Package: dqops
DQOps Data Quality Operations CenterDQOps is an DataOps friendly data quality monitoring platform with customizable data quality checks and data quality dashboards.
DQOps comes with around 150 predefined data quality checks which helps you monitor the quality of your data.Key featuresIntuitive graphical interface and access via CLISupport of a number of different data sources: BigQuery, Snowflake, PostgreSQL, Redshift, SQL Server and MySQL~150 build-in table and column checks with easy customizationTable and column-level checks which allows writing your own SQL queriesDaily and monthly date partition testingData grouping by up to 9 different data grouping levelsBuild-in schedulingCalculation of data quality KPIs which can be displayed on multiple built-in data quality dashboardsData quality incident managementInstallationTo use DQOps you need:Python version 3.8 or greater (for details seePython's documentationanddownload sites).Ability to install Python packages with pip.DQOps is available onPyPi repository.To install DQOps via pip manager just runpython -m pip install --user dqopsIf you prefer to work with the source code, just clone our GitHub repositoryhttps://github.com/dqops/dqoand runRun dqops module to finalize the installation.python -m dqopsCreate DQOps user home folder.After installation, you will be asked whether to initialize the DQO user's home folder in the default location. Type Y to create the folder.The user's home folder locally stores data such as sensor readouts and the data quality check results, as well as data source configurations.You can learn more about data storage here.Login to DQOps Cloud.To use DQOps features, such as storing data quality definitions and results in the cloud or data quality dashboards, you
must create a DQOps cloud account.After creating a user's home folder, you will be asked whether to log in to the DQO cloud. After typing Y, you will be
redirected tohttps://cloud.dqops.com/registration, where you can create a new account, use Google single sign-on (SSO) or log in if you already have an account.During the first registration, a unique identification code (API Key) will be generated and automatically retrieved by DQO application.
The API Key is now stored in the configuration file.Open the DQOps User Interface Console in your browser by CTRL-clicking on the link displayed on the command line (for examplehttp://localhost:8888)
or by copying the link.What you can do with DQOpsDQOps is designed as the primary platform for data quality teams, and for all data engineering or data science
teams who want to apply data quality for their data platforms.The following list shows selected use cases, with examples and best practices.Thedefinition of data quality KPIsdescribes the formulas used by DQOps to calculate a data quality score, used to measure the quality of data sourcesYou will learn how tocreate custom data quality dashboardsusing a custom connector for Looker Studio provided by DQOps. Your data quality dashboards will show the data quality
results organized in a format that is easy to understand by your business sponsors.Look at thecategories of data quality checksthat are supported by DQOps.Learn how todetect timeliness and freshnesswith DQOps.Learn how tomeasure data quality incrementallyusing time partitioned checks that are a unique feature of DQOps, allowing the analysis of financial data, append-only data,
or very big tables at a terabyte or petabyte scale.Similar data quality issues are grouped into data quality incidents,
learn howgrouping data quality issues to incidents,
and how to receive notifications using Slack or passed to a webhook.The following examples also show the whole process of configuring data quality checks, both using YAML files,
or using theDQOps user interface.Learn how todetect database and table availability issues.Learn how to configure data volume checks todetect empty or incomplete tables.Detect ifcolumns contain only accepted values.Detect duplicate values in columns bymeasuring the percentage of duplicates.Validate values in text columns using a regular expression todetect values that are invalid emails.Use schema drift data quality checks todetect table schema changes,
such as missing columns, column order change, column data type change, or just that new columns were added or removed from a table.DQOps clientYou can integrate DQOps into data pipelines and ML pipelines by calling aPython client for DQOps.
Install the client as a Python package:python -m pip install --user dqopsThedqopspackage contains a remote client that can connect to a DQOps instance and perform all operations supported by the user interface.
The DQOps client could be used inside data pipelines or data preparation code to verify the quality of tables.You can use the unauthenticated client to connect to a local DQOps instance from your data pipeline code. First, create the client object.fromdqopsimportclientdqops_client=client.Client(base_url="http://localhost:8888")Alternatively, if you are connecting to a production instance of DQOps that has authentication
enabled, you have to open the user's profile screen in DQOps and generate your DQOps API Key.
Then take the key and use it as the token, when creating anAuthenticatedClientinstead.fromdqopsimportclientdqops_client=client.AuthenticatedClient(base_url="http://localhost:8888",token="Your DQO API Key")Now, you can call operations on DQOps. The following code shows how to execute data quality checks
on data sources that are already registered in DQOps.fromdqops.client.api.jobsimportrun_checksfromdqops.client.modelsimportCheckSearchFilters,\RunChecksParametersrequest_body=RunChecksParameters(check_search_filters=CheckSearchFilters(column='sample_column',column_data_type='string',connection='sample_connection',full_table_name='sample_schema.sample_table',enabled=True))check_results=run_checks.sync(client=dqops_client,json_body=request_body)Therun_checksoperation returns a summary of executed data quality checks and the highest
data quality issue severity level. In the following example, the most severe issue was at anerrorseverity level.{"jobId":{"jobId":123456789,"createdAt":"2023-10-11T13:42:00Z"},"result":{"highest_severity":"error","executed_checks":10,"valid_results":7,"warnings":1,"errors":2,"fatals":0,"execution_errors":0},"status":"finished"}Learn more about the DQOps Python client in theDQOps REST API clientreference
documentation that shows Python code examples for every operation supported by the client.DocumentationFor full documentation with guides and use cases, visithttps://dqops.com/docs/Thegetting startedguide shows how to start using DQOps.Also, read theDQOps conceptguide to know how DQOps operates,
and how to configure data quality checks.Contact and issuesIf you find any issues with the tool, just post it here:https://github.com/dqops/dqo/issuesor contact us viahttps://dqops.com/