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Data Connectors#

A Data Connector facilitates access to an external data store, such as a database, filesystem, or cloud storage. The Data Connector can inspect an external data store to identify available Batches, build Batch Definitions using Batch Identifiers, and translate Batch Definitions to Execution Engine-specific Batch Specs.

API note

Data Connectors replace Batch Kwargs Generators in the updated Great Expectations API.

There are several types of Data Connectors in Great Expectations, such as the ConfiguredAssetFilesystemDataConnector , DatabaseDataConnector, and RuntimeDataConnector.

Each Data Connector holds configuration for connecting to a different type of external data source, and can connect to and inspect that data source.

For example, a ConfiguredAssetFilesystemDataConnector could be configured with the root directory for files on a filesystem or bucket and prefix used to access files from a cloud storage environment.

The simplest RuntimeDataConnector may simply store lookup information about Data Assets to facilitate running in a pipeline where you already have a DataFrame in memory or available in a cluster. However, Great Expectations makes it possible to configure Data Connectors that offer stronger guarantees about reproducibility, sampling, and compatibility with other tools.

The Data Connector uses Batch Definitions to identify the Batches available in a Data Asset.

A Batch is what differentiates a specific set of data that is part of a Data Asset. The Batch uniquely identifies a subset of data based on the purpose for which you validate, such as the most recent delivery. The ConfiguredAssetFilesystemDataConnector can use a regex string to match files and produce named match groups that define unique Batches. Data Connectors use Sorters to help define a unique order for Batches, such as sorting files by date or alphabetically.


The main goal of Data Connectors is to provide useful guarantees about Batches, for example ensuring that they cover data from non-overlapping date ranges. A Batch is a combination of data and metadata.


The Datasource is responsible for orchestrating the building of a Batch, using the following components:

new_batch = Batch(    data = batch_data,    batch_request=batch_request    batch_definition=batch_definition,    batch_spec=batch_spec,    batch_markers=batch_markers,)

Typically, a user need specify only the BatchRequest. The BatchRequest is a description of what data Great Expectations should fetch, including the name of the Data Asset and other identifiers (see more detail below).

A Batch Definition includes all the information required to precisely identify a set of data from the external data source that should be translated into a Batch. One or more BatchDefinitions are always returned from the Datasource, as a result of processing the BatchRequest. A BatchDefinition includes several key components:

  • Batch Identifiers: contains information that uniquely identifies a specific batch from the Data Asset, such as the delivery date or query time.
  • Engine Passthrough: contains information that will be passed directly to the Execution Engine as part of the Batch Spec.
  • Sample Definition: contains information about sampling or limiting done on the Data Asset to create a Batch.
Best practice

We recommend that you make every Data Asset Name unique in your Data Context configuration. Even though a Batch Definition includes the Data Connector Name and Execution Environment Name, choosing a unique data asset name makes it easier to navigate quickly through Data Docs and ensures your logical data assets are not confused with any particular view of them provided by an Execution Engine.

A Batch Spec is an Execution Engine-specific description of the Batch. The Data Connector is responsible for working with the Execution Engine to translate the Batch Definition into a spec that enables Great Expectations to access the data using that Execution Engine.

Finally, the BatchMarkers are additional pieces of metadata that can be useful to understand reproducibility, such as the time the batch was constructed, or hash of an in-memory DataFrame.

Batch examples#

Let's explore Batches with some examples:

Given the following BatchDefinition:

{  "datasource_name": "cloud_spark",  "data_connector_name": "s3",  "data_asset_name": "nightly_logs",  "batch_identifiers": {    "year": 2020,    "month": 9,    "day": 15  }}

The ConfiguredAssetFilesystemDataConnector might work with a configured SparkDFExecutionEngine to translate that to the following Batch Spec:

{  "paths": [    "s3a://logs.priv/nightly/2020/09/15/f23b4301-dcfa-4a1d-b054-23659b55c4f2.csv",    "s3a://logs.priv/nightly/2020/09/15/212becf1-45d4-4cce-a0fb-52d5b7883a89.csv"  ]  "reader_method": "read_csv",  "reader_options": {    "sep": "\t"  }}

[[Diagram: External Datasource -> Batch Identifiers -> Batch Definition -> Batch Spec -> Execution Engine -> External Datasource ]]

The Datasource can then query the ExecutionEngine to fetch data and BatchMarkers, producing a new_batch that may look something like this:

Batch(    data,    batch_request: {        "datasource": "myds"        "data_connector": "pipeline",        "data_asset_name": "my_asset",        "data_connector_query" : {            "batch_filter_parameters" : {                "airflow_run_id": "string_airflow_run_id_that_was_provided",                "other_key": "string_other_key_that_was_provided",            }            "custom_filter_function": my_filter_fn,            "limit": 10,            "index": Optional[Union[int, list, tuple, slice, str]],  # examples: 0; "-1"; [3:7]; "[2:4]"        }    },    batch_definition: {        "datasource": "myds",        "data_connector": "pipeline",        "data_asset_name": "my_asset",        "batch_identifiers": {          "airflow_run_id": "string_airflow_run_id_that_was_provided",          "other_key": "string_other_key_that_was_provided",      }    },    batch_spec: {            in_memory_df: True    },    batch_markers: {        "pandas_hash": "_______"    })

A full journey#

Let's follow the outline in this diagram to follow the journey from BatchRequest to BatchDefinition to BatchSpec to Batch:


  1. BatchRequest
  • The BatchRequest is the object a user passes to the DataSource to request a Batch (or Batches).

    • It can include data_connector_query params with values relative to the latest batch (e.g. the "latest" slice). Conceptually, this enables "fetch the latest Batch" behavior. It is the key thing that differentiates a BatchRequest, which does NOT necessarily uniquely identify the Batch(es) to be fetched, from a BatchDefinition.
    • The BatchRequest can also include a section called batch_spec_passthrough to make it easy to directly communicate parameters to a specific ExecutionEngine.
    • When resolved, the BatchRequest may point to many BatchDefinitions and Batches.
  • A BatchRequest is the entrypoint to getting a Batch from a Datasource, using the get_batch_list_from_batch_request() method:

DataSource.get_batch_list_from_batch_request(batch_request={    "datasource": "myds",    "data_connector": "pipeline",    "in_memory_dataset": df,    "data_connector_query" : {      "batch_filter_parameters" : {          "airflow_run_id": my_run_id,          "other_key": my_other_key      }      "custom_filter_function": my_filter_fn,      "limit": 10,      "index": Optional[Union[int, list, tuple, slice, str]],  # examples: 0; "-1"; [3:7]; "[2:4]"    },    "sampling": {        "limit": 1000,        "sample": 0.1    }})
  1. BatchDefinition
  • A BatchDefinition resolves any ambiguity in BatchRequest to uniquely identify a single Batch to be fetched. BatchDefinition is Datasource- and ExecutionEngine-agnostic. That means that its parameters may depend on the configuration of the Datasource, but they do not otherwise depend on the specific DataConnector type (e.g. filesystem, SQL, etc.) or ExecutionEngine being used to instantiate Batches.
BatchDefinition    datasource: str    data_connector: str    data_asset_name: str    batch_identifiers:        ** contents depend on the configuration of the DataConnector **        ** provides a persistent, unique identifier for the Batch within the context of the Data Asset **
  1. BatchSpec
  • A BatchSpec is a set of specific instructions for the ExecutionEngine to fetch specific data; it is the ExecutionEngine-specific version of the BatchDefinition. For example, a BatchSpec could include the path to files, information about headers, or other configuration required to ensure the data is loaded properly for validation.
  1. During initialization of the Batch, BatchMarkers, calculated by the ExecutionEngine, are also added. They are metadata that can be used to calculate performance characteristics, ensure reproducibility of Validation Results, and provide indicators of the state of the underlying data system.

RuntimeDataConnector and RuntimeBatchRequest#

A Runtime Data Connector is a special kind of Data Connector that supports easy integration with Pipeline Runners where the data is already available as a reference that needs only a lightweight wrapper to track validations. Runtime Data Connectors are used alongside a special kind of Batch Request class called a RuntimeBatchRequest. Instead of serving as a description of what data Great Expectations should fetch, a Runtime Batch Request serves as a wrapper for data that is passed in at runtime (as an in-memory dataframe, file/S3 path, or SQL query), with user-provided identifiers for uniquely identifying the data.

In a Batch Definition produced by a Runtime Data Connector, the batch_identifiers come directly from the Runtime Batch Request and serve as a persistent, unique identifier for the data included in the Batch. By relying on user-provided batch_identifiers, we allow the definition of the specific batch's identifiers to happen at runtime, for example using a run_id from an Airflow DAG run. The specific runtime batch_identifiers to be expected are controlled in the Runtime Data Connector configuration. Using that configuration creates a control plane for governance-minded engineers who want to enforce some level of consistency between validations.