import awswrangler as wr import pandas as pd from datetime import datetime df = pd.DataFrame({"id": [1, 2], "value": ["foo", "boo"]}) # Storing data on Data Lake wr.s3.to_parquet( df=df, path="s3://bucket/dataset/", dataset=True, database="my_db", table="my_table" ) # Retrieving the data directly from Amazon S3 df = wr.s3.read_parquet("s3://bucket/dataset/", dataset=True) # Retrieving the data from Amazon Athena df = wr.athena.read_sql_query("SELECT * FROM my_table", database="my_db") # Get a Redshift connection from Glue Catalog and retrieving data from Redshift Spectrum con = wr.redshift.connect("my-glue-connection") df = wr.redshift.read_sql_query("SELECT * FROM external_schema.my_table", con=con) con.close() # Amazon Timestream Write df = pd.DataFrame({ "time": [datetime.now(), datetime.now()], "my_dimension": ["foo", "boo"], "measure": [1.0, 1.1], }) rejected_records = wr.timestream.write(df, database="sampleDB", table="sampleTable", time_col="time", measure_col="measure", dimensions_cols=["my_dimension"], ) # Amazon Timestream Query wr.timestream.query(""" SELECT time, measure_value::double, my_dimension FROM "sampleDB"."sampleTable" ORDER BY time DESC LIMIT 3 """)

import logging logging.basicConfig(level=logging.INFO, format="[%(name)s][%(funcName)s] %(message)s") logging.getLogger("awswrangler").setLevel(logging.DEBUG) logging.getLogger("botocore.credentials").setLevel(logging.CRITICAL)

import logging logging.getLogger("awswrangler").setLevel(logging.DEBUG)