@Scout can you explain the following error and possible fixes:

dagster._check.CheckError: Member of iterable mismatches type. Expected (<class ‘dagster._core.definitions.assets.AssetsDefinition’>, <class ‘dagster._core.definitions.source_asset.SourceAsset’>, <class ‘dagster._core.definitions.cacheable_assets.CacheableAssetsDefinition’>). Got [<dagster._core.definitions.assets.AssetsDefinition object at 0x142a9a080>] of type <class ‘list’>.

The error message you're encountering indicates that Dagster expected an iterable containing instances of

AssetsDefinition

,

SourceAsset

, or

CacheableAssetsDefinition

, but instead received a list that contains an

AssetsDefinition

object. This type mismatch is causing the

CheckError

. The error typically occurs when defining a

Definitions

object, which is used to bundle together various Dagster definitions such as assets, resources, and others. When you pass a list of assets to the

Definitions

constructor, you should ensure that each item in the list is an asset definition, rather than wrapping them in another list. Here's a common mistake that leads to this error:

python # Incorrect usage that leads to the error airbyte_assets = build_airbyte_assets(...) defs = Definitions(     assets=[         airbyte_assets  # This is incorrect if airbyte_assets is already a list     ],     resources={...}, )

To fix the error, you should pass the

airbyte_assets

directly if it's already a list of asset definitions:

python # Correct usage airbyte_assets = build_airbyte_assets(...) defs = Definitions(     assets=airbyte_assets,  # Pass the list directly without additional brackets     resources={...}, )

Make sure that

airbyte_assets

is an iterable of asset definitions and not wrapped in an additional list. If you have multiple lists of asset definitions that you want to combine, you can concatenate them before passing them to `Definitions`:

python # Concatenating multiple lists of asset definitions assets_list_1 = build_some_assets(...) assets_list_2 = build_other_assets(...) all_assets = assets_list_1 + assets_list_2  defs = Definitions(     assets=all_assets,     resources={...}, )

By ensuring that the

assets

parameter receives the correct type of iterable, you should be able to resolve the

CheckError

.

nice, thanks. @Scout separate question, but how do you insert data in a table in bigquery? the docs only shows examples of creating a BQ table from a dataframe

To insert data into an existing BigQuery table, you would typically use the BigQuery client library provided by Google. However, within the context of Dagster, you would use an I/O manager that is designed to interact with BigQuery. The BigQuery I/O manager can handle the loading and materializing of data to and from BigQuery tables. Here's a high-level overview of how you might insert data into a BigQuery table using Dagster: 1. Define an asset that represents the data you want to insert into BigQuery. 2. Use an I/O manager that is capable of writing to BigQuery, such as

BigQueryPandasIOManager

if you are working with Pandas DataFrames. 3. Configure the I/O manager with the necessary BigQuery credentials and table details. 4. In your asset's computation function, return the data you want to insert as a Pandas DataFrame. 5. Dagster will use the I/O manager to insert the data into the specified BigQuery table when the asset is materialized. Here's a simplified example of how you might set this up:

python from dagster import asset from dagster_gcp.bigquery import BigQueryPandasIOManager  # Define an asset with a BigQuery I/O manager @asset(io_manager_key="bigquery_io_manager") def my_bigquery_asset():     # Generate or load your data as a Pandas DataFrame     data_to_insert = ...      # The returned DataFrame will be written to BigQuery by the I/O manager     return data_to_insert  # In your repository definition, configure the BigQuery I/O manager @repository def my_repo():     return [         my_bigquery_asset,         ...,         io_manager_defs={             "bigquery_io_manager": BigQueryPandasIOManager,         },     ]

In this example,

data_to_insert

should be a Pandas DataFrame containing the data you want to insert into the BigQuery table. The

BigQueryPandasIOManager

will handle the insertion of the DataFrame into the table when the asset is materialized. Please note that you will need to provide the appropriate configuration for the BigQuery I/O manager, including credentials and table details, in your Dagster instance's configuration file or environment variables. For more detailed instructions and examples, you should refer to the Dagster documentation on BigQuery integration, which provides guidance on setting up and using BigQuery with Dagster.

@Scout what’s the difference between

BigQueryPandasIOManager

and

BigQueryResource

?

BigQueryPandasIOManager

and

BigQueryResource

serve different purposes within the Dagster framework when interacting with Google BigQuery: 1. BigQueryPandasIOManager: - It is a specialized I/O manager for handling the storage and retrieval of Pandas DataFrames to and from BigQuery tables. - When you define an asset that produces a Pandas DataFrame, the

BigQueryPandasIOManager

can automatically handle writing the DataFrame to a BigQuery table when the asset is materialized. - Similarly, it can load data from a BigQuery table into a Pandas DataFrame when an asset requires it as an input. - The

BigQueryPandasIOManager

is configured with details about the BigQuery project, dataset, and table, as well as any necessary authentication information. 2. BigQueryResource: - It is a general-purpose resource that provides a way to interact with BigQuery through the BigQuery client library. - You can use the

BigQueryResource

to execute arbitrary SQL queries, manage datasets, and perform other BigQuery operations that are not limited to handling Pandas DataFrames. - It is useful when you need more control over BigQuery interactions beyond just loading and storing DataFrames, such as creating tables, running complex queries, or managing jobs. - The

BigQueryResource

is also configured with details about the BigQuery project and authentication information, and it exposes a BigQuery client that you can use within your asset computations. In summary, the

BigQueryPandasIOManager

is specifically designed for automated DataFrame storage and retrieval with BigQuery, while the

BigQueryResource

provides a more flexible interface to the full range of BigQuery services. Depending on your use case, you might use one or both of these in your Dagster pipelines. If you are simply reading from or writing DataFrames to BigQuery, the

BigQueryPandasIOManager

might be sufficient. If you need to perform additional BigQuery operations, you would use the

BigQueryResource

.