1. Create a Glue Crawler:
Navigate to the AWS Glue Console.
Create a new crawler to scan your S3 bucket and populate the AWS Glue Data Catalog with table definitions based on your data structure.
2. Run the Glue Crawler:
Execute the crawler. Once it completes, it will create one or more table definitions in the Glue Data Catalog.
3. Create an ETL Job (Optional):
If your data requires transformation:
Use the AWS Glue Console to create an ETL job.
Define a source (the catalog table created by the crawler), the philippines whatsapp number data transformation(s) needed, and the target, which initially could be another S3 bucket location or directly to the RDS instance if direct writes are preferred and supported for your use case.
Retrieving Data from an AWS Data Lake to RDS MS SQL
1. Prepare Your RDS Instance:
Ensure your AWS RDS instance running MS SQL Server is correctly configured, including security groups for network access and the initial database setup.
AWS Lambda can orchestrate the movement of data from S3 (or a transformed dataset in S3) into your RDS MS SQL database.
Create a Lambda Function: Write a function in your preferred language supported by Lambda (e.g., Python). This function will use the “boto3” SDK to access S3 data and a database connector (e.g., “pyodbc” for Python) to insert data into RDS MS SQL.
Example snippet to fetch data from S3: