Skip to main content

Utilities for creating ETL pipelines with mara

Project description

Mara ETL Tools

Build Status PyPI - License PyPI version Slack Status

A collection of utilities around Project A's best practices for creating data integration pipelines with mara. The package is intended as a start for new projects. Forks/ copies are preferred over PRs.

For more details on how to use this package, have a look at the mara example project.

The package consists of a number modules that all can be used independently from each other:

SQL utility functions

Function initialize_utils in etl_tools/initialize_utils/init.py returns a pipeline that creates a util schema with a number of PostgreSQL functions for organizing data pipelines. Add to your root pipeline like this:

from etl_tools import initialize_utils

my_pipeline.add(initialize_utils.utils_pipeline(with_hll=True, with_cstore_fdw=True))

Please have a look at the .sql files in etl_tools/initialize_utils for available functions.

Schema copying

The file The file etl_tools/schema_copying.py contains the function add_schema_copying_to_pipeline that copies a PostgreSQL database schema from on host to another at the end of a pipeline run. This is useful for running the ETL and frontend tools on different database servers so that a running ETL does not affect the performance of dashboard queries.

Given that there is a pipline my_pipeline that has a number of child pipelines with the Schema label set to the respective schema to copy, then this is how the schema copying can be added to those child pipelines.

from mara_db import dbs
from data_integration.commands.sql import ExecuteSQL
from data_integration.pipelines import Task
from etl_tools.schema_copying import add_schema_copying_to_pipeline

# when etl und frontend db are different, add schema copying
if dbs.db('mdwh-etl').database != dbs.db('mdwh-frontend').database \
        or dbs.db('mdwh-etl').host != dbs.db('mdwh-frontend').host:

    # run some of the files from etl_tools/initalize_utils in frontend db
    initialize_frontend_db_commands = [ExecuteSQL(
        sql_statement="DROP SCHEMA IF EXISTS util CASCADE; CREATE SCHEMA util;", db_alias='mdwh-frontend')]

    for file_name in ['schema_switching.sql', 'data_sets.sql', 'hll.sql', 'cstore_fdw.sql']:
        initialize_frontend_db_commands.append(
            ExecuteSQL(sql_file_name=str(
                my_pipeline.nodes['utils'].nodes['initialize_utils'].base_path() / file_name),
                db_alias='mdwh-frontend'))

    my_pipeline.nodes['utils'].add(
        Task(id='initialize_frontend_db',
             description='Adds some functions to the frontend db so that schema copying works',
             commands=initialize_frontend_db_commands))

    # Add schema copying for time schema
    add_schema_copying_to_pipeline(pipeline=my_pipeline.nodes['utils'].nodes['create_time_dimensions'],
                                   schema_name='time',
                                   source_db_alias='dwh-etl', target_db_alias='dwh-frontend')

    # Add schema copying to all root pipelines
    for pipeline in my_pipeline.nodes.values():
        if "Schema" in pipeline.labels:
            schema = pipeline.labels['Schema']
            add_schema_copying_to_pipeline(pipeline=pipeline, schema_name=schema + '_next',
                                           source_db_alias='dwh-etl', target_db_alias='dwh-frontend')
            pipeline.final_node.commands_after.append(
                ExecuteSQL(sql_statement=f"SELECT util.replace_schema('{schema}', '{schema}_next')",
                           db_alias='mdwh-frontend')
            )

Time dimensions

The file etl_tools/create_time_dimensions/init.py defines a pipeline that creates and updates time schema with the tables day and duration:

select * from time.day order by _date desc limit 10;
     day_id  |     day_name     | year_id | iso_year_id | quarter_id | quarter_name | month_id | month_name | week_id |  week_name   | day_of_week_id | day_of_week_name | day_of_month_id |   _date    
   ----------+------------------+---------+-------------+------------+--------------+----------+------------+---------+--------------+----------------+------------------+-----------------+------------
    20190815 | Thu, Aug 15 2019 |    2019 |        2019 |      20193 | 2019 Q3      |   201908 | 2019 Aug   |  201933 | 2019 - CW 33 |              4 | Thursday         |              15 | 2019-08-15
    20190814 | Wed, Aug 14 2019 |    2019 |        2019 |      20193 | 2019 Q3      |   201908 | 2019 Aug   |  201933 | 2019 - CW 33 |              3 | Wednesday        |              14 | 2019-08-14
    20190813 | Tue, Aug 13 2019 |    2019 |        2019 |      20193 | 2019 Q3      |   201908 | 2019 Aug   |  201933 | 2019 - CW 33 |              2 | Tuesday          |              13 | 2019-08-13
    20190812 | Mon, Aug 12 2019 |    2019 |        2019 |      20193 | 2019 Q3      |   201908 | 2019 Aug   |  201933 | 2019 - CW 33 |              1 | Monday           |              12 | 2019-08-12
    20190811 | Sun, Aug 11 2019 |    2019 |        2019 |      20193 | 2019 Q3      |   201908 | 2019 Aug   |  201932 | 2019 - CW 32 |              7 | Sunday           |              11 | 2019-08-11
    20190810 | Sat, Aug 10 2019 |    2019 |        2019 |      20193 | 2019 Q3      |   201908 | 2019 Aug   |  201932 | 2019 - CW 32 |              6 | Saturday         |              10 | 2019-08-10
    20190809 | Fri, Aug 09 2019 |    2019 |        2019 |      20193 | 2019 Q3      |   201908 | 2019 Aug   |  201932 | 2019 - CW 32 |              5 | Friday           |               9 | 2019-08-09
    20190808 | Thu, Aug 08 2019 |    2019 |        2019 |      20193 | 2019 Q3      |   201908 | 2019 Aug   |  201932 | 2019 - CW 32 |              4 | Thursday         |               8 | 2019-08-08
    20190807 | Wed, Aug 07 2019 |    2019 |        2019 |      20193 | 2019 Q3      |   201908 | 2019 Aug   |  201932 | 2019 - CW 32 |              3 | Wednesday        |               7 | 2019-08-07
    20190806 | Tue, Aug 06 2019 |    2019 |        2019 |      20193 | 2019 Q3      |   201908 | 2019 Aug   |  201932 | 2019 - CW 32 |              2 | Tuesday          |               6 | 2019-08-06
select * from time.duration where duration_id >= 0 order by duration_id limit 10;
 duration_id | days | days_name | weeks | weeks_name | four_weeks | four_weeks_name | months | months_name | sixth_years | sixth_years_name | half_years | half_years_name | years | years_name 
-------------+------+-----------+-------+------------+------------+-----------------+--------+-------------+-------------+------------------+------------+-----------------+-------+------------
           0 |    0 | 0 days    |     0 | 0-6 days   |          0 | 0-27 days       |      0 | 0-29 days   |           0 | 0-59 days        |          0 | 0-179 days      |     0 | 0-359 days
           1 |    1 | 1 days    |     0 | 0-6 days   |          0 | 0-27 days       |      0 | 0-29 days   |           0 | 0-59 days        |          0 | 0-179 days      |     0 | 0-359 days
           2 |    2 | 2 days    |     0 | 0-6 days   |          0 | 0-27 days       |      0 | 0-29 days   |           0 | 0-59 days        |          0 | 0-179 days      |     0 | 0-359 days
           3 |    3 | 3 days    |     0 | 0-6 days   |          0 | 0-27 days       |      0 | 0-29 days   |           0 | 0-59 days        |          0 | 0-179 days      |     0 | 0-359 days
           4 |    4 | 4 days    |     0 | 0-6 days   |          0 | 0-27 days       |      0 | 0-29 days   |           0 | 0-59 days        |          0 | 0-179 days      |     0 | 0-359 days
           5 |    5 | 5 days    |     0 | 0-6 days   |          0 | 0-27 days       |      0 | 0-29 days   |           0 | 0-59 days        |          0 | 0-179 days      |     0 | 0-359 days
           6 |    6 | 6 days    |     0 | 0-6 days   |          0 | 0-27 days       |      0 | 0-29 days   |           0 | 0-59 days        |          0 | 0-179 days      |     0 | 0-359 days
           7 |    7 | 7 days    |     1 | 7-13 days  |          0 | 0-27 days       |      0 | 0-29 days   |           0 | 0-59 days        |          0 | 0-179 days      |     0 | 0-359 days
           8 |    8 | 8 days    |     1 | 7-13 days  |          0 | 0-27 days       |      0 | 0-29 days   |           0 | 0-59 days        |          0 | 0-179 days      |     0 | 0-359 days
           9 |    9 | 9 days    |     1 | 7-13 days  |          0 | 0-27 days       |      0 | 0-29 days   |           0 | 0-59 days        |          0 | 0-179 days      |     0 | 0-359 days

Add the pipeline to your project with

from etl_tools import create_time_dimensions

my_pipeline.add(create_time_dimensions.pipeline)

Set min and max dates by overwriting the first_date_in_time_dimensions and last_date_in_time_dimensions in etl_tools/config.py.

Euro currency exchange rates

The file etl_tools/load_euro_exchange_rates/init.py contains a pipeline that loads (historic) Euro exchange rates from the European central bank.

Add to your pipeline with

from etl_tools import load_euro_exchange_rates

my_pipeline.add(load_euro_exchange_rates.euro_exchange_rates_pipeline('db-alias'))

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for mara-etl-tools, version 3.0.0
Filename, size File type Python version Upload date Hashes
Filename, size mara-etl-tools-3.0.0.tar.gz (11.3 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page