables: ./tables.yaml

landing: # this is the landing stage in the deltalake house
  read: # this is the type of spark asset that the pipeline needs to read from
    trigger: customerdetailscomplete-{{filename_date_format}}*.flg
    trigger_type: file
    container: datalake
    root: "/mnt/{{container}}/data/landing/dbx_patterns/{{table}}/{{path_date_format}}"
    filename: "{{table}}-{{filename_date_format}}*.csv"
    filename_date_format: "%Y%m%d"
    path_date_format: "%Y%m%d"
    format: cloudFiles
    spark_schema: ../schema/{{table.lower()}}.yaml
    options:
      # autoloader
      cloudFiles.format: csv
      cloudFiles.schemaLocation:  /mnt/{{container}}/checkpoint/{{checkpoint}}
      cloudFiles.useIncrementalListing: auto
      # schema
      inferSchema: false
      enforceSchema: true
      columnNameOfCorruptRecord: _corrupt_record
      # csv
      header: false
      mode: PERMISSIVE
      encoding: windows-1252
      delimiter: ","
      escape: '"'
      nullValue: ""
      quote: '"'
      emptyValue: ""
    

raw: # this is the bronze stage in the deltalake house
  delta_lake: # this is the type of spark asset that the pipeline needs to read and write to
    # delta table properties can be set at stage level or table level
    delta_properties:
      delta.appendOnly: true
      delta.autoOptimize.autoCompact: true    
      delta.autoOptimize.optimizeWrite: true  
      delta.enableChangeDataFeed: false
    managed: false
    create_table: true
    container: datalake
    root: /mnt/{{container}}/data/raw
    path: "{{database}}/{{table}}"
    options:
      checkpointLocation: /mnt/{{container}}/checkpoint/{{database}}_{{table}}
      mergeSchema: true

Import the config objects into you pipeline:

from yetl import Config, Timeslice, StageType

pipeline = "auto_load_schema"
project = "test_project"
timeslice = Timeslice(day="*", month="*", year="*")
config = Config(
    project=project, pipeline=pipeline
)
table_mapping = config.get_table_mapping(
    timeslice=timeslice, stage=StageType.raw, table="customers"
)

print(table_mapping)

Use even less code and use the decorator pattern:

@yetl_flow(
        project="test_project", 
        stage=StageType.raw
)
def auto_load_schema(table_mapping:TableMapping):

    # << ADD YOUR PIPELINE LOGIC HERE - USING TABLE MAPPING CONFIG >>
    return table_mapping # return whatever you want here.


result = auto_load_schema(table="customers")

Development Setup

pip install -r requirements.txt

Unit Tests

To run the unit tests with a coverage report.

pip install -e .
pytest test/unit --junitxml=junit/test-results.xml --cov=yetl --cov-report=xml --cov-report=html

Integration Tests

To run the integration tests with a coverage report.

pip install -e .
pytest test/integration --junitxml=junit/test-results.xml --cov=yetl --cov-report=xml --cov-report=html

Build

python setup.py sdist bdist_wheel

Publish

twine upload dist/*