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Merge pull request #1 from fishtown-analytics/dev/stephen-girard
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.bumpversion.cfg

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[bumpversion]
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current_version = 0.12.2a1
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current_version = 0.13.0a1
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parse = (?P<major>\d+)
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\.(?P<minor>\d+)
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\.(?P<patch>\d+)
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[bumpversion:part:num]
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first_value = 1
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[bumpversion:file:setup.py]
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[bumpversion:file:core/setup.py]
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[bumpversion:file:dbt/version.py]
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[bumpversion:file:core/dbt/version.py]
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[bumpversion:file:plugins/postgres/setup.py]
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[bumpversion:file:plugins/redshift/setup.py]
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[bumpversion:file:plugins/snowflake/setup.py]
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[bumpversion:file:plugins/bigquery/setup.py]
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.coveragerc

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[report]
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include =
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dbt/*
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core/dbt/*
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plugins/adapters/dbt/*

README.md

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## dbt dev/stephen-girard (0.13.0? - To be released)
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## Overview
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This release makes dbt and its adapters into a core-and-plugin architecture.
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### Breaking Changes
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- '{{this}}' is no longer respected in hooks [#1176](https://github.com/fishtown-analytics/dbt/pull/1176), implementing [#878](https://github.com/fishtown-analytics/dbt/issues/878)
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## dbt 0.12.1 - (November 15, 2018)
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### Overview

CONTRIBUTING.md renamed to core/CONTRIBUTING.md

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pyenv virtualenv 3.6.5 dbt36
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cd ~/git/dbt
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pyenv local dbt36
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pyenv activate
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```
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This makes a new virtualenv based on python 3.6.5 named `dbt36`, and tells pyenv that when you're in the `dbt` directory it should automatically use that virtualenv.
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### installation
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First, from the `dbt` directory, install dbt in 'editable' mode. There are a couple ways to do it, but I'm in the habit of `pip install -e .`, which tells pip to install the package in the current directory in "editable" mode. What's cool about this mode is any changes you make to the current dbt directory will be reflected immediately in your next `dbt` run.
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First make sure that you setup your `virtualenv` as described in section _Setting up your environment_.
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From the `dbt` directory, install dbt in 'editable' mode. There are a couple ways to do it, but I'm in the habit of `pip install -e .`, which tells pip to install the package in the current directory in "editable" mode. Since you are running `pip install` in virtualenv, this will not change your normal dbt installation, if you have one.
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What's cool about this mode is any changes you make to the current dbt directory will be reflected immediately in your next `dbt` run.
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### Profile
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So what does that mean? Well:
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- `2 models` refers to the contents of the `models` directory
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- `59 macros` are the builtin global macros defind by dbt itself
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- `59 macros` are the builtin global macros defined by dbt itself
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- `1 operations` is the catalog generation operation that runs by default
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- `1 seed files` refers to the seed data in `data/moby_dick.csv`
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core/README.md

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<p align="center">
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<img src="/etc/dbt-horizontal.png" alt="dbt logo"/>
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</p>
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<p align="center">
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<a href="https://codeclimate.com/github/fishtown-analytics/dbt">
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<img src="https://codeclimate.com/github/fishtown-analytics/dbt/badges/gpa.svg" alt="Code Climate"/>
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</a>
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<a href="https://circleci.com/gh/fishtown-analytics/dbt/tree/master">
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<img src="https://circleci.com/gh/fishtown-analytics/dbt/tree/master.svg?style=svg" alt="CircleCI" />
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</a>
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<a href="https://ci.appveyor.com/project/DrewBanin/dbt/branch/development">
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<img src="https://ci.appveyor.com/api/projects/status/v01rwd3q91jnwp9m/branch/development?svg=true" alt="AppVeyor" />
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</a>
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<a href="https://slack.getdbt.com">
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<img src="https://slack.getdbt.com/badge.svg" alt="Slack" />
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</a>
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</p>
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**[dbt](https://www.getdbt.com/)** (data build tool) enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.
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dbt is the T in ELT. Organize, cleanse, denormalize, filter, rename, and pre-aggregate the raw data in your warehouse so that it's ready for analysis.
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![dbt architecture](/etc/dbt-arch.png?raw=true)
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dbt can be used to [aggregate pageviews into sessions](https://github.com/fishtown-analytics/snowplow), calculate [ad spend ROI](https://github.com/fishtown-analytics/facebook-ads), or report on [email campaign performance](https://github.com/fishtown-analytics/mailchimp).
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## Understanding dbt
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Analysts using dbt can transform their data by simply writing select statements, while dbt handles turning these statements into tables and views in a data warehouse.
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These select statements, or "models", form a dbt project. Models frequently build on top of one another – dbt makes it easy to [manage relationships](https://docs.getdbt.com/reference#ref) between models, and [visualize these relationships](https://docs.getdbt.com/docs/documentation), as well as assure the quality of your transformations through [testing](https://docs.getdbt.com/docs/testing).
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![dbt dag](/etc/dbt-dag.png?raw=true)
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## Getting started
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- [Install dbt](https://docs.getdbt.com/docs/installation)
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- Read the [documentation](https://docs.getdbt.com/).
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- Productionize your dbt project with [Sinter](https://www.sinterdata.com)
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## Find out more
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- Check out the [Introduction to dbt](https://dbt.readme.io/docs/introduction).
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- Read the [dbt Viewpoint](https://dbt.readme.io/docs/viewpoint).
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## Join thousands of analysts in the dbt community
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- Join the [chat](http://slack.getdbt.com/) on Slack.
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- Find community posts on [dbt Discourse](https://discourse.getdbt.com).
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## Reporting bugs and contributing code
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- Want to report a bug or request a feature? Let us know on [Slack](http://slack.getdbt.com/), or open [an issue](https://github.com/fishtown-analytics/dbt/issues/new).
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- Want to help us build dbt? Check out the [Contributing Getting Started Guide](/CONTRIBUTING.md)
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## Code of Conduct
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Everyone interacting in the dbt project's codebases, issue trackers, chat rooms, and mailing lists is expected to follow the [PyPA Code of Conduct](https://www.pypa.io/en/latest/code-of-conduct/).

core/dbt/__init__.py

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__path__ = __import__('pkgutil').extend_path(__path__, __name__)

core/dbt/adapters/__init__.py

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__path__ = __import__('pkgutil').extend_path(__path__, __name__)

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