diff --git a/.github/workflows/update_datario_template.yaml b/.github/workflows/update_datario_template.yaml new file mode 100644 index 000000000..7f82f975c --- /dev/null +++ b/.github/workflows/update_datario_template.yaml @@ -0,0 +1,43 @@ +name: Update data.rio description template + +on: + push: + branches: + - master + paths: + - ".github/workflows/update_datario_template.yaml" + - "templates/**/*" + pull_request: + branches: + - master + paths: + - ".github/workflows/update_datario_template.yaml" + - "templates/**/*" + +env: + GCP_PROJECT_ID: ${{ secrets.GCP_PROJECT_ID }} + GCP_SA_KEY: ${{ secrets.GCP_SA_KEY }} + GH_PAT: ${{ secrets.GH_PAT }} + GKE_CLUSTER: ${{ secrets.GKE_CLUSTER_NAME }} + GKE_ZONE: ${{ secrets.GKE_ZONE }} + +jobs: + build-container: + name: Update data.rio description template + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + + - name: Setup Google Cloud CLI + uses: google-github-actions/setup-gcloud@v0.2.1 + with: + service_account_key: ${{ secrets.GCP_SA_KEY }} + project_id: ${{ secrets.GCP_PROJECT_ID }} + export_default_credentials: true + + - name: Upload templates folder + id: upload_folder + uses: google-github-actions/upload-cloud-storage@v1 + with: + path: templates + destination: datario-public \ No newline at end of file diff --git a/Dockerfile b/Dockerfile index 5d7476435..3d182507c 100644 --- a/Dockerfile +++ b/Dockerfile @@ -44,7 +44,8 @@ RUN python3 -m pip install --no-cache-dir -U "pip>=21.2.4" "prefect==$PREFECT_VE WORKDIR /app COPY . . COPY --from=curl-step /tmp/GDAL-3.4.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl /tmp/GDAL-3.4.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl -RUN python3 -m pip install --prefer-binary --no-cache-dir -U . && \ +RUN python3 -m pip install --prefer-binary --no-cache-dir "arcgis==1.9.1" && \ + python3 -m pip install --prefer-binary --no-cache-dir -U . && \ mkdir -p /opt/prefect/app/bases && \ mkdir -p /root/.basedosdados/templates && \ mkdir -p /root/.basedosdados/credentials/ && \ diff --git a/pipelines/rj_escritorio/data_catalog/constants.py b/pipelines/rj_escritorio/data_catalog/constants.py new file mode 100644 index 000000000..776e9e377 --- /dev/null +++ b/pipelines/rj_escritorio/data_catalog/constants.py @@ -0,0 +1,17 @@ +# -*- coding: utf-8 -*- +""" +Constants for the data catalog flow. +""" +from enum import Enum + + +class constants(Enum): # pylint: disable=invalid-name + """ + Constant values for the data catalog flow + """ + + ARCGIS_CREDENTIALS_SECRET_PATH = "arcgis_credentials" + DONT_PUBLISH = ["datario.dados_mestres.bairro", "datario.dados_mestres.logradouro"] + GCS_BUCKET_NAME = "datario-public" + DESCRIPTION_HTML_TEMPLATE_PATH = "templates/datario_description.html.jinja" + DISCORD_WEBHOOK_SECRET_PATH = "missing_metadata_webhook" diff --git a/pipelines/rj_escritorio/data_catalog/flows.py b/pipelines/rj_escritorio/data_catalog/flows.py index 1a615ff5c..e8bd78fe8 100644 --- a/pipelines/rj_escritorio/data_catalog/flows.py +++ b/pipelines/rj_escritorio/data_catalog/flows.py @@ -10,10 +10,10 @@ from pipelines.constants import constants from pipelines.rj_escritorio.data_catalog.schedules import update_data_catalog_schedule from pipelines.rj_escritorio.data_catalog.tasks import ( - generate_dataframe_from_list_of_tables, + fetch_metadata, list_tables, merge_list_of_list_of_tables, - update_gsheets_data_catalog, + update_datario_catalog, ) from pipelines.rj_escritorio.notify_flooding.tasks import ( parse_comma_separated_string_to_list, @@ -30,8 +30,6 @@ # Parameters project_ids = Parameter("project_ids") - spreadsheet_url = Parameter("spreadsheet_url") - sheet_name = Parameter("sheet_name") bq_client_mode = Parameter("bq_client_mode", default="prod") exclude_staging = Parameter("exclude_staging", default=True) exclude_test = Parameter("exclude_test", default=True) @@ -49,12 +47,8 @@ exclude_logs=unmapped(exclude_logs), ) list_of_tables = merge_list_of_list_of_tables(list_of_list_of_tables) - dataframe = generate_dataframe_from_list_of_tables(list_of_tables) - update_gsheets_data_catalog( - dataframe=dataframe, - spreadsheet_url=spreadsheet_url, - sheet_name=sheet_name, - ) + list_of_metadata = fetch_metadata(list_of_tables=list_of_tables) + update_datario_catalog(list_of_metadata=list_of_metadata) rj_escritorio_data_catalog_flow.storage = GCS(constants.GCS_FLOWS_BUCKET.value) diff --git a/pipelines/rj_escritorio/data_catalog/tasks.py b/pipelines/rj_escritorio/data_catalog/tasks.py index b124748ba..d825bcc83 100644 --- a/pipelines/rj_escritorio/data_catalog/tasks.py +++ b/pipelines/rj_escritorio/data_catalog/tasks.py @@ -3,16 +3,22 @@ """ Tasks for generating a data catalog from BigQuery. """ +from typing import List + +from arcgis.gis import Item from google.cloud import bigquery -import gspread -import pandas as pd from prefect import task +from pipelines.rj_escritorio.data_catalog.constants import constants from pipelines.rj_escritorio.data_catalog.utils import ( + build_items_data_from_metadata_json, + create_or_update_item, + fetch_api_metadata, get_bigquery_client, - write_data_to_gsheets, + get_directory, + get_all_items, ) -from pipelines.utils.utils import get_credentials_from_env, log +from pipelines.utils.utils import log @task @@ -99,65 +105,100 @@ def merge_list_of_list_of_tables(list_of_list_of_tables: list) -> list: @task -def generate_dataframe_from_list_of_tables(list_of_tables: list) -> pd.DataFrame: +def fetch_metadata(list_of_tables: list) -> list: """ - Generate a Pandas DataFrame from a list of tables. + For each table in the list, fetches metadata from the metadata API. Args: list_of_tables: List of tables. Returns: - Pandas DataFrame. + List of tables with metadata. Each table is a dictionary in the format: + { + "project_id": "project_id", + "dataset_id": "dataset_id", + "table_id": "table_id", + "url": "https://console.cloud.google.com/bigquery?p={project_id}&d={dataset_id}&t={table_id}&page=table", + "private": True/False, + "metadata": { + "title": "Title", + "short_description": "Short description", + "long_description": "Long description", + "update_frequency": "Update frequency", + "temporal_coverage": "Temporal coverage", + "data_owner": "Data owner", + "publisher_name": "Publisher name", + "publisher_email": "Publisher email", + "tags": ["Tag1", "Tag2"], + "categories": ["Category1", "Category2"], + "columns": [ + { + "name": "column_name", + "description": "Column description", + } + ] + } + } """ - dataframe = pd.DataFrame(list_of_tables) - log(f"Generated DataFrame with shape {dataframe.shape}.") - return dataframe + log(f"Fetching metadata for {len(list_of_tables)} tables.") + remove_tables = [] + for table in list_of_tables: + project_id = table["project_id"] + dataset_id = table["dataset_id"] + table_id = table["table_id"] + try: + table["metadata"] = fetch_api_metadata( + project_id=project_id, dataset_id=dataset_id, table_id=table_id + ) + except: # pylint: disable=bare-except + log( + f"Error fetching metadata for {project_id}.{dataset_id}.{table_id}. Will exclude this table from the catalog." # pylint: disable=line-too-long + ) + remove_tables.append(table) + for table in remove_tables: + list_of_tables.remove(table) + log(f"Fetched metadata for {len(list_of_tables)} tables.") + return list_of_tables @task -def update_gsheets_data_catalog( - dataframe: pd.DataFrame, spreadsheet_url: str, sheet_name: str -) -> None: +def update_datario_catalog(list_of_metadata: list): # pylint: disable=too-many-locals """ - Update a Google Sheets spreadsheet with a DataFrame. + Update the data.rio catalog with our tables. Args: - dataframe: Pandas DataFrame. - spreadsheet_url: Google Sheets spreadsheet URL. - sheet_name: Google Sheets sheet name. + list_of_metadata: List of tables with metadata. """ - # Get gspread client - credentials = get_credentials_from_env( - scopes=[ - "https://www.googleapis.com/auth/spreadsheets", - "https://www.googleapis.com/auth/drive", - ] - ) - gspread_client = gspread.authorize(credentials) - # Open spreadsheet - log(f"Opening Google Sheets spreadsheet {spreadsheet_url} with sheet {sheet_name}.") - sheet = gspread_client.open_by_url(spreadsheet_url) - worksheet = sheet.worksheet(sheet_name) - # Update spreadsheet - log("Deleting old data.") - worksheet.clear() - log("Rewriting headers.") - write_data_to_gsheets( - worksheet=worksheet, - data=[dataframe.columns.tolist()], - ) - log("Updating new data.") - write_data_to_gsheets( - worksheet=worksheet, - data=dataframe.values.tolist(), - start_cell="A2", - ) - # Add filters - log("Adding filters.") - first_col = "A" - last_col = chr(ord(first_col) + len(dataframe.columns) - 1) - worksheet.set_basic_filter(f"{first_col}:{last_col}") - # Resize columns - log("Resizing columns.") - worksheet.columns_auto_resize(0, len(dataframe.columns) - 1) - log("Done.") + log(f"Updating data.rio catalog with {len(list_of_metadata)} tables.") + updated_items = [] + duplicates_list = constants.DONT_PUBLISH.value + ( + items_data, + categories, + project_ids, + dataset_ids, + table_ids, + ) = build_items_data_from_metadata_json(metadata=list_of_metadata) + for item_data, item_categories, project_id, dataset_id, table_id in zip( + items_data, categories, project_ids, dataset_ids, table_ids + ): + log(f"Updating table `{project_id}.{dataset_id}.{table_id}`") + item: Item = create_or_update_item( + project_id=project_id, + dataset_id=dataset_id, + table_id=table_id, + data=item_data, + ) + log(f"Created/updated item: ID={item.id}, Title={item.title}") + item.share(everyone=True, org=True, groups=item_categories) + log(f"Shared item: ID={item.id} with groups: {item_categories}") + move_dir = get_directory(project_id, dataset_id, table_id, duplicates_list) + item.move(move_dir) + log(f"Moved item: ID={item.id} to {move_dir}/ directory") + updated_items.append(item.id) + all_items: List[Item] = get_all_items() + not_updated_items = [item for item in all_items if item.id not in updated_items] + log(f"Deleting {len(not_updated_items)} items.") + for item in not_updated_items: + item.delete() + log(f"Deleted item: ID={item.id}") diff --git a/pipelines/rj_escritorio/data_catalog/utils.py b/pipelines/rj_escritorio/data_catalog/utils.py index 04b2a624e..f6b5a3759 100644 --- a/pipelines/rj_escritorio/data_catalog/utils.py +++ b/pipelines/rj_escritorio/data_catalog/utils.py @@ -2,12 +2,238 @@ """ Helper functions for generating a data catalog from BigQuery. """ -from typing import Any, List +from typing import Any, Dict, List, Union +from arcgis import GIS +from arcgis.gis import ContentManager, Item from google.cloud import bigquery from gspread.worksheet import Worksheet +import jinja2 +import requests -from pipelines.utils.utils import get_credentials_from_env +from pipelines.rj_escritorio.data_catalog.constants import ( + constants as data_catalog_constants, +) +from pipelines.utils.utils import ( + get_credentials_from_env, + get_vault_secret, + list_blobs_with_prefix, + send_discord_message, +) + + +class GisItemNotFound(Exception): + """ + Raised when an item is not found. + """ + + +def build_html_from_metadata( + html_template: jinja2.Template, + metadata: Dict[str, Any], +) -> str: + """ + Builds HTML from the table metadata + """ + return html_template.render( + **metadata, + ) + + +def build_items_data_from_metadata_json(metadata: List[Dict[str, Any]]) -> List[dict]: + """ + Builds item data from a schema.yml file. + Returns: + A dictionary containing the item data. The format is: + { + item_properties={ + "type": "Document Link", + "typeKeywords": "Data, Document", + "description": "some description here", # We need to parse from Markdown to HTML + "title": "some title here", + "url": "https://some.url.here", + "tags": "tag1, tag2, tag3", + ... + }, + } + """ + items_data = [] + categories = [] + project_ids = [] + dataset_ids = [] + table_ids = [] + html_template = get_description_html_template() + for table in metadata: + project_id = table["project_id"] + dataset_id = table["dataset_id"] + table_id = table["table_id"] + item_data = { + "item_properties": { + "type": "Document Link", + "typeKeywords": "Data, Document", + "description": build_html_from_metadata( + html_template=html_template, + metadata=table, + ), + "snippet": table["metadata"]["short_description"], + "title": table["metadata"]["title"], + "url": table["url"], + "tags": ",".join( + get_default_tags(project_id, dataset_id, table_id) + + table["metadata"]["tags"] + ), + "licenseInfo": get_license(), + "accessInformation": table["metadata"]["data_owner"], + }, + } + items_data.append(item_data) + categories.append(table["metadata"]["categories"]) + project_ids.append(project_id) + dataset_ids.append(dataset_id) + table_ids.append(table_id) + return items_data, categories, project_ids, dataset_ids, table_ids + + +def create_or_update_item( + project_id: str, dataset_id: str, table_id: str, data: dict +) -> Item: + """ + Creates or updates an item. + """ + gis = get_gis_client() + tags = get_default_tags(project_id, dataset_id, table_id) + tags_query = "" + for tag in tags: + if tags_query == "": + tags_query = f"tags:{tag}" + else: + tags_query += f" AND tags:{tag}" + try: # If item already exists, update it. + item = get_item( + search_query=tags_query, + gis=gis, + ) + item.update(**data) + return item + except GisItemNotFound: # Else, create it. + content_manager: ContentManager = gis.content + item = content_manager.add(**data) # pylint: disable=no-member + return item + + +def fetch_api_metadata( + project_id: str, dataset_id: str, table_id: str +) -> Dict[str, Any]: + """ + Fetches table metadata from the metadata API. + + Args: + project_id: Project ID. + dataset_id: Dataset ID. + table_id: Table ID. + + Returns: + A dictionary in the following format: + { + "title": "Title", + "short_description": "Short description", + "long_description": "Long description", + "update_frequency": "Update frequency", + "temporal_coverage": "Temporal coverage", + "data_owner": "Data owner", + "publisher_name": "Publisher name", + "publisher_email": "Publisher email", + "tags": ["Tag1", "Tag2"], + "categories": ["Category1", "Category2"], + "columns": [ + { + "name": "column_name", + "description": "Column description", + } + ] + } + """ + metadata = { + "title": None, + "short_description": None, + "long_description": None, + "update_frequency": None, + "temporal_coverage": None, + "data_owner": None, + "publisher_name": None, + "publisher_email": None, + "tags": [], + "categories": [], + "columns": [], + } + base_url = "https://meta.dados.rio/api" + dataset_metadata = fetch_single_result( + f"{base_url}/datasets/?project={project_id}&name={dataset_id}" + ) + if dataset_metadata is None: + notify_missing_metadata( + project_id=project_id, + dataset_id=dataset_id, + table_id=None, + ) + raise Exception( + f"Dataset metadata not found for project {project_id} and dataset {dataset_id}" + ) + title_prefix = dataset_metadata["title_prefix"] + table_metadata = fetch_single_result( + f"{base_url}/tables/?project={project_id}&dataset={dataset_id}&name={table_id}" + ) + if table_metadata is None: + notify_missing_metadata( + project_id=project_id, + dataset_id=dataset_id, + table_id=table_id, + ) + raise Exception( + f"Table metadata not found for project {project_id}, dataset {dataset_id} and table {table_id}" # noqa, pylint: disable=line-too-long + ) + metadata["title"] = f"{title_prefix}: {table_metadata['title']}" + metadata["short_description"] = table_metadata["short_description"] + metadata["long_description"] = table_metadata["long_description"] + metadata["update_frequency"] = table_metadata["update_frequency"] + metadata["temporal_coverage"] = table_metadata["temporal_coverage"] + metadata["data_owner"] = table_metadata["data_owner"] + metadata["publisher_name"] = table_metadata["publisher_name"] + metadata["publisher_email"] = table_metadata["publisher_email"] + metadata["tags"] = table_metadata["tags"] + metadata["categories"] = table_metadata["categories"] + metadata["columns"] = [] + for column in table_metadata["columns"]: + metadata["columns"].append( + { + "name": column["name"], + "description": column["description"], + } + ) + return metadata + + +def fetch_single_result(url: str) -> Union[Dict[str, Any], None]: + """ + Fetches a DRF API and returns None if there's more than one result or no result at all. + """ + response = requests.get(url) + response.raise_for_status() + response_json = response.json() + if response_json["count"] == 1: + return response_json["results"][0] + return None + + +def get_all_items() -> List[Item]: + """ + Lists all datalake items on ArcGIS Online. + """ + search_query = "tags:datalake" + gis = get_gis_client() + content_manager: ContentManager = gis.content + items = content_manager.search(search_query) # pylint: disable=no-member + return items def get_bigquery_client(mode: str = "prod") -> bigquery.Client: @@ -22,6 +248,115 @@ def get_bigquery_client(mode: str = "prod") -> bigquery.Client: return client +def get_default_tags(project_id: str, dataset_id: str, table_id: str) -> List[str]: + """ + Returns the default tags. + """ + return ["datario", "escritorio_de_dados", "datalake"] + [ + project_id, + dataset_id, + table_id, + ] + + +def get_description_html_template() -> jinja2.Template: + """ + Downloads the Jinja template from GCS and then returns it. + """ + blobs = list_blobs_with_prefix( + bucket_name=data_catalog_constants.GCS_BUCKET_NAME.value, + prefix=data_catalog_constants.DESCRIPTION_HTML_TEMPLATE_PATH.value, + ) + blob = blobs[0] + template = blob.download_as_string().decode("utf-8") + return jinja2.Template(template) + + +def get_directory( + project_id: str, dataset_id: str, table_id: str, duplicates_list: List[str] = None +) -> str: + """ + Returns the directory where the item will be stored. + """ + duplicates_list = duplicates_list or data_catalog_constants.DONT_PUBLISH.value + full_name = f"{project_id}.{dataset_id}.{table_id}" + if full_name in duplicates_list: + return "duplicates" + return "public" + + +def get_gis_client() -> GIS: + """ + Returns a GIS client. + """ + gis_credentials = get_vault_secret( + data_catalog_constants.ARCGIS_CREDENTIALS_SECRET_PATH.value + )["data"] + url = gis_credentials["url"] + username = gis_credentials["username"] + password = gis_credentials["password"] + return GIS(url, username, password) + + +def get_item(*, item_id: str = None, search_query: str = None, gis: GIS = None) -> Item: + """ + Returns an item. + """ + # Ensures that either item_id or search is provided. + if not search_query and not item_id: + raise ValueError("You must provide either an item_id or a search query.") + # Get GIS client and the content manager. + gis = gis or get_gis_client() + content_manager: ContentManager = gis.content + # If item_id is provided, get and return the item. + if item_id: + item = content_manager.get(item_id) + if not item: + raise GisItemNotFound(f"Item with id {item_id} not found.") + return item + # Else, search for the item. + items = content_manager.search(search_query) + if len(items) == 0: + raise GisItemNotFound(f"No items found for search query: {search_query}") + return items[0] + + +def get_license(): + """ + Returns the license (can be HTML formatted). + """ + cc_license = "by-nd/3.0" + cc_license_name = "Attribution-NoDerivatives 3.0" + license_url = f"https://creativecommons.org/licenses/{cc_license}/deed.pt_BR" + return f""" + + Creative Commons License + +
+ This work is licensed under a + Creative Commons {cc_license_name} Unported License. + """ + + +def notify_missing_metadata(project_id: str, dataset_id: str, table_id: str) -> None: + """ + Notifies in Discord that the metadata is missing. + """ + if table_id is None: + message = f"Metadata for dataset `{project_id}.{dataset_id}` is missing." + else: + message = ( + f"Metadata for table `{project_id}.{dataset_id}.{table_id}` is missing." + ) + webhook_url = get_vault_secret( + data_catalog_constants.DISCORD_WEBHOOK_SECRET_PATH.value + )["data"]["url"] + send_discord_message(message=message, webhook_url=webhook_url) + + def write_data_to_gsheets( worksheet: Worksheet, data: List[List[Any]], start_cell: str = "A1" ): diff --git a/poetry.lock b/poetry.lock index 2227c76fb..e692f5513 100644 --- a/poetry.lock +++ b/poetry.lock @@ -285,6 +285,18 @@ files = [ {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"}, ] +[[package]] +name = "appnope" +version = "0.1.3" +description = "Disable App Nap on macOS >= 10.9" +category = "main" +optional = false +python-versions = "*" +files = [ + {file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"}, + {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"}, +] + [[package]] name = "apscheduler" version = "3.6.3" @@ -316,6 +328,44 @@ tornado = ["tornado (>=4.3)"] twisted = ["twisted"] zookeeper = ["kazoo"] +[[package]] +name = "arcgis" +version = "1.9.1" +description = "ArcGIS API for Python" +category = "main" +optional = false +python-versions = "*" +files = [ + {file = "arcgis-1.9.1.tar.gz", hash = "sha256:30fdbfe15bc408d662a9a53db496ecada864e24c06c59e1b81cf5fa5f817bbea"}, +] + +[package.dependencies] +cachetools = "*" +geomet = "*" +ipywidgets = ">=7" +jupyterlab = "*" +keyring = ">=19,<=21.8" +lerc = "*" +matplotlib = "*" +numpy = 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Jupyter Notebook" +category = "main" +optional = false +python-versions = ">=3.7" +files = [ + {file = "widgetsnbextension-4.0.5-py3-none-any.whl", hash = "sha256:eaaaf434fb9b08bd197b2a14ffe45ddb5ac3897593d43c69287091e5f3147bf7"}, + {file = "widgetsnbextension-4.0.5.tar.gz", hash = "sha256:003f716d930d385be3fd9de42dd9bf008e30053f73bddde235d14fbeaeff19af"}, +] + [[package]] name = "win32-setctime" version = "1.1.0" @@ -6491,6 +7962,33 @@ files = [ [package.extras] dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"] +[[package]] +name = "winkerberos" +version = "0.9.1" +description = "High level interface to SSPI for Kerberos client auth" +category = "main" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +files = [ + {file = "winkerberos-0.9.1-cp27-cp27m-win32.whl", hash = "sha256:aba3190d8c1f1bb876f398346ff7d7c2e173046a1653086dad638dc3b16cccdf"}, + {file = "winkerberos-0.9.1-cp27-cp27m-win_amd64.whl", hash = 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b/requirements-deploy.txt index bf5e18d7d..1dc430a46 100644 --- a/requirements-deploy.txt +++ b/requirements-deploy.txt @@ -6,3 +6,4 @@ typer==0.4.0 networkx==2.6.3 pytest-cov==3.0.0 pyyaml +arcgis==1.9.1 diff --git a/requirements-test.txt b/requirements-test.txt index f6bd03c70..ed0537293 100644 --- a/requirements-test.txt +++ b/requirements-test.txt @@ -1,4 +1,5 @@ networkx==2.6.3 pytest-cov==3.0.0 pyyaml -prefect==0.15.9 \ No newline at end of file +prefect==0.15.9 +arcgis==1.9.1 diff --git a/templates/datario_description.html.jinja b/templates/datario_description.html.jinja new file mode 100644 index 000000000..789725047 --- /dev/null +++ b/templates/datario_description.html.jinja @@ -0,0 +1,172 @@ +{% if private %} +
+

+ DADOS PRIVADOS +

+

+ Lorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam sed mi at ex imperdiet aliquam. Nulla in elit + elit. Fusce dapibus lacinia velit vel vehicula. Morbi convallis scelerisque eros, a euismod orci mattis + pulvinar. Quisque quis pellentesque libero. In varius lacinia nunc a scelerisque. Mauris placerat sem nulla, sit + amet. +

+
+{% endif %} +
+

+ {{ metadata.get('long_description') }} +

+
+
+

+ Como acessar +

+

+ Nessa página +

+

+ Aqui, você encontrará um botão para realizar o download dos dados em formato CSV e compactados com gzip. Ou, + para mesmo resultado, pode clicar aqui. +

+

+ BigQuery +

+
+ 
+            
+                SELECT
+            
+            
+                *
+            
+            
+                FROM
+            
+            
+                `{{ project_id }}.{{ dataset_id }}.{{ table_id }}`
+            
+            
+                LIMIT
+            
+            
+                1000
+            
+
+
+

+ Clique aqui + para ir diretamente a essa tabela no BigQuery. Caso não tenha experiência com BigQuery, + acesse nossa documentação para entender como acessar os dados. +

+

+ Python +

+
+ 
+            import
+            basedosdados
+            as
+            bd
+            

+            

+            # Para carregar o dado direto no pandas
+            

+            df
+            =
+            bd.read_sql
+            (
+            "SELECT * FROM `{{ project_id }}.{{ dataset_id }}.{{ table_id }}` LIMIT 1000"
+            ,
+            billing_project_id
+            =
+            "<id_do_seu_projeto_gcp>"
+            )
+
+
+

+

+ R +

+
+ 
+            install.packages(
+            "basedosdados"
+            )
+            

+            library(
+            "basedosdados"
+            )
+            

+            

+            # Defina o seu projeto no Google Cloud
+            

+            set_billing_id(
+            "<id_do_seu_projeto_gcp>"
+            )
+            

+            

+            # Para carregar o dado direto no R
+            

+            tb <- read_sql(
+            "SELECT * FROM `{{ project_id }}.{{ dataset_id }}.{{ table_id }}` LIMIT 1000"
+            )
+
+
+

+
+
+

+ Cobertura temporal +

+

+ {{ metadata.get('temporal_coverage') }} +

+
+
+

+ Frequência de atualização +

+

+ {{ metadata.get('update_frequency') }} +

+
+
+

+ Órgão gestor +

+

+ {{ metadata.get('data_owner') }} +

+
+
+

+ Colunas +

+ + + + + + + {% for column in metadata.get('columns') %} + + + + + {% endfor %} + +
NomeDescrição
{{ column["name"] }}{{ column["description"] }}
+
+
+

+ Dados do publicador +

+

+ Nome: {{ metadata.get('publisher_name') }}
+ E-mail: {{ metadata.get('publisher_email') }} +

+
\ No newline at end of file diff --git a/templates/datario_description_staging.html.jinja b/templates/datario_description_staging.html.jinja new file mode 100644 index 000000000..50248bcd7 --- /dev/null +++ b/templates/datario_description_staging.html.jinja @@ -0,0 +1,158 @@ +
+

+ {{ metadata.get('long_description') }} +

+
+
+

+ Como acessar +

+

+ Nessa página +

+

+ Aqui, você encontrará um botão para realizar o download dos dados em formato CSV e compactados com gzip. Ou, + para mesmo resultado, pode clicar aqui. +

+

+ BigQuery +

+
+ 
+            
+                SELECT
+            
+            
+                *
+            
+            
+                FROM
+            
+            
+                `{{ project_id }}.{{ dataset_id }}.{{ table_id }}`
+            
+            
+                LIMIT
+            
+            
+                1000
+            
+
+
+

+ Clique aqui + para ir diretamente a essa tabela no BigQuery. Caso não tenha experiência com BigQuery, + acesse nossa documentação para entender como acessar os dados. +

+

+ Python +

+
+ 
+            import
+            basedosdados
+            as
+            bd
+            

+            

+            # Para carregar o dado direto no pandas
+            

+            df
+            =
+            bd.read_sql
+            (
+            "SELECT * FROM `{{ project_id }}.{{ dataset_id }}.{{ table_id }}` LIMIT 1000"
+            ,
+            billing_project_id
+            =
+            "<id_do_seu_projeto_gcp>"
+            )
+
+
+

+

+ R +

+
+ 
+            install.packages(
+            "basedosdados"
+            )
+            

+            library(
+            "basedosdados"
+            )
+            

+            

+            # Defina o seu projeto no Google Cloud
+            

+            set_billing_id(
+            "<id_do_seu_projeto_gcp>"
+            )
+            

+            

+            # Para carregar o dado direto no R
+            

+            tb <- read_sql(
+            "SELECT * FROM `{{ project_id }}.{{ dataset_id }}.{{ table_id }}` LIMIT 1000"
+            )
+
+
+

+
+
+

+ Cobertura temporal +

+

+ {{ metadata.get('temporal_coverage') }} +

+
+
+

+ Frequência de atualização +

+

+ {{ metadata.get('update_frequency') }} +

+
+
+

+ Órgão gestor +

+

+ {{ metadata.get('data_owner') }} +

+
+
+

+ Colunas +

+ + + + + + + {% for column in metadata.get('columns') %} + + + + + {% endfor %} + +
NomeDescrição
{{ column["name"] }}{{ column["description"] }}
+
+
+

+ Dados do publicador +

+

+ Nome: {{ metadata.get('publisher_name') }}
+ E-mail: {{ metadata.get('publisher_email') }} +

+
\ No newline at end of file