From af572ef7f10c6c795a0d4882b2ee032e203f779f Mon Sep 17 00:00:00 2001
From: Pedro Castro <aspeddro@gmail.com>
Date: Tue, 25 Jun 2024 16:51:06 -0300
Subject: [PATCH] Initial (#1)

* initial

* add TableArch class and add tests

* fix type hint

* fix type hint

* add more methods

* env: fix typo

* remove link_directory_metadata.py

* update README.md

* update

* update

* add get_architecture_table_from_api

* add new scripts

* add more tests

* add verbose param to upload_columns

* add authors

* create_yaml_file strip name and description
---
 .github/workflows/ci.yaml                     |   41 +
 .gitignore                                    |  162 ++
 CONTRIBUTING.md                               |   51 +
 LICENSE                                       |   21 +
 README.md                                     |   75 +
 poetry.lock                                   | 1709 +++++++++++++++++
 pyproject.toml                                |   36 +
 scripts/lint.py                               |   11 +
 src/databasers_utils/__init__.py              |    9 +
 src/databasers_utils/constants.py             |   12 +
 .../copy_models_from_dev_to_prod.py           |   60 +
 src/databasers_utils/create_yaml_file.py      |  209 ++
 .../get_architecture_table_from_api.py        |  152 ++
 src/databasers_utils/table_architecture.py    |  104 +
 src/databasers_utils/upload_columns.py        |  351 ++++
 src/databasers_utils/utils.py                 |  108 ++
 tests/__init__.py                             |    0
 .../models/br_ibge_pib/br_ibge_pib__gini.sql  |   11 +
 tests/models/br_ibge_pib/br_ibge_pib__uf.sql  |   13 +
 tests/models/br_ibge_pib/schema.yml           |   87 +
 tests/test_get_architecture_table_from_api.py |    8 +
 tests/test_table_architecture.py              |   63 +
 22 files changed, 3293 insertions(+)
 create mode 100644 .github/workflows/ci.yaml
 create mode 100644 .gitignore
 create mode 100644 CONTRIBUTING.md
 create mode 100644 LICENSE
 create mode 100644 poetry.lock
 create mode 100644 pyproject.toml
 create mode 100644 scripts/lint.py
 create mode 100644 src/databasers_utils/__init__.py
 create mode 100644 src/databasers_utils/constants.py
 create mode 100644 src/databasers_utils/copy_models_from_dev_to_prod.py
 create mode 100644 src/databasers_utils/create_yaml_file.py
 create mode 100644 src/databasers_utils/get_architecture_table_from_api.py
 create mode 100644 src/databasers_utils/table_architecture.py
 create mode 100644 src/databasers_utils/upload_columns.py
 create mode 100644 src/databasers_utils/utils.py
 create mode 100644 tests/__init__.py
 create mode 100644 tests/models/br_ibge_pib/br_ibge_pib__gini.sql
 create mode 100644 tests/models/br_ibge_pib/br_ibge_pib__uf.sql
 create mode 100644 tests/models/br_ibge_pib/schema.yml
 create mode 100644 tests/test_get_architecture_table_from_api.py
 create mode 100644 tests/test_table_architecture.py

diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
new file mode 100644
index 0000000..6be485a
--- /dev/null
+++ b/.github/workflows/ci.yaml
@@ -0,0 +1,41 @@
+---
+name: CI
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  test:
+    name: main
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Setup poetry
+        run: pipx install poetry
+
+      - name: Setup python
+        uses: actions/setup-python@v4
+        with:
+          cache: poetry
+          architecture: x64
+          python-version: '3.9'
+
+      - name: Poetry check
+        run: poetry check
+
+      - name: Install requirements
+        run: poetry install
+
+      - name: Test
+        run: poetry run pytest
+
+      - name: Ruff check
+        run: poetry run ruff check .
+
+      - name: Ruff format check
+        run: poetry run ruff format --check --diff
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..82f9275
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,162 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000..545a2f7
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,51 @@
+# Contribuindo
+
+## Setup
+
+Requisitos:
+
+- [pyenv](https://github.com/pyenv/pyenv): Recomendado
+- [poetry](https://python-poetry.org/)
+
+1. Clone o repositório e entre no diretório clonado
+
+```sh
+git clone https://github.com/basedosdados/databasers-utils
+```
+
+1. Ative a versão 3.9 do Python
+
+```sh
+pyenv shell 3.9
+```
+
+2. Crie a env ou ativa se já existir
+
+```sh
+poetry shell
+```
+
+3. Instale as dependências
+
+```sh
+poetry install --with dev --no-root
+```
+
+## Code Style
+
+Após finalizar as alterações no código execute:
+
+```sh
+poetry run lint
+```
+
+> [!IMPORTANT]
+> O command vai executar o formatador `ruff format .` e o linter `ruff check --fix .`. Algumas regras do linter requer intervenção manual para ser resolvido
+
+## Testes
+
+Para rodar os testes:
+
+```sh
+poetry run pytest
+```
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..31124c7
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Base dos Dados
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
index 9371f11..62ffb33 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,76 @@
 # databasers-utils
+
+Pacote para uso da equipe de dados na Base dos Dados.
+
+- [CONTRIBUTING.md](./CONTRIBUTING.md)
+
+## Instalação
+
+```sh
+poetry add git+https://github.com/basedosdados/databasers-utils.git
+```
+
+## Setup
+
+### Credenciais
+
+Para fazer o upload de colunas usando a arquitetura você deve configurar suas
+credenciais.
+
+#### Linux/WSL
+
+Adicione no seu `.bashrc` ou `.zshrc`
+
+```sh
+export BD_DJANGO_EMAIL="seuemail@basedosdados.org"
+export BD_DJANGO_PASSWORD="password"
+```
+
+#### Windows
+
+- Em Pesquisar, procure e selecione: Sistema (Painel de Controle)
+- Clique no link Configurações avançadas do sistema.
+- Clique em Variáveis de Ambiente. 
+- Na seção 'Variáveis do Sistema' clique em 'Novo' e insira `BD_DJANGO_EMAIL` como nome da variável e o seu email como valor. Faça o mesmo para `BD_DJANGO_PASSWORD`.
+- Clique em OK. Feche todas as janelas restantes clicando em OK.
+
+### Uso
+
+```python
+from databasers_utils import (
+    TableArchitecture,
+    copy_models_from_dev_to_prod,
+    get_architecture_table_from_api,
+)
+
+arch = TableArchitecture(
+    dataset_id="br_ibge_pib",
+    tables={
+        "uf": "https://docs.google.com/spreadsheets/d/12F5NzhOYlN_bi9flLBEdXDWpa5iVakSP4EKm9UoyWuo/edit?usp=drive_link",
+        "gini": "https://docs.google.com/spreadsheets/d/1K1svie4Gyqe6NnRjBgJbapU5sTsLqXWTQUmTRVIRwQc/edit?usp=drive_link",
+    },
+)
+
+# Retorna um dict[str, pd.DataFrame], cada chave o id da tabela e o dataframe
+# da arquitetura
+arch.tables()
+
+# Cria o yaml file
+arch.create_yaml_file()
+
+# Cria os arquivos sql
+arch.create_sql_files()
+
+# Atualiza o dbt_project.yml
+arch.update_dbt_project()
+
+# Faz o upload das colunas para o DJango
+arch.upload_columns()
+
+# Copia os modelos em dev para prod
+copy_models_from_dev_to_prod(["br_ibge_ppm", "br_ibge_pam"])
+
+# Retorna um DataFrame da arquitetura obtida na API
+# Util para gerar arquitetura quando ela não está no Drive
+get_architecture_table_from_api("br_ms_sinasc", "microdados")
+```
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..c12d15b
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,1709 @@
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+
+[[package]]
+name = "anyio"
+version = "4.4.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "anyio-4.4.0-py3-none-any.whl", hash = "sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7"},
+    {file = "anyio-4.4.0.tar.gz", hash = "sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94"},
+]
+
+[package.dependencies]
+exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
+idna = ">=2.8"
+sniffio = ">=1.1"
+typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+
+[package.extras]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.23)"]
+
+[[package]]
+name = "backoff"
+version = "2.2.1"
+description = "Function decoration for backoff and retry"
+optional = false
+python-versions = ">=3.7,<4.0"
+files = [
+    {file = "backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8"},
+    {file = "backoff-2.2.1.tar.gz", hash = "sha256:03f829f5bb1923180821643f8753b0502c3b682293992485b0eef2807afa5cba"},
+]
+
+[[package]]
+name = "basedosdados"
+version = "2.0.0b15"
+description = "Organizar e facilitar o acesso a dados brasileiros através de tabelas públicas no BigQuery."
+optional = false
+python-versions = ">=3.8.1,<4"
+files = [
+    {file = "basedosdados-2.0.0b15-py3-none-any.whl", hash = "sha256:2832cc2e93e6207ef4600516d90dc0858794e56d19674cc8493d77c2ae0a7e38"},
+    {file = "basedosdados-2.0.0b15.tar.gz", hash = "sha256:4fb669a087e8d12e30978efaca375d6760324eb3da6635e7ab35ed761cfdaef3"},
+]
+
+[package.dependencies]
+click = {version = ">=8.1,<9", optional = true, markers = "extra == \"all\" or extra == \"cli\""}
+google-api-python-client = ">=2.86,<3.0"
+google-cloud-bigquery = ">=3.10,<4.0"
+google-cloud-bigquery-connection = ">=1.12,<2.0"
+google-cloud-bigquery-storage = ">=2.19,<3.0"
+google-cloud-storage = ">=2.9,<3.0"
+gql = {version = ">=3.4,<4.0", optional = true, markers = "extra == \"all\" or extra == \"upload\""}
+loguru = "0.7.0"
+pandas = ">=2.0,<3.0"
+pandas-gbq = ">=0.19,<0.20"
+pandavro = {version = ">=1.7,<2.0", optional = true, markers = "extra == \"all\" or extra == \"avro\""}
+pydata-google-auth = ">=1.8,<2.0"
+requests-toolbelt = {version = ">=1,<2", optional = true, markers = "extra == \"all\" or extra == \"upload\""}
+tomlkit = ">=0.11,<0.12"
+tqdm = ">=4,<5"
+
+[package.extras]
+all = ["click (>=8.1,<9)", "gql (>=3.4,<4.0)", "pandavro (>=1.7,<2.0)", "requests-toolbelt (>=1,<2)"]
+avro = ["pandavro (>=1.7,<2.0)"]
+cli = ["click (>=8.1,<9)"]
+upload = ["gql (>=3.4,<4.0)", "requests-toolbelt (>=1,<2)"]
+
+[[package]]
+name = "cachetools"
+version = "5.3.3"
+description = "Extensible memoizing collections and decorators"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "cachetools-5.3.3-py3-none-any.whl", hash = "sha256:0abad1021d3f8325b2fc1d2e9c8b9c9d57b04c3932657a72465447332c24d945"},
+    {file = "cachetools-5.3.3.tar.gz", hash = "sha256:ba29e2dfa0b8b556606f097407ed1aa62080ee108ab0dc5ec9d6a723a007d105"},
+]
+
+[[package]]
+name = "certifi"
+version = "2024.6.2"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "certifi-2024.6.2-py3-none-any.whl", hash = "sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56"},
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+
+[[package]]
+name = "charset-normalizer"
+version = "3.3.2"
+description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
+optional = false
+python-versions = ">=3.7.0"
+files = [
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+    {file = "yarl-1.9.4-py3-none-any.whl", hash = "sha256:928cecb0ef9d5a7946eb6ff58417ad2fe9375762382f1bf5c55e61645f2c43ad"},
+    {file = "yarl-1.9.4.tar.gz", hash = "sha256:566db86717cf8080b99b58b083b773a908ae40f06681e87e589a976faf8246bf"},
+]
+
+[package.dependencies]
+idna = ">=2.0"
+multidict = ">=4.0"
+
+[metadata]
+lock-version = "2.0"
+python-versions = "~3.9"
+content-hash = "4e3333bac3bf5ec8c49e43e6ebd097e01586053924a3607a27de36bde0791e65"
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..d8c0b41
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,36 @@
+[tool.poetry]
+name = "databasers-utils"
+version = "0.1.0"
+description = ""
+authors = [
+    "Laura Amaral <laura.amaral@basedosdados.org>",
+    "Pedro Castro <pedro.castro@basedosdados.org>",
+    "Arthur Gusmão <arthur.gusmao@basedosdados.org>"
+]
+readme = "README.md"
+packages = [
+    { include = "databasers_utils", from = "src" }
+]
+
+[tool.poetry.scripts]
+lint = "scripts.lint:main"
+
+[tool.poetry.dependencies]
+python = "~3.9"
+basedosdados = {version = "2.0.0b15", extras = ["all"]}
+pandas = "^2.2.2"
+numpy = "^1.26.4"
+requests = "^2.32.3"
+ruamel-yaml = "^0.18.6"
+
+
+[tool.poetry.group.dev.dependencies]
+pytest = "^8.2.2"
+ruff = "^0.4.8"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.ruff]
+line-length = 80
diff --git a/scripts/lint.py b/scripts/lint.py
new file mode 100644
index 0000000..f0d592f
--- /dev/null
+++ b/scripts/lint.py
@@ -0,0 +1,11 @@
+from subprocess import run
+
+
+def main():
+    """Lint all python files in the project"""
+    ruff_check = run(["ruff", "check", "--fix", "."])
+    ruff_format = run(["ruff", "format", "."])
+    exit_code = (
+        0 if ruff_check.returncode == 0 and ruff_format.returncode == 0 else 1
+    )
+    exit(exit_code)
diff --git a/src/databasers_utils/__init__.py b/src/databasers_utils/__init__.py
new file mode 100644
index 0000000..15a2d62
--- /dev/null
+++ b/src/databasers_utils/__init__.py
@@ -0,0 +1,9 @@
+from .copy_models_from_dev_to_prod import copy_models_from_dev_to_prod
+from .get_architecture_table_from_api import get_architecture_table_from_api
+from .table_architecture import TableArchitecture
+
+__all__ = [
+    "copy_models_from_dev_to_prod",
+    "get_architecture_table_from_api",
+    "TableArchitecture",
+]
diff --git a/src/databasers_utils/constants.py b/src/databasers_utils/constants.py
new file mode 100644
index 0000000..a40a43d
--- /dev/null
+++ b/src/databasers_utils/constants.py
@@ -0,0 +1,12 @@
+from enum import Enum
+
+
+class constants(Enum):
+    API_URL = {
+        "staging": "https://staging.backend.basedosdados.org/api/v1/graphql",
+        "prod": "https://backend.basedosdados.org/api/v1/graphql",
+    }
+    ENV = {
+        "email": "BD_DJANGO_EMAIL",
+        "password": "BD_DJANGO_PASSWORD",
+    }
diff --git a/src/databasers_utils/copy_models_from_dev_to_prod.py b/src/databasers_utils/copy_models_from_dev_to_prod.py
new file mode 100644
index 0000000..d109023
--- /dev/null
+++ b/src/databasers_utils/copy_models_from_dev_to_prod.py
@@ -0,0 +1,60 @@
+import os
+from typing import Optional
+from distutils.dir_util import copy_tree
+from .utils import update_dbt_project
+
+
+def change_origin_from_dev_to_staging(
+    file: str, prod_models_dataset_dir: str
+) -> None:
+    sql_file = f"{prod_models_dataset_dir}/{file}"
+
+    with open(sql_file, "r") as io:
+        sql_content = io.read()
+
+    new_sql_content = sql_content.replace(
+        "basedosdados-dev.", "basedosdados-staging."
+    )
+
+    with open(sql_file, "w") as io:
+        io.write(new_sql_content)
+
+
+def search_model_directory_recursive(directory: str) -> Optional[str]:
+    # Check if 'model' is in the current directory
+    ls_dir = os.listdir(directory)
+    if "models" in ls_dir:
+        return os.path.join(directory, "models")
+
+    if "queries-basedosdados-dev" in ls_dir:
+        return os.path.join(directory, "queries-basedosdados-dev", "models")
+
+    # Get the parent directory
+    parent_directory = os.path.dirname(directory)
+
+    # If we've reached the root directory without finding 'model', return None
+    if directory == parent_directory:
+        return None
+
+    # Otherwise, continue searching recursively in parent directories
+    return search_model_directory_recursive(parent_directory)
+
+
+def copy_models_from_dev_to_prod(datasets: list[str]) -> None:
+    dev_models_path = search_model_directory_recursive(os.getcwd())
+
+    if dev_models_path is None:
+        raise Exception("Failed to find model directory")
+
+    prod_models_dir = dev_models_path.replace(
+        "queries-basedosdados-dev", "queries-basedosdados"
+    )
+    for dataset_id in datasets:
+        prod_models_dataset_dir = f"{prod_models_dir}/{dataset_id}"
+        copy_tree(f"{dev_models_path}/{dataset_id}", prod_models_dataset_dir)
+        update_dbt_project(dataset_id, prod_models_dir)
+        [
+            change_origin_from_dev_to_staging(file, prod_models_dataset_dir)
+            for file in os.listdir(prod_models_dataset_dir)
+            if file.endswith(".sql")
+        ]
diff --git a/src/databasers_utils/create_yaml_file.py b/src/databasers_utils/create_yaml_file.py
new file mode 100644
index 0000000..90507cd
--- /dev/null
+++ b/src/databasers_utils/create_yaml_file.py
@@ -0,0 +1,209 @@
+import os
+import re
+import pandas as pd
+import ruamel.yaml as yaml
+from typing import Optional, Union
+from .utils import get_model_directory, read_architecture_table
+
+
+def extract_column_parts(input_string: str) -> str:
+    pattern_1 = re.compile(r"(\w+)\.(\w+):(\w+)")
+    pattern_2 = re.compile(r"\w+\.(\w+)\.(\w+):(\w+)")
+
+    if pattern_1.match(input_string):
+        return pattern_1.findall(input_string)[0]
+    elif pattern_2.match(input_string):
+        return pattern_2.findall(input_string)[0]
+    else:
+        raise ValueError(
+            f"Invalid input format on `{input_string}`. Expected format: 'dataset.table:column'"
+        )
+
+
+def extract_relationship_info(input_string: str) -> Optional[tuple[str, str]]:
+    try:
+        dataset, table, column = extract_column_parts(input_string)
+
+        if column == table:
+            column = f"{column}.{column}"
+
+        field = column
+
+        table_path = f"ref('{dataset}__{table}')"
+
+        return table_path, field
+
+    except Exception as e:
+        print(f"An unexpected error occurred: {e}")
+        return None
+
+
+def create_relationships(directory_column: str) -> list[dict[str, str]]:
+    relationship_table, relationship_field = extract_relationship_info(
+        directory_column
+    )  # type: ignore
+    yaml_relationship = yaml.comments.CommentedMap()
+    yaml_relationship["relationships"] = {
+        "to": relationship_table,
+        "field": relationship_field,
+    }
+    return [yaml_relationship]
+
+
+def create_unique_combination(unique_keys: list[str]) -> list:
+    combination = yaml.comments.CommentedMap()
+    combination["dbt_utils.unique_combination_of_columns"] = {
+        "combination_of_columns": unique_keys
+    }
+    return [combination]
+
+
+def create_not_null_proportion(at_least: float) -> list:
+    not_null = yaml.comments.CommentedMap()
+    not_null["not_null_proportion_multiple_columns"] = {
+        "at_least": at_least,
+    }
+    return [not_null]
+
+
+def create_dict_coverage(
+    dataset_id: str, list_covered_by_dict_columns: list[str]
+) -> list:
+    dict_coverage = yaml.comments.CommentedMap()
+    dict_coverage["custom_dictionaries"] = {
+        "columns_covered_by_dictionary": list_covered_by_dict_columns,
+        "dictionary_model": f"ref('{dataset_id}__dicionario')",
+    }
+    return [dict_coverage]
+
+
+def create_unique() -> list[str]:
+    return ["unique", "not_null"]
+
+
+def create_yaml_file(
+    arch_url: Union[str, list[str]],
+    table_id: Union[str, list[str]],
+    dataset_id: str,
+    table_description: str = "Insert table description here",
+    at_least: float = 0.95,
+    unique_keys: list[str] = ["insert unique keys here"],
+    mkdir: bool = True,
+    dir: str = os.getcwd(),
+) -> str:
+    """
+    Creates dbt models and schema.yaml files based on the architecture table, including data quality tests automatically.
+
+    Args:
+        arch_url (str or list): The URL(s) or file path(s) of the input file(s) containing the data.
+        table_id (str or list): The table ID(s) or name(s) to use as the YAML model name(s).
+        dataset_id (str): The ID or name of the dataset to be used in the dbt models.
+        at_least (float): The proportion of non-null values accepted in the columns.
+        unique_keys (list, optional): A list of column names for which the 'dbt_utils.unique_combination_of_columns' test should be applied.
+                                      Defaults to ["insert unique keys here"].
+        mkdir (bool, optional): If True, creates a directory for the new model(s). Defaults to True.
+        preprocessed_staging_column_names (bool, optional):  If True, builds SQL file renaming from 'original_name' to 'name' using the architecture file. Defaults to True.
+
+    Raises:
+        TypeError: If the table_id is not a string or a list.
+        ValueError: If the number of URLs or file paths does not match the number of table IDs.
+
+    Notes:
+        The function generates dbt models in YAML format based on the input data and saves them to the specified output file.
+        The generated YAML file includes information about the dataset, model names, descriptions, and column details.
+
+    Example:
+        ```python
+        create_yaml_file(arch_url='input_data.csv', table_id='example_table', dataset_id='example_dataset')
+        ```
+
+    """
+    if mkdir:
+        models_path = get_model_directory(dir)
+        output_path = f"{models_path}/{dataset_id}"
+        os.makedirs(output_path, exist_ok=True)
+    else:
+        print(
+            f"Directory for the new model has not been created, saving files in {os.getcwd()}"
+        )
+        output_path = os.getcwd()
+
+    schema_path = f"{output_path}/schema.yml"
+
+    yaml_obj = yaml.YAML(typ="rt")
+    yaml_obj.indent(mapping=4, sequence=4, offset=2)
+
+    if os.path.exists(schema_path):
+        with open(schema_path, "r") as file:
+            data = yaml_obj.load(file)
+    else:
+        data = yaml.comments.CommentedMap()
+        data["version"] = 2
+        data.yaml_set_comment_before_after_key("models", before="\n\n")
+        data["models"] = []
+
+    exclude = ["(excluded)", "(erased)", "(deleted)", "(excluido)"]
+
+    if isinstance(table_id, str):
+        table_id = [table_id]
+        arch_url = [arch_url]  # type: ignore
+
+    # If table_id is a list, assume multiple input files
+    if not isinstance(arch_url, list) or len(arch_url) != len(table_id):
+        raise ValueError(
+            "The number of URLs or file paths must match the number of table IDs."
+        )
+
+    for url, id in zip(arch_url, table_id):
+        unique_keys_copy = unique_keys.copy()
+        architecture_df = read_architecture_table(url)
+        architecture_df.dropna(subset=["bigquery_type"], inplace=True)
+        architecture_df: pd.DataFrame = architecture_df[  # type: ignore
+            ~architecture_df["bigquery_type"].apply(
+                lambda x: any(word in x.lower() for word in exclude)
+            )
+        ]
+
+        table = yaml.comments.CommentedMap()
+        table["name"] = f"{dataset_id}__{id}"
+
+        # If model is already in the schema.yaml, delete old model from schema and create a new one
+        for model in data["models"]:
+            if id == model["name"] or table["name"] == model["name"]:
+                data["models"].remove(model)
+                break
+
+        table["description"] = table_description
+        table["tests"] = create_unique_combination(unique_keys_copy)
+        table["tests"] += create_not_null_proportion(at_least)
+
+        covered_by_dict_columns = (
+            architecture_df["covered_by_dictionary"] == "yes"
+        )
+        if covered_by_dict_columns.sum():
+            list_covered_by_dict_columns = architecture_df[
+                covered_by_dict_columns
+            ]["name"].tolist()
+            table["tests"] += create_dict_coverage(
+                dataset_id, list_covered_by_dict_columns
+            )
+
+        table["columns"] = []
+
+        for _, row in architecture_df.iterrows():
+            column = yaml.comments.CommentedMap()
+            column["name"] = row["name"].strip()
+            column["description"] = row["description"].strip()
+            directory_column = row["directory_column"].strip()
+            if len(directory_column) != 0:
+                tests = []
+                tests = create_relationships(directory_column)
+                column["tests"] = tests
+            table["columns"].append(column)
+
+        data["models"].append(table)
+
+    with open(schema_path, "w") as file:
+        yaml_obj.dump(data, file)
+
+    return schema_path
diff --git a/src/databasers_utils/get_architecture_table_from_api.py b/src/databasers_utils/get_architecture_table_from_api.py
new file mode 100644
index 0000000..0c2fe03
--- /dev/null
+++ b/src/databasers_utils/get_architecture_table_from_api.py
@@ -0,0 +1,152 @@
+import pandas as pd
+from basedosdados import backend as b
+from typing import Optional
+from .constants import constants
+
+
+def get_columns_info_from_api(
+    table_name: str, dataset_name: str, url_api: str
+) -> pd.DataFrame:
+    backend = b.Backend(graphql_url=url_api)
+
+    id = backend._get_table_id_from_name(
+        gcp_dataset_id=dataset_name, gcp_table_id=table_name
+    )
+
+    variables = {"table_Id": id}
+
+    query = """query($table_Id: ID){
+    allColumn(table_Id: $table_Id){
+      edges{
+        node{
+          name
+          order
+          bigqueryType{
+            name
+          }
+          description
+          coverages{
+            edges{
+              node{
+                datetimeRanges{
+                  edges{
+                    node{
+                      startYear
+                      endYear
+                    }
+                  }
+                }
+              }
+            }
+          }
+          coveredByDictionary
+          directoryPrimaryKey{
+            cloudTables{
+              edges{
+                node{
+                  gcpDatasetId
+                  gcpTableId
+                }
+              }
+            }
+            name
+          }
+          measurementUnit
+          containsSensitiveData
+          observations
+        }
+      }
+    }
+  }"""
+
+    query_data = backend._execute_query(query=query, variables=variables)  # type: ignore
+
+    query_data = backend._simplify_graphql_response(query_data)["allColumn"]
+
+    df = pd.json_normalize(query_data, max_level=3)
+    df.index = df.order
+    df.sort_index(inplace=True)
+    return df
+
+
+def format_temporal_coverage_column(df: pd.DataFrame) -> pd.DataFrame:
+    try:
+        start_year = (
+            df["coverages"]
+            .map(lambda x: x[0]["datetimeRanges"][0]["startYear"])
+            .astype("Int64")
+        )
+        end_year = (
+            df["coverages"]
+            .map(lambda x: x[0]["datetimeRanges"][0]["endYear"])
+            .astype("Int64")
+        )
+        df["temporal_coverage"] = (
+            start_year.astype(str) + "(1)" + end_year.astype(str)
+        )
+        df["temporal_coverage"] = df["temporal_coverage"].str.replace(
+            "<NA>", ""
+        )
+    except Exception:
+        df["temporal_coverage"] = "(1)"
+
+    return df["temporal_coverage"]  # type: ignore
+
+
+def boolean_to_yes_no() -> dict[bool, str]:
+    return {True: "yes", False: "no"}
+
+
+def get_directory_column(row) -> Optional[str]:
+    if "directoryPrimaryKey.cloudTables" in row.index:
+        if isinstance(row["directoryPrimaryKey.cloudTables"], list):
+            directory_dataset = row["directoryPrimaryKey.cloudTables"][0][
+                "gcpDatasetId"
+            ]
+            directory_table = row["directoryPrimaryKey.cloudTables"][0][
+                "gcpTableId"
+            ]
+            directory_column_name = row["directoryPrimaryKey.name"]
+            return (
+                directory_dataset
+                + "."
+                + directory_table
+                + ":"
+                + directory_column_name
+            )
+    return None
+
+
+def get_architecture_table_from_api(
+    dataset_id: str,
+    table_id: str,
+    url_api: str = constants.API_URL.value["prod"],
+) -> pd.DataFrame:
+    df = get_columns_info_from_api(table_id, dataset_id, url_api)
+
+    df["bigquery_type"] = df["bigqueryType.name"].str.lower()
+    df["temporal_coverage"] = format_temporal_coverage_column(df)
+    df["covered_by_dictionary"] = df["coveredByDictionary"].map(
+        boolean_to_yes_no()  # type: ignore
+    )
+    df["has_sensitive_data"] = df["containsSensitiveData"].map(
+        boolean_to_yes_no()  # type: ignore
+    )
+    df["directory_column"] = df.apply(
+        lambda row: get_directory_column(row), axis=1
+    )
+    df["measurement_unit"] = df["measurementUnit"]
+
+    columns = [
+        "name",
+        "bigquery_type",
+        "description",
+        "temporal_coverage",
+        "covered_by_dictionary",
+        "directory_column",
+        "measurement_unit",
+        "has_sensitive_data",
+        "observations",
+    ]
+
+    return df[columns]  # type: ignore
diff --git a/src/databasers_utils/table_architecture.py b/src/databasers_utils/table_architecture.py
new file mode 100644
index 0000000..3cc6764
--- /dev/null
+++ b/src/databasers_utils/table_architecture.py
@@ -0,0 +1,104 @@
+import pandas as pd
+import os
+from basedosdados import Backend
+from .utils import (
+    read_architecture_table,
+    get_model_directory,
+    update_dbt_project,
+)
+from .create_yaml_file import create_yaml_file
+from .upload_columns import upload_columns_from_architecture
+from .constants import constants
+
+
+class TableArchitecture:
+    def __init__(self, dataset_id: str, tables: dict[str, str]):
+        self.dataset_id = dataset_id
+        self.__tables = tables
+
+    def __str__(self) -> str:
+        return f"TableArchitecture({self.dataset_id}, {str(self.__tables)})"
+
+    def tables(self) -> dict[str, pd.DataFrame]:
+        return {k: read_architecture_table(v) for k, v in self.__tables.items()}
+
+    def create_yaml_file(
+        self,
+        table_description: str = "Insert table description here",
+        at_least: float = 0.95,
+        unique_keys: list[str] = ["insert unique keys here"],
+        mkdir: bool = True,
+        dir: str = os.getcwd(),
+    ) -> None:
+        path = create_yaml_file(
+            arch_url=list(self.__tables.values()),
+            table_id=list(self.__tables.keys()),
+            dataset_id=self.dataset_id,
+            table_description=table_description,
+            at_least=at_least,
+            unique_keys=unique_keys,
+            mkdir=mkdir,
+            dir=dir,
+        )
+        print(f"Files successfully created for {self.dataset_id} at {path}")
+        return None
+
+    def create_sql_files(
+        self,
+        preprocessed_staging_column_names: bool = True,
+        dir: str = os.getcwd(),
+    ) -> None:
+        models_dir = get_model_directory(dir)
+
+        output_path = f"{models_dir}/{self.dataset_id}"
+
+        for table_id, url in self.__tables.items():
+            architecture_df = read_architecture_table(url)
+
+            if preprocessed_staging_column_names:
+                architecture_df["original_name"] = architecture_df["name"]
+
+            header = f'{{{{ config(alias="{table_id}", schema="{self.dataset_id}") }}}}'
+
+            with open(
+                f"{output_path}/{self.dataset_id}__{table_id}.sql", "w"
+            ) as file:
+                file.write(header + "\n")
+                file.write("select\n")
+
+                for _, column in architecture_df.iterrows():
+                    original_name = column["original_name"]
+                    column_name = column["name"]
+                    bq_type = column["bigquery_type"].lower()  # type: ignore
+
+                    sql_line = f"    safe_cast({original_name} as {bq_type}) {column_name},\n"
+                    file.write(sql_line)
+
+                sql_last_line = f"from `basedosdados-dev.{self.dataset_id}_staging.{table_id}` as t\n"
+                file.write(sql_last_line)
+
+        print("SQL files created!")
+        return None
+
+    def update_dbt_project(self) -> None:
+        return update_dbt_project(self.dataset_id, dir=os.getcwd())
+
+    def upload_columns(
+        self,
+        if_column_exists: str = "pass",
+        replace_all_schema: bool = True,
+        verbose: bool = False,
+    ) -> None:
+        backend = Backend(graphql_url=constants.API_URL.value["prod"])
+        for table_id, url in self.__tables.items():
+            upload_columns_from_architecture(
+                dataset_id=self.dataset_id,
+                table_id=table_id,
+                url_architecture=url,
+                backend=backend,
+                if_column_exists=if_column_exists,
+                replace_all_schema=replace_all_schema,
+                verbose=verbose,
+            )
+
+        return None
diff --git a/src/databasers_utils/upload_columns.py b/src/databasers_utils/upload_columns.py
new file mode 100644
index 0000000..98e7e20
--- /dev/null
+++ b/src/databasers_utils/upload_columns.py
@@ -0,0 +1,351 @@
+import json
+import pandas as pd
+from basedosdados import backend as b
+from .utils import get_headers, read_architecture_table
+from typing import Optional
+
+
+def get_directory_column_id(
+    directory_column_name: str,
+    directory_table_name: str,
+    backend: b.Backend,
+    verbose: bool = False,
+) -> str:
+    """
+    Get the directory id from that column
+    """
+    query = """ query($column_name: String)
+        {allColumn(name: $column_name) {
+                edges {
+                node {
+                    name
+                    _id
+                    table {
+                    dataset {
+                        fullSlug
+                    }
+                    slug
+                    }
+                }
+                }
+            }
+            }
+    """
+
+    variables = {"column_name": directory_column_name}
+    response = backend._execute_query(query=query, variables=variables)
+    response = backend._simplify_graphql_response(response)["allColumn"]
+    df = pd.json_normalize(response)
+
+    colunas_de_diretorio = df["table.dataset.fullSlug"].str.contains(
+        "diretorios"
+    )
+    for _, coluna in df[colunas_de_diretorio].iterrows():
+        if coluna["table.slug"] == directory_table_name:
+            if verbose:
+                print(
+                    f"Connecting to the directory column: {coluna['table.dataset.fullSlug']}.{coluna['table.slug']}:{coluna['name']}"
+                )
+
+            return coluna["_id"]  # type: ignore
+
+    raise (
+        ValueError(
+            f"WARNING - Unable to find the directory column with the following information: Column name: {directory_column_name}. Table: {directory_table_name}"
+        )
+    )
+
+
+def create_column(
+    backend: b.Backend,
+    mutation_parameters: Optional[dict[str, str]] = None,
+    verbose: bool = False,
+) -> bool:
+    ## tinha que ser create or replace, por enquanto ele duplica
+    ## os dados se rodar duas vezes por isso atenção na hora de rodar!
+
+    # GraphQL mutation to create or update a column
+    mutation = """
+                    mutation($input: CreateUpdateColumnInput!) {
+                        CreateUpdateColumn(input: $input) {
+                            errors {
+                                field,
+                                messages
+                            },
+                            clientMutationId,
+                            column {
+                                id,
+                            }
+                        }
+                    }
+        """
+
+    # Set headers for the GraphQL request, including the token for authentication
+    headers = get_headers(backend)
+
+    # Print the mutation parameters for debugging purposes
+    pretty_json = json.dumps(mutation_parameters, indent=4)
+
+    if verbose:
+        print(pretty_json)
+
+    # Execute the GraphQL query with the provided mutation parameters and headers
+    response = backend._execute_query(
+        query=mutation,
+        variables={"input": mutation_parameters},  # type: ignore
+        headers=headers,
+    )
+
+    # Print the response for debugging purposes
+    if response["CreateUpdateColumn"]["errors"] != []:
+        pretty_json = json.dumps(response, indent=4)
+
+        if verbose:
+            print(pretty_json)
+
+        return False
+
+    return True
+
+
+def get_column_id(
+    table_id: str, column_name: str, backend: b.Backend
+) -> Optional[str]:
+    query = f"""{{
+        allColumn(table_Id:"{table_id}", name:"{column_name}"){{
+        edges{{
+            node{{
+            _id
+            }}
+        }}
+        }}
+    }}"""
+
+    data = backend._execute_query(query=query)
+    data = backend._simplify_graphql_response(response=data)["allColumn"]
+    if data:
+        return data[0]["_id"]
+    else:
+        return None
+
+
+def get_n_columns(table_id, backend: b.Backend):
+    query = f"""query get_n_columns{{
+        allTable(id:"{table_id}"){{
+            edges{{
+            node{{
+                columns{{
+                edgeCount
+                }}
+            }}
+            }}
+        }}
+        }}"""
+
+    data = backend._execute_query(query=query)
+    data = backend._simplify_graphql_response(response=data)["allTable"]
+
+    return data[0]["columns"]["edgeCount"]
+
+
+def get_bqtype_dict(backend: b.Backend):
+    # GraphQL query to fetch all BigQuery types
+    query = """{
+    allBigquerytype{
+      edges{
+        node{
+          name
+          _id
+        }
+      }
+    }
+  }"""
+
+    # Execute the GraphQL query to retrieve the data
+    data = backend._execute_query(query=query)
+
+    # Simplify the GraphQL response to extract the relevant data
+    data = backend._simplify_graphql_response(response=data)["allBigquerytype"]
+
+    # Create a dictionary where the 'name' part is the key and the '_id' is the value
+    bqtype_dict = {item["name"]: item["_id"] for item in data}
+
+    # Return the resulting dictionary
+    return bqtype_dict
+
+
+def check_metadata_columns(
+    dataset_id: str,
+    table_slug: str,
+    backend: b.Backend,
+    architecture: pd.DataFrame,
+) -> None:
+    # Get the table ID using the dataset ID and table ID
+    table_id = backend._get_table_id_from_name(
+        gcp_dataset_id=dataset_id, gcp_table_id=table_slug
+    )
+
+    n_columns_metadata = get_n_columns(table_id=table_id, backend=backend)
+    n_columns_architecture = architecture.shape[0]
+
+    if n_columns_metadata == n_columns_architecture:
+        print("Upload done!!. Columns metadata equal to columns architecture!!")
+    else:
+        print(
+            f"Something wrong!!. Number of metadata columns not equal to architecture, {n_columns_metadata} != {n_columns_architecture}"
+        )
+
+
+def get_all_columns_id(table_id: str, backend: b.Backend):
+    query = f"""{{
+        allColumn(table_Id:"{table_id}"){{
+        edges{{
+            node{{
+            _id
+            }}
+        }}
+        }}
+    }}"""
+
+    data = backend._execute_query(query=query)
+    columns_json = backend._simplify_graphql_response(response=data)[
+        "allColumn"
+    ]
+
+    if data:
+        columns_list = [col["_id"] for col in columns_json]
+        return columns_list
+    else:
+        print("There is no column in this table to be deleted")
+
+
+def delete_column_by_id(column_id: str, backend: b.Backend):
+    mutation = """
+                    mutation($input: UUID!) {
+                        DeleteColumn(id: $input) {
+                            errors,
+                            ok
+                        }
+                    }
+        """
+
+    # Set headers for the GraphQL request, including the token for authentication
+    headers = get_headers(backend)
+    # Execute the GraphQL query with the provided mutation parameters and headers
+    response = backend._execute_query(
+        query=mutation, variables={"input": column_id}, headers=headers
+    )
+
+    # Print the response for debugging purposes
+    if response["DeleteColumn"]["errors"] != []:
+        pretty_json = json.dumps(response, indent=4)
+        print(pretty_json)
+        return False
+
+    return True
+
+
+def delete_all_columns(table_id: str, backend: b.Backend):
+    columns = get_all_columns_id(table_id, backend)
+
+    for col in columns:
+        delete_column_by_id(col, backend)
+
+
+def upload_columns_from_architecture(
+    dataset_id: str,
+    table_id: str,
+    url_architecture: str,
+    backend: b.Backend,
+    if_column_exists: str = "pass",
+    replace_all_schema: bool = True,
+    verbose: bool = False,
+):
+    """
+    Uploads columns from an architecture table to the specified dataset and table in  platform.
+
+    Notes:
+    - This function assumes a specific structure/format for the architecture table.
+    - It interacts with the Base dos Dados GraphQL API to create or update columns.
+    - Columns from the architecture table are uploaded to the specified dataset and table.
+    - It prints information about the existing columns
+    and performs metadata checks after uploading columns.
+    """
+    accepted_if_exists_values = ["pass", "replace"]
+
+    if if_column_exists not in accepted_if_exists_values:
+        raise ValueError(
+            f"`if_exists` only accepts {accepted_if_exists_values}"
+        )
+
+    # Get the table ID using the dataset ID and table ID
+    table_slug = backend._get_table_id_from_name(
+        gcp_dataset_id=dataset_id, gcp_table_id=table_id
+    )
+
+    # Read the architecture table
+    architecture = read_architecture_table(url=url_architecture)
+
+    # Get the id of BigQueryTypes in a dict
+    bqtype_dict = get_bqtype_dict(backend)
+
+    if replace_all_schema:
+        delete_all_columns(table_slug, backend)
+
+    # Iterate over each row in the 'architecture' DataFrame
+    for _, row in architecture.iterrows():
+        column_name = row["name"]
+
+        if verbose:
+            print(f"\nColumn: {column_name}")
+
+        column_id = get_column_id(
+            table_id=table_slug,
+            column_name=column_name,
+            backend=backend,
+        )
+
+        if column_id is None and verbose:
+            print(f"{column_name} dont exists")
+
+        if column_id is not None and if_column_exists == "pass":
+            if verbose:
+                print("row already exists")
+            continue
+
+        # Define the mutation parameters for creating a new column
+        directory_column_id = None
+        if row["directory_column"]:
+            directory_table_slug = (
+                row["directory_column"].split(":")[0].split(".")[1]
+            )
+            directory_column_name = row["directory_column"].split(":")[1]
+            directory_column_id = get_directory_column_id(
+                directory_column_name, directory_table_slug, backend, verbose
+            )
+
+        mutation_parameters = {
+            "table": table_slug,
+            "bigqueryType": bqtype_dict[row["bigquery_type"].upper()],
+            "name": row["name"],
+            "description": row["description"],
+            "coveredByDictionary": row["covered_by_dictionary"] == "yes",
+            "measurementUnit": row["measurement_unit"],
+            "containsSensitiveData": row["has_sensitive_data"] == "yes",
+            "observations": row["observations"],
+            "directoryPrimaryKey": directory_column_id,
+        }
+
+        if column_id is not None:
+            mutation_parameters["id"] = column_id
+
+        create_column(
+            backend, mutation_parameters=mutation_parameters, verbose=verbose
+        )
+
+    check_metadata_columns(
+        dataset_id=dataset_id,
+        table_slug=table_id,
+        backend=backend,
+        architecture=architecture,
+    )
diff --git a/src/databasers_utils/utils.py b/src/databasers_utils/utils.py
new file mode 100644
index 0000000..60776fe
--- /dev/null
+++ b/src/databasers_utils/utils.py
@@ -0,0 +1,108 @@
+import io
+import os
+import pandas as pd
+import numpy as np
+import requests
+from basedosdados import Backend
+from urllib.parse import urlparse, urlunparse, urlencode
+from .constants import constants
+import ruamel.yaml as yaml
+
+
+def read_architecture_table(url: str) -> pd.DataFrame:
+    parsed_url = urlparse(url)
+
+    # Path Format: /spreadsheets/d/<ID>/edit
+    # Drop /edit and add "/export"
+    new_path = "/".join(parsed_url.path.split("/")[0:-1]) + "/export"
+
+    new_query = urlencode({"format": "csv"}, doseq=True)
+    new_url = parsed_url._replace(path=new_path, query=new_query)
+
+    export_url = urlunparse(new_url)
+
+    df_architecture = pd.read_csv(
+        io.StringIO(
+            requests.get(export_url, timeout=10).content.decode("utf-8")
+        )
+    )
+
+    df_architecture = df_architecture.loc[
+        df_architecture["name"] != "(excluido)"
+    ]
+
+    return df_architecture.replace(np.nan, "", regex=True)
+
+
+def get_credentials_from_env() -> dict[str, str]:
+    return {
+        "email": os.getenv(constants.ENV.value["email"]).strip(),  # type: ignore
+        "password": os.getenv(constants.ENV.value["password"]).strip(),  # type: ignore
+    }
+
+
+def get_headers(backend: Backend) -> dict[str, str]:
+    """
+    Get headers to be able to do mutations in backend api
+    """
+
+    credentials = get_credentials_from_env()
+
+    mutation = """
+        mutation ($email: String!, $password: String!) {
+            tokenAuth(email: $email, password: $password) {
+                token
+            }
+        }
+    """
+
+    variables = {
+        "email": credentials["email"],
+        "password": credentials["password"],
+    }
+
+    response = backend._execute_query(query=mutation, variables=variables)
+    token = response["tokenAuth"]["token"]
+
+    header_for_mutation_query = {"Authorization": f"Bearer {token}"}
+
+    return header_for_mutation_query
+
+
+def get_model_directory(dir: str) -> str:
+    model_dir = f"{dir}/models"
+    if os.path.exists(model_dir):
+        return model_dir
+    else:
+        raise Exception(f"Failed to find models directory under {dir}")
+
+
+def update_dbt_project(dataset_id: str, dir: str) -> None:
+    if ["dbt_project.yml", "dbt_project.yaml"] not in os.listdir(dir):
+        raise Exception("Failed to find root directory with dbt_project file")
+
+    dbt_project_yaml = f"{dir}/dbt_project.yml"
+
+    if not os.path.exists(dbt_project_yaml):
+        dbt_project_yaml = f"{dir}/dbt_project.yaml"
+
+    yaml_obj = yaml.YAML(typ="rt")
+    yaml_obj.explicit_start = True
+    yaml_obj.indent(mapping=2, sequence=2, offset=2)
+
+    with open(dbt_project_yaml, "r") as file:
+        data = yaml_obj.load(file)
+
+    models = data["models"]["basedosdados"]
+    models.update(
+        {dataset_id: {"+materialized": "table", "+schema": dataset_id}}
+    )
+
+    data["models"]["basedosdados"] = {
+        key: models[key] for key in sorted(models)
+    }
+
+    with open(dbt_project_yaml, "w") as file:
+        yaml_obj.dump(data, file)
+
+    print(f"dbt_project successfully updated with {dataset_id}!")
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/models/br_ibge_pib/br_ibge_pib__gini.sql b/tests/models/br_ibge_pib/br_ibge_pib__gini.sql
new file mode 100644
index 0000000..4518a0a
--- /dev/null
+++ b/tests/models/br_ibge_pib/br_ibge_pib__gini.sql
@@ -0,0 +1,11 @@
+{{ config(alias="gini", schema="br_ibge_pib") }}
+select
+    safe_cast(id_uf as string) id_uf,
+    safe_cast(ano as int64) ano,
+    safe_cast(gini_pib as int64) gini_pib,
+    safe_cast(gini_va as int64) gini_va,
+    safe_cast(gini_va_agro as int64) gini_va_agro,
+    safe_cast(gini_va_industria as int64) gini_va_industria,
+    safe_cast(gini_va_servicos as int64) gini_va_servicos,
+    safe_cast(gini_va_adespss as int64) gini_va_adespss,
+from `basedosdados-dev.br_ibge_pib_staging.gini` as t
diff --git a/tests/models/br_ibge_pib/br_ibge_pib__uf.sql b/tests/models/br_ibge_pib/br_ibge_pib__uf.sql
new file mode 100644
index 0000000..d89b5c6
--- /dev/null
+++ b/tests/models/br_ibge_pib/br_ibge_pib__uf.sql
@@ -0,0 +1,13 @@
+{{ config(alias="uf", schema="br_ibge_pib") }}
+select
+    safe_cast(ano as int64) ano,
+    safe_cast(id_uf as string) id_uf,
+    safe_cast(sigla_uf as string) sigla_uf,
+    safe_cast(pib as int64) pib,
+    safe_cast(impostos_liquidos as int64) impostos_liquidos,
+    safe_cast(va as int64) va,
+    safe_cast(va_agropecuaria as int64) va_agropecuaria,
+    safe_cast(va_industria as int64) va_industria,
+    safe_cast(va_servicos as int64) va_servicos,
+    safe_cast(va_adespss as int64) va_adespss,
+from `basedosdados-dev.br_ibge_pib_staging.uf` as t
diff --git a/tests/models/br_ibge_pib/schema.yml b/tests/models/br_ibge_pib/schema.yml
new file mode 100644
index 0000000..3a056b1
--- /dev/null
+++ b/tests/models/br_ibge_pib/schema.yml
@@ -0,0 +1,87 @@
+version: 2
+
+models:
+  - name: br_ibge_pib__uf
+    description: Insert table description here
+    tests:
+      - dbt_utils.unique_combination_of_columns:
+            combination_of_columns:
+              - insert unique keys here
+      - not_null_proportion_multiple_columns:
+            at_least: 0.95
+    columns:
+      - name: ano
+        description: Ano
+        tests:
+          - relationships:
+                to: ref('br_bd_diretorios_data_tempo__ano')
+                field: ano.ano
+      - name: id_uf
+        description: ID UF - IBGE 2 Dígitos
+        tests:
+          - relationships:
+                to: ref('br_bd_diretorios_brasil__uf')
+                field: id_uf
+      - name: sigla_uf
+        description: Sigla da Unidade da Federação
+        tests:
+          - relationships:
+                to: ref('br_bd_diretorios_brasil__uf')
+                field: sigla
+      - name: pib
+        description: Produto Interno Bruto a preços correntes
+      - name: impostos_liquidos
+        description: Impostos, líquidos de subsídios, sobre produtos a preços correntes
+      - name: va
+        description: Valor adicionado bruto a preços correntes total
+      - name: va_agropecuaria
+        description: Valor adicionado bruto a preços correntes da agropecuária
+      - name: va_industria
+        description: Valor adicionado bruto a preços correntes da indústria
+      - name: va_servicos
+        description: Valor adicionado bruto a preços correntes dos serviços, exclusive
+            administração, defesa, educação e saúde públicas e seguridade social
+      - name: va_adespss
+        description: Valor adicionado bruto a preços correntes da administração, defesa,
+            educação e saúde públicas e seguridade social
+  - name: br_ibge_pib__gini
+    description: Insert table description here
+    tests:
+      - dbt_utils.unique_combination_of_columns:
+            combination_of_columns:
+              - insert unique keys here
+      - not_null_proportion_multiple_columns:
+            at_least: 0.95
+    columns:
+      - name: id_uf
+        description: ID UF - IBGE 2 Dígitos
+        tests:
+          - relationships:
+                to: ref('br_bd_diretorios_brasil__uf')
+                field: id_uf
+      - name: ano
+        description: Ano
+        tests:
+          - relationships:
+                to: ref('br_bd_diretorios_data_tempo__ano')
+                field: ano.ano
+      - name: gini_pib
+        description: Índice de Gini da distribuição do produto interno bruto a preços
+            correntes
+      - name: gini_va
+        description: Índice de Gini da distribuição do valor adicionado bruto a preços
+            correntes da agropecuária
+      - name: gini_va_agro
+        description: Índice de Gini da distribuição do valor adicionado bruto a preços
+            correntes da agropecuária
+      - name: gini_va_industria
+        description: Índice de Gini da distribuição do valor adicionado bruto a preços
+            correntes da indústria
+      - name: gini_va_servicos
+        description: Índice de Gini da distribuição do valor adicionado bruto a preços
+            correntes dos serviços, exclusive administração, defesa, educação e saúde
+            públicas e seguridade social
+      - name: gini_va_adespss
+        description: Índice de Gini da distribuição do valor adicionado bruto a preços
+            correntes da administração, defesa, educação e saúde públicas e seguridade
+            social
diff --git a/tests/test_get_architecture_table_from_api.py b/tests/test_get_architecture_table_from_api.py
new file mode 100644
index 0000000..f484a45
--- /dev/null
+++ b/tests/test_get_architecture_table_from_api.py
@@ -0,0 +1,8 @@
+import pandas as pd
+from databasers_utils import get_architecture_table_from_api
+
+
+def test_get_architecture_table_from_api():
+    arch = get_architecture_table_from_api("br_ms_sinasc", "microdados")
+    assert isinstance(arch, pd.DataFrame)
+    assert len(arch) > 0
diff --git a/tests/test_table_architecture.py b/tests/test_table_architecture.py
new file mode 100644
index 0000000..6e46a60
--- /dev/null
+++ b/tests/test_table_architecture.py
@@ -0,0 +1,63 @@
+import os
+import pandas as pd
+from databasers_utils import TableArchitecture
+
+TESTS_DIR = os.path.join(os.getcwd(), "tests")
+
+
+def test_architecture():
+    table_arch = TableArchitecture(
+        dataset_id="br_ibge_pib",
+        tables={
+            "uf": "https://docs.google.com/spreadsheets/d/12F5NzhOYlN_bi9flLBEdXDWpa5iVakSP4EKm9UoyWuo/edit?usp=drive_link",
+            "gini": "https://docs.google.com/spreadsheets/d/1K1svie4Gyqe6NnRjBgJbapU5sTsLqXWTQUmTRVIRwQc/edit?usp=drive_link",
+        },
+    )
+
+    tables = table_arch.tables()
+    assert "uf" and "gini" in tables.keys()
+    assert isinstance(tables["uf"], pd.DataFrame)
+    assert isinstance(tables["gini"], pd.DataFrame)
+    assert len(tables) == 2
+
+
+def test_create_yaml_file():
+    table_arch = TableArchitecture(
+        dataset_id="br_ibge_pib",
+        tables={
+            "uf": "https://docs.google.com/spreadsheets/d/12F5NzhOYlN_bi9flLBEdXDWpa5iVakSP4EKm9UoyWuo/edit?usp=drive_link",
+            "gini": "https://docs.google.com/spreadsheets/d/1K1svie4Gyqe6NnRjBgJbapU5sTsLqXWTQUmTRVIRwQc/edit?usp=drive_link",
+        },
+    )
+
+    os.makedirs(os.path.join(TESTS_DIR, "models"), exist_ok=True)
+
+    table_arch.create_yaml_file(dir=TESTS_DIR)
+
+    assert os.path.exists(
+        os.path.join(TESTS_DIR, "models", "br_ibge_pib", "schema.yml")
+    )
+
+
+def test_create_sql_files():
+    table_arch = TableArchitecture(
+        dataset_id="br_ibge_pib",
+        tables={
+            "uf": "https://docs.google.com/spreadsheets/d/12F5NzhOYlN_bi9flLBEdXDWpa5iVakSP4EKm9UoyWuo/edit?usp=drive_link",
+            "gini": "https://docs.google.com/spreadsheets/d/1K1svie4Gyqe6NnRjBgJbapU5sTsLqXWTQUmTRVIRwQc/edit?usp=drive_link",
+        },
+    )
+
+    os.makedirs(os.path.join(TESTS_DIR, "models"), exist_ok=True)
+
+    table_arch.create_sql_files(dir=TESTS_DIR)
+
+    assert os.path.exists(
+        os.path.join(
+            TESTS_DIR, "models", "br_ibge_pib", "br_ibge_pib__gini.sql"
+        )
+    )
+
+    assert os.path.exists(
+        os.path.join(TESTS_DIR, "models", "br_ibge_pib", "br_ibge_pib__uf.sql")
+    )