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Version: 0.15.50

How to configure a RuntimeDataConnector

This guide demonstrates how to configure a RuntimeDataConnector and only applies to the V3 (Batch Request) API. A RuntimeDataConnector allows you to specify a BatchA selection of records from a Data Asset. using a Runtime Batch RequestProvided to a Datasource in order to create a Batch., which is used to create a Validator. A ValidatorUsed to run an Expectation Suite against data. is the key object used to create ExpectationsA verifiable assertion about data. and ValidateThe act of applying an Expectation Suite to a Batch. datasets.

Prerequisites: This how-to guide assumes you have:

A RuntimeDataConnector is a special kind of Data Connector that enables you to use a RuntimeBatchRequest to provide a Batch's data directly at runtime. The RuntimeBatchRequest can wrap an in-memory dataframe, a filepath, or a SQL query, and must include batch identifiers that uniquely identify the data (e.g. a run_id from an AirFlow DAG run). The batch identifiers that must be passed in at runtime are specified in the RuntimeDataConnector's configuration.

Steps​

1. Instantiate your project's DataContext​

Import these necessary packages and modules:

from ruamel import yaml

import great_expectations as gx
from great_expectations.core.batch import RuntimeBatchRequest

context = gx.get_context()

2. Set up a Datasource​

All of the examples below assume you’re testing configuration using something like:

datasource_yaml = """
name: taxi_datasource
class_name: Datasource
execution_engine:
  class_name: PandasExecutionEngine
data_connectors:
  <DATACONNECTOR NAME GOES HERE>:
    <DATACONNECTOR CONFIGURATION GOES HERE>
"""
context.test_yaml_config(yaml_config=datasource_config)

If you’re not familiar with the test_yaml_config method, please check out: How to configure Data Context components using test_yaml_config

3. Add a RuntimeDataConnector to a Datasource configuration​

This basic configuration can be used in multiple ways depending on how the RuntimeBatchRequest is configured:

datasource_yaml = r"""
name: taxi_datasource
class_name: Datasource
module_name: great_expectations.datasource
execution_engine:
  module_name: great_expectations.execution_engine
  class_name: PandasExecutionEngine
data_connectors:
  default_runtime_data_connector_name:
    class_name: RuntimeDataConnector
    batch_identifiers:
      - default_identifier_name
"""

Once the RuntimeDataConnector is configured you can add your DatasourceProvides a standard API for accessing and interacting with data from a wide variety of source systems. using:

context.add_datasource(**datasource_config)

Example 1: RuntimeDataConnector for access to file-system data:​

At runtime, you would get a Validator from the Data ContextThe primary entry point for a Great Expectations deployment, with configurations and methods for all supporting components. by first defining a RuntimeBatchRequest with the path to your data defined in runtime_parameters:

batch_request = RuntimeBatchRequest(
    datasource_name="version-0.15.50 taxi_datasource",
    data_connector_name="version-0.15.50 default_runtime_data_connector_name",
    data_asset_name="version-0.15.50 YOUR_MEANINGFUL_NAME",  # This can be anything that identifies this data_asset for you
    runtime_parameters={"path": "PATH_TO_YOUR_DATA_HERE"},  # Add your path here.
    batch_identifiers={"default_identifier_name": "YOUR_MEANINGFUL_IDENTIFIER"},
)

Next, you would pass that request into context.get_validator:

validator = context.get_validator(
    batch_request=batch_request,
    create_expectation_suite_with_name="version-0.15.50 MY_EXPECTATION_SUITE_NAME",
)
print(validator.head())

Example 2: RuntimeDataConnector that uses an in-memory DataFrame​

At runtime, you would get a Validator from the Data Context by first defining a RuntimeBatchRequest with the DataFrame passed into batch_data in runtime_parameters:

import pandas as pd
path = "PATH_TO_YOUR_DATA_HERE"
df = pd.read_csv(path)

batch_request = RuntimeBatchRequest(
    datasource_name="version-0.15.50 taxi_datasource",
    data_connector_name="version-0.15.50 default_runtime_data_connector_name",
    data_asset_name="version-0.15.50 YOUR_MEANINGFUL_NAME",  # This can be anything that identifies this data_asset for you
    runtime_parameters={"batch_data": df},  # Pass your DataFrame here.
    batch_identifiers={"default_identifier_name": "YOUR_MEANINGFUL_IDENTIFIER"},
)

Next, you would pass that request into context.get_validator:

validator = context.get_validator(
    batch_request=batch_request,
    expectation_suite_name="version-0.15.50 MY_EXPECTATION_SUITE_NAME",
)
print(validator.head())

Additional Notes​

To view the full script used in this page, see it on GitHub: