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#ask-ai
Title
# ask-ai
g

Greg H

02/28/2024, 10:42 PM
what's the best way to load an asset that was created in dbt into a pandas dataframe?
s

Scout

02/28/2024, 10:42 PM
The best way to load an asset that was created in dbt into a pandas DataFrame is to use an I/O manager that is appropriate for the storage system where the dbt asset is located. Here are the steps you can follow, as described in the documentation: 1. Set
io_manager_key
when defining the dbt assets
: If you have control over the code that loads dbt assets asset definitions, you can set an
io_manager_key
on the dbt assets. This key won't affect how dbt writes the assets, but it will determine how they're loaded as inputs to assets that depend on them. For example, if your dbt assets are stored in Snowflake, you might use the
SnowflakePandasIOManager
. 2. Set
input_manager_key
on the asset that depends on the dbt assets
: If you don't have control over the code that defines your dbt assets, you can control what I/O manager is used at the site where the assets are loaded. You can specify the
input_manager_key
in the
@asset
decorator for the asset that depends on the dbt assets. Here's an example of how you might define an asset that depends on a dbt asset and uses an I/O manager to load it into a pandas DataFrame:
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python from dagster import asset, AssetIn from dagster_snowflake_pandas import SnowflakePandasIOManager  @asset(ins={"raw_table": AssetIn(input_manager_key="snowflake_io_manager")}) def transformed_table(raw_table: DataFrame) -> DataFrame:     # Perform transformations on raw_table, which is a pandas DataFrame     ...  defs = Definitions(     assets=[...],     resources={"snowflake_io_manager": SnowflakePandasIOManager(database=...)}, )
In this example,
raw_table
is the dbt asset, and
transformed_table
is the downstream asset that processes the data from
raw_table
. The
SnowflakePandasIOManager
is used to load the dbt asset into a pandas DataFrame. Remember to replace
"snowflake_io_manager"
with the appropriate I/O manager key for your storage system and to configure the I/O manager with the necessary parameters such as database connection details. For more details and examples, you can refer to the documentation on using I/O managers with dbt assets.
g

Greg H

02/28/2024, 10:44 PM
@Scout what I/O manager should I use for a Postgres database?
s

Scout

02/28/2024, 10:44 PM
For a Postgres database, you can use the
PostgresIOManager
provided by Dagster. This I/O manager is designed to work with Postgres databases and can handle the loading and storing of data frames as tables in a Postgres database. Here's an example of how you might configure the
PostgresIOManager
in your Dagster repository:
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python from dagster import repository from dagster_postgres import PostgresIOManager  @repository def my_repository():     return [         # ... include your assets, pipelines, and other repository components here ...         PostgresIOManager.configured({"hostname": "localhost", "username": "user", "password": "password", "db_name": "my_database"}),     ]
In this example, you would replace
"localhost"
,
"user"
,
"password"
, and
"my_database"
with your actual Postgres database connection details. Then, you can use this I/O manager in your asset definitions to load data from and store data to your Postgres database. Please note that the actual configuration details and usage may vary depending on the version of Dagster you are using and any updates to the library. Always refer to the latest Dagster documentation for the most accurate and up-to-date information.
g

Greg H

02/28/2024, 10:45 PM
@Scout how would the code look like if I materialise an asset
my_dbt_asset
using dbt and I'd like to load this assets from the Postgres database into a pandas dataframe?
s

Scout

02/28/2024, 10:45 PM
To materialize an asset
my_dbt_asset
using dbt and then load this asset from a Postgres database into a pandas DataFrame, you would typically follow these steps: 1. Define the dbt asset: This is done within your dbt project, where you define your models, seeds, snapshots, etc. 2. Create a Dagster asset that represents the dbt asset: You can use the
@asset
decorator to define an asset in Dagster that represents the table created by dbt in your Postgres database. 3. Configure the Postgres I/O Manager: You need to configure the
PostgresIOManager
with the appropriate connection details for your Postgres database. 4. Use the Postgres I/O Manager to load the asset: When defining the Dagster asset, you can specify the
io_manager_key
to tell Dagster to use the Postgres I/O Manager to load the asset into a pandas DataFrame. Here's an example of how this might look in your Dagster code:
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python from dagster import asset, repository from dagster_postgres import PostgresIOManager import pandas as pd  # Configure the Postgres I/O Manager with your database connection details postgres_io_manager = PostgresIOManager.configured({     "hostname": "localhost",     "username": "user",     "password": "password",     "db_name": "my_database",     "schema": "public"  # Optional: specify the schema if not default })  # Define a Dagster asset that represents the dbt asset @asset(io_manager_key="postgres_io_manager") def my_dbt_asset(context) -> pd.DataFrame:     # The context provides access to resources, including the I/O manager     # The I/O manager handles loading the DataFrame from the Postgres table     table_name = "my_dbt_asset_table"  # The name of the table in Postgres created by dbt     query = f"SELECT * FROM {table_name};"     return pd.read_sql_query(query, context.resources.postgres_io_manager.engine)  # Define a repository to hold our assets and resources @repository def my_repository():     return [         my_dbt_asset,         {"postgres_io_manager": postgres_io_manager}     ]
In this example, replace
"localhost"
,
"user"
,
"password"
, and
"my_database"
with your actual Postgres database connection details. The
my_dbt_asset_table
should be replaced with the actual table name in your Postgres database that was created by dbt. Please ensure that you have the necessary dependencies installed, such as
dagster-postgres
for the Postgres I/O Manager and
pandas
for DataFrame operations. Also, make sure to check the latest Dagster documentation for any updates or changes to the API.