Welcome to etl_toolkit’s documentation!#
The etl_toolkit contains many utilities to write better pyspark to simplify pipelines on Databricks.
Navigate to each module of the etl_toolkit to learn about the functions provided and how to use them.
expressions: Contains functions for deriving complex
pyspark.Columnsanalyses: Contains functions for deriving complex
pyspark.DataFramesthat apply many data transformations.
Contents:
Emodule (“expressions”)Amodule (“analyses”)- Calculation Analyses
- Card Analyses
- E-Receipt Analyses
- Dedupe Analyses
- Index Analyses
- Investor Standard Analyses
- Investor Reporting Analyses
- Ordering Analyses
- Parser Analyses
- Scalar Analyses
- Time Analyses
- Comparison Analyses
- Investor Standard Metrics Analyses
standard_metric_unified_kpi()standard_metric_unified_kpi_derived()standard_metric_data_download()standard_metric_live_feed()standard_metric_feed()standard_metric_daily_growth()standard_metric_quarter_month_pivot()standard_metric_trailing_day_pivot()standard_metric_net_adds()standard_metric_weekly_qtd_progress()standard_metric_monthly_qtd_progress()standard_metric_daily_qtd_progress()standard_metric_half_year_progress()standard_metric_ui_metadata()
- Calendar Analyses
- Table creation functions
- Mutable Table functions