fix: replace amazon-reviews with ursa-labs public S3 bucket (#2460)

tests/glue_scripts/wrangler_blog_simple.py

@@ -7,37 +7,33 @@
 glue_database = os.environ["glue-database"]
 glue_table = os.environ["glue-table"]
 
-category = "toys"
+# Read 1.5 Gb Parquet data
+df = wr.s3.read_parquet(path="s3://ursa-labs-taxi-data/2017/")
 
-# Read Parquet data (1.2 Gb parquet compressed)
-df = wr.s3.read_parquet(
-    path=f"s3://amazon-reviews-pds/parquet/product_category={category.title()}/",
-)
-
-# Drop customer_id column
-df.drop("customer_id", axis=1, inplace=True)
+# Drop vendor_id column
+df.drop("vendor_id", axis=1, inplace=True)
 
-# Filter reviews with 5-star rating
-df5 = df[df["star_rating"] == 5]
+# Filter trips with 1 passenger
+df1 = df[df["trip_distance"] > 1]
 
-# Write partitioned five stars reviews to S3 in Parquet format
+# Write partitioned trips to S3 in Parquet format
 wr.s3.to_parquet(
-    df5,
-    path=f"{output_path}output/{category}/",
-    partition_cols=["year", "marketplace"],
+    df1,
+    path=f"{output_path}output/{glue_table}/",
+    partition_cols=["passenger_count", "payment_type"],
     dataset=True,
     database=glue_database,
     table=glue_table,
 )
 
 # Read the data back to a modin df via Athena
-df5_athena = wr.athena.read_sql_query(
+df1_athena = wr.athena.read_sql_query(
     f"SELECT * FROM {glue_table}",
     database=glue_database,
     ctas_approach=False,
     unload_approach=True,
     workgroup=workgroup_name,
-    s3_output=f"{output_path}unload/{category}/",
+    s3_output=f"{output_path}unload/{glue_table}/",
 )
 
 # Delete table (required due to LF)

tests/load/test_databases.py

@@ -109,15 +109,15 @@ def test_redshift_copy_unload(
 
 @pytest.mark.parametrize("benchmark_time", [40])
 def test_athena_unload(benchmark_time: int, path: str, glue_table: str, glue_database: str, request) -> None:
-    df = wr.s3.read_parquet(path="s3://amazon-reviews-pds/parquet/product_category=Toys/", dataset=True)
+    df = wr.s3.read_parquet(path="s3://ursa-labs-taxi-data/2017/", dataset=True)
 
     wr.s3.to_parquet(
         df,
         path,
         dataset=True,
         table=glue_table,
         database=glue_database,
-        partition_cols=["year", "marketplace"],
+        partition_cols=["passenger_count", "payment_type"],
     )
 
     with ExecutionTimer(request) as timer:
@@ -136,7 +136,7 @@ def test_athena_unload(benchmark_time: int, path: str, glue_table: str, glue_dat
 
 @pytest.mark.parametrize("benchmark_time", [80])
 def test_lakeformation_read(benchmark_time: int, path: str, glue_table: str, glue_database: str, request) -> None:
-    df = wr.s3.read_parquet(path="s3://amazon-reviews-pds/parquet/product_category=Home/", dataset=True)
+    df = wr.s3.read_parquet(path="s3://ursa-labs-taxi-data/2017/", dataset=True)
 
     wr.s3.to_parquet(
         df,
@@ -145,7 +145,7 @@ def test_lakeformation_read(benchmark_time: int, path: str, glue_table: str, glu
         dataset=True,
         table=glue_table,
         database=glue_database,
-        partition_cols=["year", "marketplace"],
+        partition_cols=["passenger_count", "payment_type"],
         glue_table_settings={
             "table_type": "GOVERNED",
         },

tests/load/test_s3.py

@@ -96,16 +96,6 @@ def test_s3_read_parquet_many_files(
     assert timer.elapsed_time < benchmark_time
 
 
-@pytest.mark.parametrize("benchmark_time", [40])
-def test_s3_read_parquet_partition_filter(benchmark_time: float, request: pytest.FixtureRequest) -> None:
-    path = "s3://amazon-reviews-pds/parquet/"
-    with ExecutionTimer(request, data_paths=path) as timer:
-        filter = lambda x: True if x["product_category"].startswith("Wireless") else False  # noqa: E731
-        wr.s3.read_parquet(path=path, dataset=True, partition_filter=filter)
-
-    assert timer.elapsed_time < benchmark_time
-
-
 @pytest.mark.parametrize("benchmark_time", [5])
 @pytest.mark.parametrize("path_suffix", [None, "df.parquet"])
 def test_s3_write_parquet_simple(