Saravanan666/pypi-org huggingface dataset:
Package: dbt-databricks
dbtenables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.TheDatabricks Lakehouseprovides one simple platform to unify all your data, analytics and AI workloads.dbt-databricksThedbt-databricksadapter contains all of the code enabling dbt to work with Databricks. This adapter is based off the amazing work done indbt-spark. Some key features include:Easy setup. No need to install an ODBC driver as the adapter uses pure Python APIs.Open by default. For example, it uses the the open and performantDeltatable format by default. This has many benefits, including letting you useMERGEas the the default incremental materialization strategy.Support for Unity Catalog. dbt-databricks>=1.1.1 supports the 3-level namespace of Unity Catalog (catalog / schema / relations) so you can organize and secure your data the way you like.Performance. The adapter generates SQL expressions that are automatically accelerated by the native, vectorizedPhotonexecution engine.Choosing between dbt-databricks and dbt-sparkIf you are developing a dbt project on Databricks, we recommend usingdbt-databricksfor the reasons noted above.dbt-sparkis an actively developed adapter which works with Databricks as well as Apache Spark anywhere it is hosted e.g. on AWS EMR.Getting startedInstallationInstall using pip:pip install dbt-databricksUpgrade to the latest versionpip install --upgrade dbt-databricksProfile Setupyour_profile_name:
  target: dev
  outputs:
    dev:
      type: databricks
      catalog: [optional catalog name, if you are using Unity Catalog, only available in dbt-databricks>=1.1.1]
      schema: [database/schema name]
      host: [your.databrickshost.com]
      http_path: [/sql/your/http/path]
      token: [dapiXXXXXXXXXXXXXXXXXXXXXXX]Quick StartsThese following quick starts will get you up and running with thedbt-databricksadapter:Developing your first dbt projectUsing dbt Cloud with Databricks (Azure|AWS)Running dbt production jobs on Databricks WorkflowsUsing Unity Catalog with dbt-databricksUsing GitHub Actions for dbt CI/CD on DatabricksLoading data from S3 into Delta using the databricks_copy_into macroContribute to this repositoryCompatibilityThedbt-databricksadapter has been tested:with Python 3.7 or above.againstDatabricks SQLandDatabricks runtime releases 9.1 LTSand later.Tips and TricksChoosing compute for a Python modelYou can override the compute used for a specific Python model by setting thehttp_pathproperty in model configuration. This can be useful if, for example, you want to run a Python model on an All Purpose cluster, while running SQL models on a SQL Warehouse. Note that this capability is only available for Python models.def model(dbt, session):
    dbt.config(
      http_path="sql/protocolv1/..."
    )
Package: dbt-databricks-factory
dbt-databricks-factoryCreates dbt based GCP workflows.InstallationUse the package managerpipto installdbt-databricks-factoryfor [dp (data-pipelines-cli)]:pipinstalldbt-databricks-factoryUsageTo create a new dbt workflow json schema, run:python-mdbt_databricks_factory.clicreate-job\--job-name'<job name>'\--project-dir'<dbt project directory>'\--profiles-dir'<path to profiles directory>'\--git-provider'<git provider>'\--git-url'https://url.to/repo.git'\--git-branch'main'\--job-clustermy-cluster-name@path/to/cluster_config.json\--default-task-clustermy-cluster-name\--library'dbt-databricks>=1.0.0,<2.0.0'\--library'dbt-bigquery==1.3.0'\--pretty\path/to/dbt/manifest.json>workflow.jsonThis workflow will create a json file with the dbt workflow definition. You can then use it to create a new workflow in Databricks by for example post request like here:curl--fail-with-body-XPOST"${DATABRICKS_HOST}api/2.1/jobs/create"\-H"Authorization: Bearer${DATABRICKS_TOKEN}"\-H"Content-Type: application/json"\-d"@workflow.json">job_id.jsonecho"Job ID:"catjob_id.json
curl--fail-with-body-XPOST"${DATABRICKS_HOST}api/2.1/jobs/run-now"\-H"Authorization: Bearer${DATABRICKS_TOKEN}"\-H"Content-Type: application/json"\-d@job_id.json>run_id.jsonecho"Run ID:"catrun_id.json
curl--fail-with-body-XGET-G"${DATABRICKS_HOST}api/2.1/jobs/runs/get"\-H"Authorization: Bearer${DATABRICKS_TOKEN}"\-d"run_id=$(jq-r'.run_id'<run_id.json)">run_status.json

jq<run_status.jsonTo get more information about the command, run:python-mdbt_databricks_factory.clicreate-job--help
Package: dbt-datadict
We are the boutique analytics consultancy that turns disorganised data into real business value.Get in touchto learn more about how Tasman can help solve your organisations data challenges.dbt-datadictdbt-datadict is a CLI tool that provides helpful functions to improve the speed and efficiency of managing column-level documentation across large dbt projects.Key Features:Rapid creation of model yaml files, leveraging dbt-labs/codegen 💥 (no more copy/pasting from the terminal 🙌)In-place updates to model yaml on schema changes 🧙Consolidatation of column descriptions into a data dictionary 📓Keeps column descriptions in sync with a single command 🔃Installation ⏬Install dbt-datadict using```bash
$ python -m pip install dbt-datadict
```Getting Started 🚀Full user guide🧑‍🏫Command:generateThis command generates yaml files using the dbt-codegen package. Where it finds existing model yaml files, it will merge the full column lists. For missing models, it will create a separate model yaml file using the name provided.Warning ⚠️This command will only run in a valid dbt project with the dbt-labs/codegen dbt package installed.Usage:$datadictgenerate[-D<DIRECTORY>][-f<NAME>]Options:-D, --directory <DIRECTORY>: Directory to search for models. Default: 'models/'.-f, --file <NAME>: The yaml file to store new model configurations that aren't referenced in an existing yaml file.--sort: Triggers the generated YAML files to be sorted alphabetically (on by default).--unique-model-yaml: Creates one YAML for each model with the same name as the model.Command:applyThis command applies data dictionary updates to all model YAML files in the specified directory and its subdirectories.Usage:$datadictapply[-D<DIRECTORY>][-d<DICTIONARY>]Options:-D, --directory <DIRECTORY>: Directory to search for fields and apply the dictionary to. Default: 'models/'.-d, --dictionary <DICTIONARY>: Location of the dictionary file. Default: 'datadictionary.yml'.⚠️ Important Note ⚠️It is highly recommend to only use this library in a version controlled environment, such as git. Additionally, please ensure that you have backed up your model YAML files and data dictionary before applying any updates. The application modifies files in place and does not create backups automatically.Use this application responsibly and verify the updates before proceeding.ContributingWe encourage you to contribute to dbt Data Dictionary! Please check out ourContributing to dbt Data Dictionaryguide for guidelines about how to proceed.Licensedbt Data Dictionary is released under the GNU General Public License v3.0. SeeLICENSEfor details.
Package: dbt-dataops-starrocks
dbt-starrocksThis project isunder development.Thedbt-starrockspackage contains all of the code enabling dbt to work with a StarRocks database. For
more information on using dbt with StarRocks.Getting startedConfiguration your envs:Python: 3.7.4StarRocks: 2.4.0+DBT: 1.1.0Install thedbt-starrocksinto theplugindirectory, andpip install .Create your project:dbt initBasic Exampledbt seed properties(yml):Minimum configuration:config:
  distributed_by: ['id']Complete configuration:config:
  engine: 'OLAP'
  keys: ['id', 'name', 'some_date']
  table_type: 'PRIMARY'     //PRIMARY or DUPLICATE or UNIQUE
  distributed_by: ['id']
  buckets: 3                //default 10
  partition_by: ['some_date']
  partition_by_init: ["PARTITION p1 VALUES [('1971-01-01 00:00:00'), ('1991-01-01 00:00:00')),PARTITION p1972 VALUES [('1991-01-01 00:00:00'), ('1999-01-01 00:00:00'))"]
  properties: {"replication_num":"1", "in_memory": "true"}dbt run config(table/incremental):Minimum configuration:{{ config(materialized=var("materialized_var", "table"), distributed_by=['id'])}}
{{ config(materialized='incremental', distributed_by=['id']) }}Complete configuration:{{ config(materialized='table', engine='OLAP', buckets=32, distributed_by=['id'], properties={"in_memory": "true"}) }}
{{ config(materialized='incremental', engine='OLAP', buckets=32, distributed_by=['id'], properties={"in_memory": "true"}) }}Test Adapterconsultthe projectNoticeCreate table ascan only set engine='OLAP' and table_type='DUPLICATE'distributed_by is must
Package: dbt-db2fori
dbt-db2foriAdbtadapter for IBM's DB2 for i v7.2+. The connection to the warehouse is throughODBCand requires thatpyodbcis installed. All credits todbt-sqlserver adapteranddbt-ibmdb2projects that heavily inspired this adapter.Why this adapter?A similar adapterdbt-ibmdb2exists, however,dbt-ibmdb2uses theibm_dbPython package to connect to IBM DB2. This adapter connects to the warehouse usingpyodbcFeaturesThe following materializations are supported:IncrementalSnapshotViewTableEphemeral models have not been tested yet.InstallationUse pip to install:pipinstalldbt-db2foriAn exampleprofiles.ymlis:default:outputs:dev:type:db2_for_ithreads:4driver:IBMiAccessODBCDriversystem:systemusername:usernamepassword:passworddatabase:dbschema:schematarget:devTo report a bug or request a feature, open anissue
Package: dbt-devnull
dbt-devnullA do-nothing dbt adapter plugin.Following commands should be succesul if configuration is correct withou any database connectiondbt debugdbt docs generateInstallpip install dbt-devnullConfigurationA sample profile configurationtest:
  target: test
  outputs:
    test:
      type: devnull
      schema: test
      database: test
      username: user
      password: password
      host: localhost
      threads: 1
Package: dbtdoc
dbtdocCreate dbt document from SQL filesInstallpip install dbtdocFeaturesCurrently support document for followingmodelseedcommon macro and testmaterializationUsagePreparedbtdoc will extract the information from SQL comment block /* */ for each macro and test.A typical comment block will look like this:/*
This information will be used in dbt document.
Information insides dbt block is used to create dbt yml file 
```dbt
arguments:
  - name: arg01
    type: string
    description: the first argument
```
* any thing after `dbt` block will be ignored
*/By default, every macro, tests will be displayed in dbt document navigator.
Remove item from document by adding following setting./*
this will no be displayed in dbt doc
```dbt
docs:
   show: false
```
*/UsageDefault syntax is below:dbtdoc [-h] [-v] [-c] [-b] [-d DOC] [-u] [-o] [-p PREFIX] [-r] [-s SCHEMA] [-S] [-D DEBUG] [-T TARGET]By defaultdbtdocscans the default DBT project directory (consulting theDBT_PROJECT_DIRenvironment variable if set or the current directory if the environment variable is unset) and all of its sub-folders for sql files, creates 2 filedbt_schema.ymlanddocs.mdfor each folder (the names of the file could be changed by .dbtdoc)
Ifdbtdocfinds adbt_project.ymlfile in the target folder, it will only scan the folders defined bymodel-paths,macro-paths, andtest-pathsin that configuration file.Parameter-ocan be used to limitdbtdocto only process the root folder and ignore its sub-folders.Note: when error happens, use the command with-D DEBUGfor more details about the errors.ConfigurationA configuration file.dbtdocis searched in the current folder. A typical configuration file looks like this:schema_file: "dbt_schema.yml"
doc_file: "docs.md"
quote_string: trueIf the configuration does not exists, default values are used.SCHEMA_FILE = "dbt_schema.yml"
DOC_FILE = "docs.md"
QUOTE_STRING = FalseOtherThis project was inspired bydbt_docstring.
Package: dbt-doc-py
DBT Documentor (Python Version)Automatically generate docs for undocumented DBT models.Table of ContentsInstallationUsageDocumentationContributingLicenseInstallationTo install the package, run the following command:pipinstalldbt-doc-pyUsagePlace inside of the DBT project folder and run this command:dbt-doc-pyYou can execute it from other directories, this way:dbt-doc-py --working-directory /folder/DBTprojectTO-DO: Finish README file:DocumentationContributingLicenseThis project is licensed under the MIT License. See the LICENSE file for details.
Package: dbt-docstring
dbt_docstringDocstring dbt test & documentation in SQL fileWhat is it?dbt has a test and documentation feature where models/schema.yml is the
definition file. While this is already a big help for testing and
documentation in ELT, not being able to keep documentation in the source code
may cause more documentats out of sync with the source.dbtdocstr lets you write docment in a docstring style directly in .sql files.Installpip install dbt_docstringHow does it work?dbtdocstr command scans .sql files under dbt's models directories and look for
a block that begins with```dbtand end with```.
Inside the block you can write the content of the models section of schema.yml
corresponding to the current table as specified indbt document:Example (<dbt_root>/models/api_key_status.sql)/*
# API key status

This table lists the API keys with the status.

```dbt
columns:
  - name: api_key
    description: API key
    tests:
      - unique
  - name: enabled
    description: True if API key is enabled status
  - name: update_datetime
    description: Last update date and time
```
*/
SELECT
   api_key,
   enabled,
   update_datetime
FROM {{ ref('my_api_key_list') }}Then run:dbtdocstr <dbt_project_root_directory>These two files will be auto-generated from each .sql file in the dbt project:models/docs.md:# This file was auto-generated by dbtdocstr.
# Don't manually update.
# https://github.com/anelendata/dbt_docstring

{% docs api_key_status %}
# API key status

This table lists the API keys with the status.
{% enddocs %}models/schema.yml:# This file was auto-generated by dbtdocstr.
# Don't manually update.
# https://github.com/anelendata/dbt_docstring

version: 2
models:
  - name: api_key_status
    description: '{{ docs("api_key_status") }}'
    columns:
      - name: api_key
        description: API key
        tests:
          - unique
      - name: enabled
        description: True if API key is enabled status
      - name: update_datetime
        description: Last update date and time
  - name: ...To see the document generation, use dbt command:dbt docs generate
dbt docs serveNotesThe doc must be a SQL comment block comment that begins with '/*' and ends with '*/'The first comment block will be extracted.The dbt block is searched within the first comment block.Any text after the dbt block will be ignored.dbt's Docs Blocks feature can be used only for table & view description. Not column descriptions.dbtdocstr --backup <dbt_root_directory>to create backup files of schema.yml and docs.yml if they exsit.Original repositoryhttps://github.com/anelendata/dbt_docstringAbout this projectThis project is developed by
ANELEN and friends. Please check out the ANELEN'sopen innovation philosophy and other projectsCopyright © 2020~ Anelen Co., LLC
Package: dbt-dremio
[dbt](https://www.getdbt.com/)enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.dbt is the T in ELT. Organize, cleanse, denormalize, filter, rename, and pre-aggregate the raw data in your warehouse so that it’s ready for analysis.## dbt-dremio version 1.5.1Thedbt-dremiopackage contains all of the code enabling dbt to work with [Dremio](https://www.dremio.com/). For more information on using dbt with Dremio, consult [the docs](https://docs.getdbt.com/reference/warehouse-profiles/dremio-profile).The dbt-dremio package supports both Dremio Cloud and Dremio Software (versions 22.0 and later).Version 1.5.1 of the dbt-dremio adapter is compatible with dbt-core versions 1.2.0 to 1.5.*.> Prior to version 1.1.0b, dbt-dremio was created and maintained by [Fabrice Etanchaud](https://github.com/fabrice-etanchaud) on [their GitHub repo](https://github.com/fabrice-etanchaud/dbt-dremio). Code for using Dremio REST APIs was originally authored by [Ryan Murray](https://github.com/rymurr). Contributors in this repo are credited for laying the groundwork and maintaining the adapter till version 1.0.6.5. The dbt-dremio adapter is maintained and distributed by Dremio starting with version 1.1.0b.## Getting started[Install dbt-dremio](https://docs.getdbt.com/reference/warehouse-setups/dremio-setup)
-   Version 1.5.1 of dbt-dremio requires dbt-core >= 1.2.0 and <= 1.5.*. Installing dbt-dremio will automatically upgrade existing dbt-core versions earlier than 1.2.0 to 1.5.*, or install dbt-core v1.5.0 if no version of dbt-core is found.Read the [introduction](https://docs.getdbt.com/docs/introduction/) and [viewpoint](https://docs.getdbt.com/docs/about/viewpoint/)## Join the dbt CommunityBe part of the conversation in the [dbt Community Slack](http://community.getdbt.com/)Read more on the [dbt Community Discourse](https://discourse.getdbt.com)## Reporting bugs and contributing codeOpen bugs and feature requests can be found at [dbt-dremio’s GitHub issues](https://github.com/dremio/dbt-dremio/issues).Want to report a bug or request a feature? Let us know by on [Slack](https://getdbt.slack.com/archives/C049G61TKBK), or opening [an issue](https://github.com/dremio/dbt-dremio/issues/new)Want to help us build dbt-dremio? Check out the [Contributing Guide](https://github.com/dremio/dbt-dremio/blob/main/CONTRIBUTING.md).## Code of ConductEveryone interacting in the dbt-dremio project’s codebases, issue trackers, chat rooms, and mailing lists is expected to follow the [dbt-dremio Code of Conduct](https://github.com/dremio/dbt-dremio/blob/main/CODE_OF_CONDUCT.md).
Package: dbtease
dbteasedbtease is a tool for sanely testing and deployinglarge multi-schema dbt repositories in a modular way.dbt+ease=dbteaseDevelopment StatusThis tool is in very early pre-alpha status,we actively recommend that you DO NOT USE IT YET.You have been warnedAmbitiondbtease handles (orwill handle):atomic swapshandling of full refreshes and incremental refreshesCI deploymentmanifest managementeasy periodic refreshes on multiple schedules using a simple CLI command.Config is managed using a yaml file in the dbt project.CLI referencedbtease status: Outputs the current commit and deployment status.dbtease deploy: Deploy a new version of your project.dbtease refresh: Refresh the parts of your project which need refreshing.dbtease test: Test your changes against the currently deployed version of your project.Development RoadmapThese elements are not currently supported but explcitly planned:Stashing the logs of certain commands for later access.Having more detailed and reliable error alerting when steps fail.Handling dbt snapshots and exposure checking.ChangelogAll notable changes to this project will be documented in this file.The format is based onKeep a Changelog,
and this project adheres toSemantic Versioning.No Existing Releases
Package: dbteasy
DBTeasyClique aquipara ler esse artigo em português.This project usesInvoke (pythonictask runner) as a libraryThe main purpose ofdbteasyis to provide an alternative CLI to work withdbt corereducing the time needed to execute repetitive commandsRequirementsdbt core installed and with version history using gitHow to install dbtHow to install gitInstallationInstalldbteasyand check the version$pipinstalldbteasy
$dbteasy--versionMain commands$dbteasy--list# List available commands$dbteasyrun-changed# Run dbt run only for newly modified models$dbteasydocs# Compile documentation and launch webserver on port 8000$dbteasyrefresh# dbt clean ; dbt deps ; dbt seed
Package: dbt-ensure-tag
dbt-ensure-tagChecks dbt models for existence of a tag(s)Installationpip install dbt-ensure-tagUsagedbt-ensure-tag [filenames]pre-commit configrepos:
-   repo: https://github.com/j-clemons/dbt-ensure-tag
    rev: v0.0.3
    hooks:
    -   id: dbt-ensure-tag
Package: dbterd
dbterdCLI to generate Diagram-as-a-code file (DBML,Mermaid,PlantUML,GraphViz,D2) from dbt artifact files (required:)pipinstalldbterd--upgradeVerify installation:dbterd--versionQuick examine with existing samplesClick me# select all models in dbt_restodbterdrun-adsamples/dbtresto# select all models in dbt_resto, Select multiple dbt resourcesdbterdrun-adsamples/dbtresto-rtmodel-rtsource# select only models in dbt_resto excluding stagingdbterdrun-adsamples/dbtresto-smodel.dbt_resto-nsmodel.dbt_resto.staging# select only models in schema name mart excluding stagingdbterdrun-adsamples/dbtresto-sschema:mart-nsmodel.dbt_resto.staging# select only models in schema full name dbt.mart excluding stagingdbterdrun-adsamples/dbtresto-sschema:dbt.mart-nsmodel.dbt_resto.staging# other samplesdbterdrun-adsamples/fivetranlog
dbterdrun-adsamples/fivetranlog-rtmodel-rtsourcedbterdrun-adsamples/facebookad
dbterdrun-adsamples/facebookad-rtmodel-rtsourcedbterdrun-adsamples/shopify-swildcard:*shopify.shopify__*
dbterdrun-adsamples/shopify-rtmodel-rtsourcedbterdrun-adsamples/dbt-constraints-a"test_relationship:(name:foreign_key|c_from:fk_column_name|c_to:pk_column_name)"# your own sample without commiting to repodbterdrun-adsamples/local-rtmodel-rtsourceQuick DEMOCheckQuick Demoout! And, following is the sample result usingdbdocs:Contributing ✨If you've ever wanted to contribute to this tool, and a great cause, now is your chance!See the contributing docsCONTRIBUTINGfor more information.Finally, super thanks to ourContributors:
Package: dbtestpackage01
# dbtestpackage01
Just a package that I post on Pypi – it’s not  a big deal.[![Build Status](https://travis-ci.com/boybluepiano/dbtestpackage01.svg?branch=main)](https://travis-ci.com/boybluepiano/dbtestpackage01)
[![codecov](https://codecov.io/gh/boybluepiano/dbtestpackage01/branch/main/graph/badge.svg?token=YYNMU5QAB1)](https://codecov.io/gh/boybluepiano/dbtestpackage01)
[![Updates](https://pyup.io/repos/github/boybluepiano/dbtestpackage01/shield.svg)](https://pyup.io/repos/github/boybluepiano/dbtestpackage01/)
[![Python 3](https://pyup.io/repos/github/boybluepiano/dbtestpackage01/python-3-shield.svg)](https://pyup.io/repos/github/boybluepiano/dbtestpackage01/)Instalar o pipenv:`console pip install pipenv `Criar ambiente virtual:`console pipenv install `Instalar dependêcias:`console pipenv sync `Instalar dependêcias de desenvolvimento:`console pipenv sync-d`Instalar o pacote teste:`console pip install dbtestpackage01 `