ingesting-into-data-lake

You can ingest data from JDBC sources into an AWS data lake by defining source discovery, validating connections, and using Glue pipelines to load data into S3 Tables or Iceberg sinks with schema handling and data quality checks.

Yes, you can load data from Snowflake and BigQuery into S3 Tables using Glue. The ingestion process includes source discovery, connection validation, target selection, and data quality checks to ensure reliable data movement.

The best way to set up recurring data lake ingestion pipelines in AWS is using Glue to automate data movement from diverse sources into Iceberg or S3 Tables, handling schema evolution and data quality checks for reliable, auditable pipelines.

Yes, this data lake ingestion approach supports schema evolution and data quality checks. It ensures reliable, auditable data movement into S3 Tables or Iceberg from sources like S3 files, DynamoDB, and existing Glue catalogs.

Yes, you can ingest DynamoDB data into an Iceberg data lake. The process handles source discovery, connection validation, target selection, and data quality checks to move data reliably into your scalable AWS data lake.

You can migrate local files into an S3 Tables data lake by using local uploads within a Glue-based ingest pipeline. The process validates connections, handles schemas, and applies data quality checks for reliable data movement.