Category: Amazon EMR

In this post, we guide you through the process of creating a Data Catalog view using EMR Serverless, adding the SQL dialect to the view for Athena, sharing it with another account using LF-Tags, and then querying the view in the recipient account using a separate EMR Serverless workspace and AWS Glue 5.0 Spark job and Athena. This demonstration showcases the versatility and cross-account capabilities of Data Catalog views and access through various AWS analytics services.

This post demonstrates how to centralize Apache Spark observability using SHS on EMR on EKS. We showcase how to enhance SHS with performance monitoring tools, with a pattern applicable to many monitoring solutions such as SparkMeasure and DataFlint.

The post demonstrates a comprehensive, end-to-end solution for processing data from MSK Serverless using an EMR Serverless Spark Streaming job, secured with IAM authentication. Additionally, it demonstrates how to query the processed data using Amazon Athena, providing a seamless and integrated workflow for data processing and analysis. This solution enables near real-time querying of the latest data processed from MSK Serverless and EMR Serverless using Athena, providing instant insights and analytics.

In this post, we dive deep into the new Amazon EMR WAL feature to help you understand how it works, how it enhances durability, and why it’s needed. We explore several scenarios that are well-suited for this feature.

In this post, we’ll build on the first post in this series to show you how to set up an Apache Iceberg data lake catalog using Amazon S3 Tables and provide different levels of access control to your data. Through this example, you’ll set up fine-grained access controls for multiple users and see how this works using Amazon Redshift. We’ll also review an example with simultaneously using data that resides both in Amazon Redshift and Amazon S3 Tables, enabling a unified analytics experience.

With SageMaker Lakehouse, you can access tables stored in Amazon Redshift managed storage (RMS) through Iceberg APIs, using the Iceberg REST catalog backed by AWS Glue Data Catalog. This post describes how to integrate data on RMS tables through Apache Spark using SageMaker Unified Studio, Amazon EMR 7.5.0 and higher, and AWS Glue 5.0.

In this post, we share how AppsFlyer successfully migrated their massive data infrastructure from self-managed Hadoop clusters to Amazon EMR Serverless, detailing their best practices, challenges to overcome, and lessons learned that can help guide other organizations in similar transformations.

This post shows how to enhance the multi-cluster solution by integrating Amazon Managed Workflows for Apache Airflow (Amazon MWAA) with BPG. By using Amazon MWAA, we add job scheduling and orchestration capabilities, enabling you to build a comprehensive end-to-end Spark-based data processing pipeline.

In this post, we demonstrate how to use Lake Formation for read access while continuing to use AWS Identity and Access Management (IAM) policy-based permissions for write workloads that update the schema and upsert (insert and update combined) data records into the Iceberg tables.

This post discusses a decoupled approach of building a serverless data lakehouse using AWS Cloud-centered services, including Amazon EMR Serverless, Amazon Athena, Amazon Simple Storage Service (Amazon S3), Apache DolphinScheduler (an open source data job scheduler) as well as PingCAP TiDB, a third-party data warehouse product that can be deployed either on premises or on the cloud or through a software as a service (SaaS).