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| author | Ang Bel <anzelikabelotelova@Anzelikas-MacBook-Air.local> | 2024-08-13 16:33:56 +0100 |
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| committer | Ang Bel <anzelikabelotelova@Anzelikas-MacBook-Air.local> | 2024-08-13 16:33:56 +0100 |
| commit | 6c7914a9d33fbaa962cf1f083c2ee79ace62f401 (patch) | |
| tree | 36083336aa958e1d16c6c78cb3ff1c9cbfa4bba9 /src | |
| parent | bb1665fd08d8abf10930875272bdc2b7f8a4c681 (diff) | |
| download | de-project-bentley-6c7914a9d33fbaa962cf1f083c2ee79ace62f401.tar.gz de-project-bentley-6c7914a9d33fbaa962cf1f083c2ee79ace62f401.zip | |
restore load_lambda script
Diffstat (limited to 'src')
| -rw-r--r-- | src/load_lambda.py | 52 |
1 files changed, 52 insertions, 0 deletions
diff --git a/src/load_lambda.py b/src/load_lambda.py index e69de29..5c6718c 100644 --- a/src/load_lambda.py +++ b/src/load_lambda.py @@ -0,0 +1,52 @@ +### Example taken from https://medium.com/@pranay1001090/how-to-load-data-from-amazon-s3-csv-parquet-to-aws-rds-using-python-3dc51dd2186e + +### THIS IS AN EXAMPLE CODE WE CAN PICK FROM, NONE OF THIS HAS BEEN CUSTOMISED YET + +import boto3 +import pandas as pd +import pyarrow.parquet as pq +from io import BytesIO +from sqlalchemy import create_engine + +# AWS credentials and region +aws_access_key = '<your-access-key>' +aws_secret_key = '<your-secret-key>' +region_name = '<your-region>' + +# S3 bucket and file details +bucket_name = '<your-bucket-name>' +file_prefix = '<your-file-prefix>' +s3_client = boto3.client('s3', aws_access_key_id=aws_access_key, aws_secret_access_key=aws_secret_key, region_name=region_name) + +# RDS connection details +database_name = '<your-database-name>' +table_name = '<your-table-name>' +rds_host = '<your-rds-host>' +rds_port = '<your-rds-port>' +rds_user = '<your-rds-username>' +rds_password = '<your-rds-password>' +# Function to load Parquet files into a Pandas DataFrame +def load_parquet_data(s3_bucket, s3_prefix): + file_objects = s3_client.list_objects_v2(Bucket=s3_bucket, Prefix=s3_prefix)['Contents'] + dfs = [] + for file_object in file_objects: + file_key = file_object['Key'] + file_obj = s3_client.get_object(Bucket=s3_bucket, Key=file_key) + parquet_file = pq.ParquetFile(BytesIO(file_obj['Body'].read())) + df = parquet_file.read().to_pandas() + dfs.append(df) + return pd.concat(dfs) + +# Load Parquet data from S3 into a Pandas DataFrame +df = load_parquet_data(bucket_name, file_prefix) +# Connect to RDS +conn_str = f'mysql+pymysql://{rds_user}:{rds_password}@{rds_host}:{rds_port}/{database_name}' +engine = create_engine(conn_str) + +# Write the DataFrame to RDS +df.to_sql(table_name, con=engine, if_exists='replace', index=False) + +# Closing the connection +engine.dispose() + +print('Data loaded successfully!')
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