A naukri.com initiative
Amazon
2w
278
Image Credit: Amazon
Access Amazon Redshift Managed Storage tables through Apache Spark on AWS Glue and Amazon EMR using Amazon SageMaker Lakehouse
- Data-driven organizations are adopting lakehouse solutions for simplified data management and access to data from various engines.
- Amazon SageMaker Lakehouse unifies data from Amazon S3 and Amazon Redshift to power analytics and AI/ML applications.
- SageMaker Lakehouse allows in-place data access across third-party sources through Amazon Athena.
- Access Amazon Redshift Managed Storage (RMS) tables via Iceberg APIs using AWS Glue Data Catalog.
- Integration steps for Apache Spark on AWS Glue and Amazon EMR to access RMS tables are described.
- Configurations for accessing RMS databases using Spark session catalog configurations are outlined.
- Create a Lakehouse catalog for RMS and manage data access through SageMaker Unified Studio and AWS Glue.
- Demonstration on querying RMS tables via SageMaker Unified Studio using Spark and Iceberg REST catalog.
- Instructions for creating, accessing, and querying tables in RMS Lakehouse catalog are provided.
- Cleaning up resources post demo implementation to prevent future charges is highlighted.
Read Full Article
16 Likes
For uninterrupted reading, download the app