Category: Technical How-to

In this post, we explain what’s new in Amazon Managed Service for Apache Flink 2.2, provide a guided migration using CLI commands, console instructions, and code examples, and show you how to monitor the upgrade and roll back if needed.

In this post, we explore how to use Apache Sedona with AWS Glue to process and analyze massive geospatial datasets.

In this post, we demonstrate how to use the metadata export capability in Amazon SageMaker Catalog and perform analytics such as historical changes, monitor asset growth and track metadata improvements.

In the first part of Configure a custom domain name for your Amazon MSK cluster, we discussed about why custom domain names are important and provided details on how to configure a custom domain name in Amazon MSK when using SASL_SCRAM authentication. In this post, we discuss how to configure a custom domain name in Amazon MSK when using IAM authentication.

In this post, we walk you through how to replicate Apache Kafka data from your external Apache Kafka deployments to Amazon MSK Express brokers using MSK Replicator. You will learn how to configure authentication on your external cluster, establish network connectivity, set up bidirectional replication, and monitor replication health to achieve a low-downtime migration.

In this post, you build a unified pipeline using Apache Iceberg and Amazon Managed Service for Apache Flink that replaces the dual-pipeline approach. This walkthrough is for intermediate AWS users who are comfortable with Amazon Simple Storage Service (Amazon S3) and AWS Glue Data Catalog but new to streaming from Apache Iceberg tables.

In this post, you will learn how to use AWS IAM Roles Anywhere with Amazon Redshift for secure, private connections. This removes the need to expose traffic to the public internet or manage long-lived access keys.

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 blog post, we use Athena and Amazon SageMaker Unified Studio to explore Parquet Column Indexes and demonstrate how they can improve Iceberg query performance. We explain what Parquet Column Indexes are, demonstrate their performance benefits, and show you how to use them in your applications.

In this post, we show how to configure Kerberos authentication for Spark jobs on Amazon EMR on EKS, authenticating against a Kerberos-enabled HMS so you can run both Amazon EMR on EC2 and Amazon EMR on EKS workloads against a single, secure HMS deployment.