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CLI for creating databases for Data Quality Dashboards.
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Data Quality CLI

A command line tool that assesses the quality of a set of data sources (e.g.: CSV files of open data published by a government).

What's it about?

The dq (alias: dataquality) CLI is a tool to create and manage a Data Package from a given source of data that can be used by Data Quality Dashboard. The quality assessment is done using GoodTables and can be configured.

The proposed workflow is this:

  • An administrator creates a folder for a given project which will be equivalent to a data package.
  • The administrator runs the dq init command to create templates for the configuration file
    and the datapackage.json file along with the folder structure.
  • The administrator updates the configuration file to reflect the structure of the data package and optionally to configure the quality assessment.
  • The administrator updates the datapackage.json file with information specific to the project and other customizations.
  • The administrator creates a source_file and a publisher_file:
  • The administrator runs the validation over the set of sources.
  • The data is managed in a git repository (or other version control system), which the administrator has locally
  • The administrator deploys the data package to a central data repository (ex: GitHub)
  • The administrator updates the configuration of the corresponding Data Quality Dashboard instance
  • The administrator, or possibly content editor, occasionally updates the source_file file in the data directory with new data
  • Periodically (once a month, once a quarter), the administrator runs dq run /path/to-config.json --deploy. This builds a new set of results for the data, and deploys the updated data back to the central data repository
  • Since Data Quality Dashboard is a pure client-side application, as soon as updated data is deployed, the app will start working with the updated data.


pip install git+


dq --help


Before starting building the database, it is recommended that you run:

dq init --folder_path /path/to/future/datapackage

This command will potentially spare you some effort and create a dq_config.json file with the default configuration for Data Quality CLI, a datapackage.json with the default info about the data package and schemas for all the required resources, a data folder that will be used to store the database and a fetched folder that will store the fetched sources. If you'd like to change the names of these folder or other configuration options, you can make a dq_config.json file before running the command. The command will leave your config file as it is and create the others according to your configuration.

After running it, you should review and update your dq_config and datapackage.json with values specific to your project.


Generic command:

dq generate generator_name http://endpoint_to_data_sources

There is currently one built-in generator for CKAN instances. Ex: In the example below, we generate a database from

dq generate ckan

By default, it will include only CSV and excel(XLS, XLSX) files. If you want to change that use the --file_type option. In the example below, we ask for CSV and TXT:

dq generate ckan  --file_type csv --file_type txt

If you want to built a custom Generator, just inherit and overwrite the methods of data_quality.generators.BaseGenerator class. To load your custom generator class you need to provide the path to it so that it can be imported via importlib.import_module. You can either provide it in the config, or by using the --generator_class_path option:

dq generate custom_generator_name endpoint --generator_class_path mymodule.MyGenerator

If no config file is provided, the generator will use the default configuration creating the files in the folder where the command is executed. If you want to change that, use the --config_file_path option:

dq generate generator_name endpoint --config_file_path path/to/config


dq run /path/to/config.json --deploy

Runs a data quality assessment on all data sources in a data repository.

  • Writes aggregated results to the results.csv.
  • Writes run meta data to the run.csv.
  • If --deploy is passed, then also commits, tags and pushes the new changes back to the data repositories central repository.


dq deploy /path/to/config.json
### Configuration

Structure of json config

  # folder that contains the source_file and publisher_file
  "data_dir": "data",

  # folder that will store each source as local cache
  "cache_dir": "fetched",

  # file that will contain the result for each source
  "result_file": "results.csv",

  # file  that will contain the report for each collection of sources
  "run_file": "runs.csv",

  # file containing the collection of sources that will be analyzed
  "source_file": "sources.csv",

  # file containing the publishers of the above mentioned sources
  "publisher_file": "publishers.csv",

  # will contain the results for each publisher
  "performance_file": "performance.csv",

  "remotes": ["origin"],
  "branch": "master",
  # name and path to custom generator (this name should be used when executing the generate command)
  "generator": {"my_generator_name": "my_module.MyGenerator" },
  # whether or not to include timeliness as a dimension of quality assessment
  "assess_timeliness": false,
  # timeliness options:
  "timeliness": {
    # columns from source_file that should be checked for period detection
    "timeliness_strategy": ["column1", "column2"],
    # whether Data Quality CLI should detect period or expect it to be provided
    "extract_period": false,
    # maximum percent of sources with empty period allowed
    "max_empty_relevance_period": 10,
    # when date is ambiguous, which order should be preffered
    "date_order": "DMY",
    # how long after the period_id range is the data still considered timely (in months)
    "timeliness_period": 1
  # options for GoodTables ("")
  "goodtables": {

    # set base url for the report links
    "goodtables_web": "",

    "arguments": {

      # options for pipeline ("")
      "pipeline": {

        # what processors will analyze every pipeline
        "processors": ["structure", "schema"],

        # specify encoding for every pipeline 
          (use this if all the files have the same encoding)
        "encoding": "ISO-8859-2",

         # pass options to procesors
        "options": {
          "schema": {"case_insensitive_headers": true}

      # options for batch ("")
      "batch": {

        # column from source_file containing path/url to data source
        "data_key": "data",

        # column from source_file containing path/url to schema
        "schema_key": "schema",

        # column from source_file containing file format (csv, xls)
        "format_key": "format",

        # column from source_file containings file encoding
          (use this if you want to specify encoding for each source separately)
        "encoding_key": "encoding",

        # time in seconds to wait between pipelines
        "sleep": 2,

        # execute something after the analysis of a batch is finished
        "post_task": "",

        # execute something after the analysis  of a pipeline is finished
        "pipeline_post_task": "",
#### Default config
    "data_dir": "current_working_directory/data",
    "cache_dir": "current_working_directory/fetched",
    "result_file": "results.csv",
    "run_file": "runs.csv",
    "source_file": "sources.csv",
    "publisher_file": "publishers.csv",
    "performance_file": "performance.csv",
    "remotes": ["origin"],
    "branch": "master",
    "assess_timeliness": false,
    "goodtables": {
        "goodtables_web": "",
        "arguments": {
            "pipeline": {},
            "batch": {
                "data_key": "data"
#### Quality assessment configuration

Currently, Data Quality CLI assesses the quality of a file based on its structure and by comparing its contents against a schema. This is done using the built-in processors (a.k.a. validators) in GoodTables.

Note: If the files are compressed, they cannot be found at the specified path or the path returns an HTML page, they will be scored 0.

If you want to add other criteria for quality assessment, you can create a custom processor for GoodTables. Then include the name of your custom processor in the list passed to the processors parameter from data quality config: "processors": ["structure", "schema", "custom_processor"]. You can also exclude processors that you don't want by removing them from the list.

Structure Processor:

Checks the structure of a tabular file.

Ex: blank or duplicate rows, rows that have more/less columns than the header, bad formatting etc.

Options and their defaults:

  • ignore_empty_rows: false - Should empty rows be considered errors or just ignored?
  • ignore_duplicate_rows: false - Should duplicate rows be considered errors or just ignored?
  • ignore_empty_columns: false
  • ignore_duplicate_columns: false
  • ignore_headerless_columns: false - Should values in a row that don't correspond to a column be ignored?
  • empty_strings: None - A list/set of what should be considered empty string, otherwise only '' will
Schema Processor:

Compares the content of a tabular file against a Json Table Schema. You have the following options for the schema:

  1. Provide a path to the schema for each source in source_file and set the "schema_key" to the name of the column that contains it
  2. Let GoodTables infer the schema for each file from its first few lines (less transparent).

Options and defaults:

  • ignore_field_order: true - Should columns have the same order as in the schema?
  • infer_schema: false - Should the schema be infered? (see above)
  • process_extra_fields: false - Should fields that are not present in the schema be infered and checked?
  • case_insensitive_headers: false - Should headers be matched with the equivalent field names from schema regardless of case?

Note: If you use the schema processor but you don't provide a schema to compare against, the files will be evaluated as having no errors.


To exemplify how using different processors influences the quality assessment, we set up several versions of the same dataset: UK public spend over £25000.

Here is a dashboard whose data quality database is assessed only on structure. You can find the database and configuration in this repository.

This alternative version uses both structure and schema processors, comparing each file agaist the spend publishing schema. It is the official configuration, with its corresponding repository here.

Lastly, here is the less predictible version that uses both structure and schema, but it compares files agaist inferred schemas (i.e. using infer_schema: true).Corresponding database repostory here.


An optional criteria for quality assessment is the timeliness of data publication. We define timeliness as the difference in months between when the data source should have been published and when it was published. If you want to include timeliness in the quality assessment set assess_timeliness: true.

"When the data should have been published" is what we call period_id and reffers to the period of time the data is relevant for. There are two options for providing period_id:

  • You can provide it for each source and include the column name in the config: "timeliness": {"timeliness_strategy": ["column_name"]}

  • Let Data Quality CLI detect the period from certain fields in source_file that are likely to contain it:

      "timeliness": {
          "extract_period": true,
          "timeliness_strategy": ["column1", "column2"]

    The order will tell Data Quality CLI which field has priority. In this example, it will try to find something in column1 and move to column2 only if nothing was found. You can specify as many fields as you want. Please note that if the date is ambiguous, Data Quality CLI will prefer the format dd-mm-yyyy. You can change that with the date_order option. For example, "timeliness": {"date_order": "MDY"} will change the preffered order to mm-dd-yyyy.

Regardless of the method you choose, Data Quality CLI will parse the fields you provided in timeliness_strategy, try to extract a period out of them and write it in the source_file.

NOTE: If you provide a period_id it will be parsed and replaced by one with the same dates but a different format used thoughout the CLI.

If no period_id can be extracted for more than 10% of the sources, Data Quality CLI will abort timeliness assessment and raise an error. If you want to change that, set max_empty_relevance_period to the desired percent. If the precent of sources laking period_id doesn't exceed max_empty_relevance_period, the value in the created_at column will be used as period_id for them.

By default, a data source is considered timely if no more than a month has passed from the end of period_id until it was published (created_at). You can change that with the timeliness_period option by providing a different number of months. Ex: "timeliness": {"timeliness_period": 3} means that the data source is timely if no more than 3 months passed since the end of period_id. The quality score will decrease for every additional month after the period considered timely.


Data Quality CLI expects the following structure of the project folder, where the names of files and folders are the ones defined in the json config given to dq run:

    │   source_file
    │   publisher_file
    │   run_file
    │   result_file
    │   performance_file

The datapackage.json file is required in order to make the project a valid Data Package. If you use the dq init command, it will be automatically generated for you from the the default datapackage. This file will be needed thoughout the app so you'll need to have it. Take a look over the Data Package specification if you'd like to customize the it for your project.

Warning: The datapackage.json file is extensively used thoughtout Data Quality CLI and the Data Quality Dashboard. To make sure it is kept in sync with the database that it describes, several checks are performed at different steps. While you are free to customize your database by using custom generators and extra fields, you have to make sure that the fields required by Data Quality CLI to perform it's tasks are present.

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