DQX Engine

To perform data quality checking with DQX, you must create a DQEngine object. The engine requires a Databricks workspace client for authentication and interaction with the Databricks workspace.

When running the code on a Databricks workspace, the workspace client is automatically authenticated, whether DQX is used in a notebook, script, or job/workflow. You only need the following code to create the workspace client if you run DQX on Databricks workspace:

Python

from databricks.sdk import WorkspaceClient
from databricks.labs.dqx.engine import DQEngine

ws = WorkspaceClient()
dq_engine = DQEngine(ws)

For external environments, such as CI servers or local machines, you can authenticate to Databricks using any method supported by the Databricks SDK. For detailed instructions, refer to the default authentication flow. If you're using Databricks configuration profiles or Databricks-specific environment variables for authentication, you can create the workspace client without needing to provide additional arguments:

ws = WorkspaceClient()

The DQEngine is initialized by default with standard Spark Session available in the current environment. If you need to use a custom Spark Session, such as from Databricks Connect you can pass it as an argument when creating the DQEngine instance:

from databricks.connect import DatabricksSession

spark = DatabricksSession.builder.getOrCreate()
dq_engine = DQEngine(ws, spark)

For local execution without a Databricks workspace, please refer to the local testing section.

DQX engine methods

The following table outlines the available methods of the DQEngine and their functionalities:

Available DQX engine methods

Method	Description	Arguments	Supports local execution
apply_checks	Applies quality checks to the DataFrame and returns a DataFrame with result columns.	df: DataFrame to check; checks: List of checks to the DataFrame. Each check is an instance of the DQRule class; ref_dfs: dictionary that maps arbitrary names to DataFrames, used in dataset-level checks such as foreign_key or custom dataset-level checks	Yes
apply_checks_and_split	Applies quality checks to the DataFrame and returns valid and invalid (quarantine) DataFrames with result columns.	df: DataFrame to check; checks: List of checks to apply to the DataFrame. Each check is an instance of the DQRule class; ref_dfs: dictionary that maps arbitrary names to DataFrames, used in dataset-level checks such as foreign_key or custom dataset-level checks	Yes
apply_checks_by_metadata	Applies quality checks defined as a dictionary to the DataFrame and returns a DataFrame with result columns.	df: DataFrame to check. checks: List of dictionaries describing checks; custom_check_functions: Optional dictionary with custom check functions (e.g., globals() of the calling module); ref_dfs: dictionary that maps arbitrary names to DataFrames, used in dataset-level checks such as foreign_key or custom dataset-level checks	Yes
apply_checks_by_metadata_and_split	Applies quality checks defined as a dictionary and returns valid and invalid (quarantine) DataFrames.	df: DataFrame to check; checks: List of dictionaries describing checks; custom_check_functions: Optional dictionary with custom check functions (e.g., globals() of the calling module); ref_dfs: dictionary that maps arbitrary names to DataFrames, used in dataset-level checks such as foreign_key or custom dataset-level checks	Yes
validate_checks	Validates the provided quality checks to ensure they conform to the expected structure and types.	checks: List of checks to validate; custom_check_functions: Optional dictionary of custom check functions that can be used.	Yes
get_invalid	Retrieves records from the DataFrame that violate data quality checks (records with warnings and errors).	df: Input DataFrame.	Yes
get_valid	Retrieves records from the DataFrame that pass all data quality checks.	df: Input DataFrame.	Yes
load_checks_from_local_file	Loads quality rules from a local file (supports YAML and JSON).	path: Path to a file containing the checks.	Yes
save_checks_in_local_file	Saves quality rules to a local file in YAML format.	checks: List of checks to save; path: Path to a file containing the checks.	Yes
load_checks_from_workspace_file	Loads checks from a file (JSON or YAML) stored in the Databricks workspace.	workspace_path: Path to the file in the workspace.	No
load_checks_from_installation	Loads checks from the workspace installation configuration file (checks_file field).	run_config_name: Name of the run config to use; product_name: Name of the product/installation directory; assume_user: If True, assume user installation.	No
save_checks_in_workspace_file	Saves checks to a file (YAML) in the Databricks workspace.	checks: List of checks to save; workspace_path: Destination path for the checks file in the workspace.	No
save_checks_in_installation	Saves checks to the installation folder as a YAML file.	checks: List of checks to save; run_config_name: Name of the run config to use; assume_user: If True, assume user installation.	No
load_run_config	Loads run configuration from the installation folder.	run_config_name: Name of the run config to use; assume_user: If True, assume user installation.	No
save_results_in_table	Save results of quality checking to delta table(s).	output_df: (optional) Dataframe containing the output data; quarantine_df: (optional) Dataframe containing the output data; output_table: name of the output table; quarantined_table: name of the quarantine table; output_table_mode: Output mode for writing to the output table (default is 'append'); quarantined_table_mode: Output mode for writing to the quarantine table (default is 'append'); run_config_name: Name of the run config to use; assume_user: If True, assume user installation; output_table_options: Additional options for writing to the output table; quarantine_table_options: additional options for writing to the quarantine table; trigger: Trigger options for streaming DataFrames, e.g. {"availableNow": True}	No

The 'Supports local execution' in the above table indicates which methods can be used for local testing without a Databricks workspace (see the usage in local testing section).