Astro SDK allows rapid and clean development of {Extract, Load, Transform} workflows using Python and SQL, powered by Apache Airflow.
load_file
#1922
transform
#1925
openlineage_dataset_uri
in databricks #1919
example_load_file
DAG tasks names #1958
databricks-sql-connector<2.9.0
#2013
Bug Fixes
Bug Fixes
skip_on_failure
to CleanupOperator
#1837 by @scottleechuaquery_modifier
to raw_sql
, transform
and transform_file
, which allow users to define SQL statements to be run before the main query statement #1898.
Example of how to use this feature can be used to add Snowflake query tags to a SQL statement:
from astro.query_modifier import QueryModifier
@aql.run_raw_sql(
results_format="pandas_dataframe",
conn_id="sqlite_default",
query_modifier=QueryModifier(pre_queries=["ALTER team_1", "ALTER team_2"]),
)
def dummy_method():
return "SELECT 1+1"
aql.dataframe
#1839
aql.transform
to receive `sql filepath #1879
query_modifier
to raw_sql
, which users can use to define SQL statements to be run before and after the main query statement #1898
Example:
from astro.query_modifier import QueryModifier
@aql.run_raw_sql(
results_format="pandas_dataframe",
conn_id="sqlite_default",
query_modifier=QueryModifier(pre_queries=["ALTER team_1", "ALTER team_2"]),
)
def dummy_method():
return "SELECT 1+1"
Bug fix:
run_raw_sql
, transform
, dataframe
)
to convert a Pandas dataframe into a table when using a DuckDB in-memory
database. #1831
Thanks to @pgzmnk for reporting the issue!