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ETL from third-party APIs into Snowflake




Mr. Plow

Mr. Plow is Poppin's ETL system to persist data from third-party APIs into a
Snowflake database for later business analysis.

We use Python to:

#. Call said APIs and store the data in AWS S3 ("Extract")
#. Issue Snowflake commands to import the data from AWS S3 ("Stage")
#. Issue Snowflake commands to transform the new data from its original
   unstructured form to the tabular form used for analysis ("Transform")
#. Issue Snowflake commands to load the new tabular data to our main store,
   eliminating any duplicates ("Load")

Mr. Plow can be run from the command line. In the future we will add support
for running its functions as AWS Lambda functions.

Why that ridiculous name?

Two reasons.

First, this is primarily a Snowflake client.



Currently, we support only Python 3.6. This is the highest current version.
There is no reason to suspect 3.7 and up will not work to run Mr. Plow.

Lower Python 3.x versions may work, but they are not tested. You are free to
try them. If you succeed running Mr. Plow in a 3.3, 3.4, or 3.5 environment,
please tell us on the Github issue tracker and we will add testing support.

First run ``python3 --version`` to ensure you have the required Python version.
It may also be installed as ``python3.6``.

We recommend you install using a virtual python environment to manage library
dependencies. `Virtualenv <>`_ is a great
tool for this.

Suggested usage:

#. `Install virtualenv <>`_
   if not already present
#. Create a virtual environment inside the clone: ``virtualenv -p python3.6 venv``

   * This creates a directory ``venv/``, which contains an entire python

#. Activate your virtual environment: ``source venv/bin/activate``

   * This adds the virtual environment's ``python`` and ``pip`` executables to your

#. Install dependencies: ``pip install mr-plow``

Now you should have the executable ``plow`` in your ``PATH``. Simply run
``plow`` at the command line to see available commands and an example workflow.

All commands accept the ``--help`` option and print a helpful usage guide.


Since we access both Snowflake and AWS S3, credentials are required. These may
be provided via a config file. It can be provided to the ``plow`` CLI tool
either via the ``-c`` option or by exporting the environment variable

The supported formats are JSON, YAML, and INI. INI files must have the
appropriate settings in the ``[mr-plow]`` section. Run ``plow generate-config``
to have a minimal example generated for you to fill in.

Mr. Plow fills in configuration it does not find by searching environment

To see more including a full list of config options, install Mr. Plow and run
``python -m pydoc plow.config.Config``.


Those commands that require Snowflake access draw the credentials from the
execution environment. Mr. Plow uses the following environment variables to
create a Snowflake connection. If both ``SNOWSQL_FOO`` and ``SNOWFLAKE_FOO``
are present as environment variables,  Mr. Plow uses ``SNOWFLAKE_FOO``.


  * visible in the URL you use to log in to Snowflake. For the author and his
    peers, this is "poppin".


  * Same as your login on the Snowflake website.


  * This is the password for that login. With the command line, this is
    optional as it can be provided as a console prompt.




  * E.g. "public". However please keep in mind that Mr. Plow creates its own
    schemas and never references tables without explicitly providing a schema.


.. warning::
    **Caution**: Choose carefully as many of these commands spin up a warehouse
    instance. You may incur charges.


We use `boto3 <>`_ to connect to AWS S3.
It has a rich system for specifying credentials, which can be used in its
entirety by omitting AWS settings from your config file. This is appropriate in
AWS Lambda, where credentials are taken care of in the background, or if you
are an `AWS CLI <>`_ user and have
existing configs. See `boto3 documentation
for more detail.

To specify config directly, you may either give the ``aws_access_key_id`` and
``aws_secret_access_key`` settings directly, or if you have an existing `AWS
CLI configuration
<>`_, you
may simply specify ``aws_profile``.

When Snowflake reads your API data from S3, it requires you to provide AWS
credentials with the appropriate S3 read permissions. Mr. Plow picks these up
as distinct config options: ``staging_aws_access_key_id`` and
``staging_aws_secret_access_key``. As with the direct S3 credentials,
``staging_aws_profile`` may also be provided.

Third-party API's

We currently provide integrations for Livechat and Snapfulfil.

The Livechat integration requires an API login, which is specified as config
options ``livechat_login`` and ``livechat_api_key``.

The Snapfulfil integration requires an API login (config ``snapfulfil_login`` and
``snapfulfil_password``) as well as an explicit designation of which Snap domain
you want to make requests to (config ``snapfulfil_api_domain``). Typically the
latter is either ```` or

Extending Mr. Plow

Mr. Plow is designed to be broadly useful and easily extensible. We currently
provide integrations for Livechat and Snapfulfil, and more will be added, but
you can easily add your own.

You must create your own implementation of ``plow.op.extract.Extractor`` to
define how to fetch data. In a pinch you can use a RestExtractor; to allow
for automatic fetching of subsequent pages, you'll have to subclass it and
implement ``postprocess_response()``. See documentation of
``plow.op.Extractor``; also see ``plow.vendors.*`` for examples.

You must furthermore create your own instances of ``plow.queries.Table`` to
specify how to translate data from the documents you fetch using the Extractor
into Snowflake DB tables. See documentation of ``plow.queries.Table``, and see
``plow.queries.livechat`` and ``plow.queries.snapfulfil`` for tested examples.

Finally, you must create a ``plow.cli.Source`` pointing to all of these.

If you write your own adapter, we'd love to include it. Please send a pull


If you have the following files in your Python project and ``mr-plow`` installed
with ``pip``::

    # mymodule/plow/
    from plow.op.extract import RestExtractor
    class Extractor(RestExtractor):


    # mymodule/plow/
    from plow.queries import Table
    class Table1(Table):
        select = "..."
        # etc...

    class Table2(Table):

    class Table3(Table):


    # mymodule/plow/
    from plow.cli import Source
    from mymodule.plow.extract import Extractor
    from mymodule.plow.tables import Table1, Table2, Table3

    extractor = Extractor()
    tables = { t for t in (Table1(), Table2(), Table3())}
    source = Source(extractor=extractor, tables=tables)

Then you can invoke the ``plow`` CLI tool, using the ``--source`` option to point
to your code::

    $ plow -c mr-plow.ini extract --source mymodule.plow.cli:source [options]...


Developer installation

To develop on Mr. Plow, clone this repository, set up and activate a virtualenv
(see Installation) in the new working copy, and run ``pip install -e .[dev]``.
This installs the ``plow`` executable as well as development dependencies like
Flake8 (the linter we use) and pytest.


Mr. Plow is tested using `pytest <>`_. If you
clone the source and install using ``pip install -e .[dev]``, it is installed
automatically along with several other test dependencies. Run ``pytest`` to run
the unit tests; add ``--cov-report=term-missing`` or ``--cov-report=html`` to
see detailed coverage information.

Testing is separated into two section, unit tests and integration tests.
Integration tests are disabled by default: specify ``pytest --integration`` to
run integration tests as well, or ``pytest --no-unit`` to disable unit tests and
run only integration tests.

The unit tests use mocking for all external functionality, including Snowflake,
S3, and third-party API's, and so may be run without an internet connection or
any of the service-specific configuration specified above. However, at this
time, with very few exceptions these tests do not verify any specific SQL
queries, nor almost any vendor-specific logic.

The integration tests run all Snowflake setup operations and a full run of ETL
operations through S3 and Snowflake, so you must do some setup in order to run
them. We mock access to the third party API, so that we can simulate the
processing of a constant dataset and verify the result with precision. To run
integration tests, you must supply a configuration file by exporting its path

.. warning::
    **Caution**: Since these run real Snowflake operations, you may incur charges by running
    these tests.

Git hooks

This project adheres to several standards including a style guide and unit
tests. To aid developers in complying, we include hooks that can be run upon
a commit. Install them as follows:

* Include whatever hooks you wish in your own .git/hooks/pre-commit::

    $ echo '#!/usr/bin/env bash' >> .git/hooks/pre-commit
    $ echo ' || exit $?' >> .git/hooks/pre-commit
    $ echo ' || exit $?' >> .git/hooks/pre-commit
    $ chmod u+x .git/hooks/pre-commit

* Add the appropriate options to your git config::

    $ git config flake8.strict true
    $ git config plow.unit.strict true

Now, the linter and the unit tests will run every time you commit and you will
be prompted to fix any deficiencies before committing. These checks can be
disabled temporarily using environment variables. To avoid a linting check::

    $ git commit
    Style errors found!
    $ FLAKE8_STRICT=false git commit
    [...] Success!

To skip unit tests::

    $ PLOW_UNIT_STRICT=false git commit



Did you know?

Socket installs a GitHub app to automatically flag issues on every pull request and report the health of your dependencies. Find out what is inside your node modules and prevent malicious activity before you update the dependencies.


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