%global _empty_manifest_terminate_build 0 Name: python-dbt2looker Version: 0.11.0 Release: 1 Summary: Generate lookml view files from dbt models License: MIT URL: https://github.com/hubble-data/dbt2looker Source0: https://mirrors.aliyun.com/pypi/web/packages/9e/e9/30e36510fc149b5b92d44809e80f33fbdfebe2742ecd1b3ad839c4f7b48d/dbt2looker-0.11.0.tar.gz BuildArch: noarch Requires: python3-lkml Requires: python3-pydantic Requires: python3-PyYAML Requires: python3-typing-extensions Requires: python3-importlib-metadata %description # dbt2looker Use `dbt2looker` to generate Looker view files automatically from dbt models. Want a deeper integration between dbt and your BI tool? You should also checkout [Lightdash - the open source alternative to Looker](https://github.com/lightdash/lightdash) **Features** * **Column descriptions** synced to looker * **Dimension** for each column in dbt model * **Dimension groups** for datetime/timestamp/date columns * **Measures** defined through dbt column `metadata` [see below](#defining-measures) * Looker types * Warehouses: BigQuery, Snowflake, Redshift (postgres to come) [![demo](https://raw.githubusercontent.com/hubble-data/dbt2looker/main/docs/demo.gif)](https://asciinema.org/a/407407) ## Quickstart Run `dbt2looker` in the root of your dbt project *after compiling looker docs*. **Generate Looker view files for all models:** ```shell dbt docs generate dbt2looker ``` **Generate Looker view files for all models tagged `prod`** ```shell dbt2looker --tag prod ``` ## Install **Install from PyPi repository** Install from pypi into a fresh virtual environment. ``` # Create virtual env python3.7 -m venv dbt2looker-venv source dbt2looker-venv/bin/activate # Install pip install dbt2looker # Run dbt2looker ``` **Build from source** Requires [poetry](https://python-poetry.org/docs/) and python >=3.7 For development, it is recommended to use python 3.7: ``` # Ensure you're using 3.7 poetry env use 3.7 # alternative: poetry env use /usr/local/opt/python@3.7/bin/python3 # Install dependencies and main package poetry install # Run dbtlooker in poetry environment poetry run dbt2looker ``` ## Defining measures You can define looker measures in your dbt `schema.yml` files. For example: ```yaml models: - name: pages columns: - name: url description: "Page url" - name: event_id description: unique event id for page view meta: measures: page_views: type: count ``` %package -n python3-dbt2looker Summary: Generate lookml view files from dbt models Provides: python-dbt2looker BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-dbt2looker # dbt2looker Use `dbt2looker` to generate Looker view files automatically from dbt models. Want a deeper integration between dbt and your BI tool? You should also checkout [Lightdash - the open source alternative to Looker](https://github.com/lightdash/lightdash) **Features** * **Column descriptions** synced to looker * **Dimension** for each column in dbt model * **Dimension groups** for datetime/timestamp/date columns * **Measures** defined through dbt column `metadata` [see below](#defining-measures) * Looker types * Warehouses: BigQuery, Snowflake, Redshift (postgres to come) [![demo](https://raw.githubusercontent.com/hubble-data/dbt2looker/main/docs/demo.gif)](https://asciinema.org/a/407407) ## Quickstart Run `dbt2looker` in the root of your dbt project *after compiling looker docs*. **Generate Looker view files for all models:** ```shell dbt docs generate dbt2looker ``` **Generate Looker view files for all models tagged `prod`** ```shell dbt2looker --tag prod ``` ## Install **Install from PyPi repository** Install from pypi into a fresh virtual environment. ``` # Create virtual env python3.7 -m venv dbt2looker-venv source dbt2looker-venv/bin/activate # Install pip install dbt2looker # Run dbt2looker ``` **Build from source** Requires [poetry](https://python-poetry.org/docs/) and python >=3.7 For development, it is recommended to use python 3.7: ``` # Ensure you're using 3.7 poetry env use 3.7 # alternative: poetry env use /usr/local/opt/python@3.7/bin/python3 # Install dependencies and main package poetry install # Run dbtlooker in poetry environment poetry run dbt2looker ``` ## Defining measures You can define looker measures in your dbt `schema.yml` files. For example: ```yaml models: - name: pages columns: - name: url description: "Page url" - name: event_id description: unique event id for page view meta: measures: page_views: type: count ``` %package help Summary: Development documents and examples for dbt2looker Provides: python3-dbt2looker-doc %description help # dbt2looker Use `dbt2looker` to generate Looker view files automatically from dbt models. Want a deeper integration between dbt and your BI tool? You should also checkout [Lightdash - the open source alternative to Looker](https://github.com/lightdash/lightdash) **Features** * **Column descriptions** synced to looker * **Dimension** for each column in dbt model * **Dimension groups** for datetime/timestamp/date columns * **Measures** defined through dbt column `metadata` [see below](#defining-measures) * Looker types * Warehouses: BigQuery, Snowflake, Redshift (postgres to come) [![demo](https://raw.githubusercontent.com/hubble-data/dbt2looker/main/docs/demo.gif)](https://asciinema.org/a/407407) ## Quickstart Run `dbt2looker` in the root of your dbt project *after compiling looker docs*. **Generate Looker view files for all models:** ```shell dbt docs generate dbt2looker ``` **Generate Looker view files for all models tagged `prod`** ```shell dbt2looker --tag prod ``` ## Install **Install from PyPi repository** Install from pypi into a fresh virtual environment. ``` # Create virtual env python3.7 -m venv dbt2looker-venv source dbt2looker-venv/bin/activate # Install pip install dbt2looker # Run dbt2looker ``` **Build from source** Requires [poetry](https://python-poetry.org/docs/) and python >=3.7 For development, it is recommended to use python 3.7: ``` # Ensure you're using 3.7 poetry env use 3.7 # alternative: poetry env use /usr/local/opt/python@3.7/bin/python3 # Install dependencies and main package poetry install # Run dbtlooker in poetry environment poetry run dbt2looker ``` ## Defining measures You can define looker measures in your dbt `schema.yml` files. For example: ```yaml models: - name: pages columns: - name: url description: "Page url" - name: event_id description: unique event id for page view meta: measures: page_views: type: count ``` %prep %autosetup -n dbt2looker-0.11.0 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-dbt2looker -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Thu Jun 08 2023 Python_Bot - 0.11.0-1 - Package Spec generated