Category: Amazon S3 Tables

Amazon Redshift now supports DELETE, UPDATE, and MERGE operations for Apache Iceberg tables stored in Amazon S3 and Amazon S3 table buckets. With these operations, you can modify data at the row level, implement upsert patterns, and manage the data lifecycle while maintaining transactional consistency using familiar SQL syntax. You can run complex transformations in Amazon Redshift and write results to Apache Iceberg tables that other analytics engines like Amazon EMR or Amazon Athena can immediately query. In this post, you work with datasets to demonstrate these capabilities in a data synchronization scenario.

In this post, you learn how to set up an automated, end-to-end solution that extracts tables from Amazon Aurora MySQL Serverless v2 and writes them to Amazon S3 Tables in Apache Iceberg format using AWS Glue.

In this post, we discuss how Taxbit partnered with Amazon Web Services (AWS) to streamline their crypto tax analytics solution using Amazon S3 Tables, achieving 82% cost savings and five times faster processing times.

In this post, we discuss the best practices that you can follow while querying Apache Iceberg data with Amazon Redshift

AWS Glue zero-ETL with SAP now supports data ingestion and replication from SAP data sources such as Operational Data Provisioning (ODP) managed SAP Business Warehouse (BW) extractors, Advanced Business Application Programming (ABAP), Core Data Services (CDS) views, and other non-ODP data sources. Zero-ETL data replication and schema synchronization writes extracted data to AWS services like Amazon Redshift, Amazon SageMaker lakehouse, and Amazon S3 Tables, alleviating the need for manual pipeline development. In this post, we show how to create and monitor a zero-ETL integration with various ODP and non-ODP SAP sources.

In this post, we show how you can use Amazon Redshift to write data directly to Apache Iceberg tables stored in Amazon S3 and S3 Tables for seamless integration between your data warehouse and data lake while maintaining ACID compliance.

In this post, you learn how to integrate SageMaker Unified Studio with S3 Tables and query your data using Amazon Athena, Amazon Redshift, or Apache Spark in EMR and AWS Glue.

In this post, we walk you through a practical solution for secure, efficient cross-account data sharing and analysis. You’ll learn how to set up cross-account access to S3 Tables using federated catalogs in Amazon SageMaker, perform unified queries across accounts with Amazon Athena in Amazon SageMaker Unified Studio, and implement fine-grained access controls at the column level using AWS Lake Formation.

This post demonstrates how Amazon Athena CREATE TABLE AS SELECT (CTAS) simplifies the data transformation process through a practical example: migrating an existing Parquet dataset into Amazon S3 Tables.

In this post, we show how to use AWS service integrations to minimize custom code while providing a robust platform for industrial data ingestion, processing, and analytics. By using Amazon S3 Tables and its built-in optimizations, you can maximize query performance and minimize costs without additional infrastructure setup.