Skip to main content

SimpleETL - ETL Processing by Simple Specifications

Project description

SimpleETL er an ETL tool developed by FlexDanmark to easily handling processing of data from user-defined data sources and automatically generates a dimensionally modelled data warehouse.

SimpleETL is developed to work with the PostgreSQL DBMS backend with psycopg2 as database adapter.


  • Automatically generates data warehouse dimensional model (star schema)

  • Can track changes of facts

  • User-defined automatic fact table partitioning

  • Handle deleted facts

  • Ensures data quality by type and value checking

  • Provides a wide range of default data types and allows user to define their own


SimpleETL can be installed in multiple ways. The simples is to install from pypi:

$ pip install simpleetl


SimpleETL requires the psycopg2-binary and the pygrametl package for database PostgreSQL database connections and table handling.

Example usages

From the source repository multiple code examples can be found in the examples folder.

A simple example could be:

from simpleetl import FactTable, runETL, datatypes as dt

factobj = FactTable(schema="testschema", table="userdata",
                    # Updated to data will be processed. Can be set to False if only appending (will speed things up)
                    store_history=False,  # Create a seperate userdata_historic table for storing changes to facts.
                    # Adds an _updated attribute which keeps track of when data was last updated.
                    lookupatts=["userid"]  # List of attributes uniquely defining a fact

# Tells ETL to mark deleted rows from source with an _deleted timestamp attribute

factobj.add_column_mapping("userid", dt.bigint, "userid")
# Map userid from source data to database with same name

factobj.add_column_mapping("sys_username", dt.text, "username")
# Rename "sys_username" from source data to "username" in database

datafeeder = [{"userid": 42, "sys_username": "Jens"}, {"userid": 56, "sys_username": "Svend"}]
# datafeeder can be a generator or simple a iterable of dictionaries

stats = runETL(facttable=factobj, datafeeder=datafeeder,
               db_host="localhost", db_port="5432", db_name="test_db", db_user="dbuser", db_pass="dbpass")


Ove Andersen, Christian Thomsen, Kristian Torp: SimpleETL: ETL Processing by Simple Specifications. DOLAP 2018

Download files

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

Source Distribution

simpleetl-1.0.3.tar.gz (49.7 kB view hashes)

Uploaded source

Built Distribution

simpleetl-1.0.3-py3-none-any.whl (56.3 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page