AWS Big Data Blog

Amazon SageMaker Unified Studio is a single data and AI development environment that brings together data preparation, analytics, machine learning (ML), and generative AI development in one place. By unifying these workflows, it saves teams from managing multiple tools and makes it straightforward for data scientists, analysts, and developers to build, train, and deploy ML […]

As modern data architectures expand, Apache Iceberg has become a widely popular open table format, providing ACID transactions, time travel, and schema evolution. In table format v2, Iceberg introduced merge-on-read, improving delete and update handling through positional delete files. These files improve write performance but can slow down reads when not compacted, since Iceberg must […]

When you’re spinning up your Amazon OpenSearch Service domain, you need to figure out the storage, instance types, and instance count; decide the sharding strategies and whether to use a cluster manager; and enable zone awareness. Generally, we consider storage as a guideline for determining instance count, but not other parameters. In this post, we […]

AWS recently announced that Amazon SageMaker now offers Amazon Simple Storage Service (Amazon S3) based shared storage as the default project file storage option for new Amazon SageMaker Unified Studio projects. This feature addresses the deprecation of AWS CodeCommit while providing teams with a straightforward and consistent way to collaborate on project files across the […]

In this post, we walk through the different considerations for using Amazon MSK Replicator over Apache Kafka’s MirrorMaker 2, and help you choose the right replication solution for your use case. We also discuss how to make applications using Amazon Managed Streaming for Apache Kafka (Amazon MSK) resilient to disasters using a multi-Region Kafka architecture using MSK Replicator.

This blog post discusses how to create a seamless integration between Amazon SageMaker Lakehouse and Snowflake for modern data analytics. It specifically demonstrates how organizations can enable Snowflake to access tables in AWS Glue Data Catalog (stored in S3 buckets) through SageMaker Lakehouse Iceberg REST Catalog, with security managed by AWS Lake Formation. The post provides a detailed technical walkthrough of implementing this integration, including creating IAM roles and policies, configuring Lake Formation access controls, setting up catalog integration in Snowflake, and managing data access permissions. While four different patterns exist for accessing Iceberg tables from Snowflake, the blog focuses on the first pattern using catalog integration with SigV4 authentication and Lake Formation credential vending.

In this post, we discuss how to build a fully automated, scheduled Spark processing pipeline using Amazon EMR on EC2, orchestrated with Step Functions and triggered by EventBridge. We walk through how to deploy this solution using AWS CloudFormation, processes COVID-19 public dataset data in Amazon Simple Storage Service (Amazon S3), and store the aggregated results in Amazon S3.

In this post, we explore how to use Amazon EMR, the AWS Cloud Development Kit (AWS CDK), and the Data Solutions Framework (DSF) on AWS to streamline the development process, from setting up a local development environment to deploying serverless Spark infrastructure, and implementing a CI/CD pipeline for automated testing and deployment.

In this post, we demonstrate how to connect your local VS Code to SageMaker Unified Studio so you can build complete end-to-end data and AI workflows while working in your preferred development environment.

In this blog post, we walk through how to migrate from API keys to service account tokens when automating Amazon Managed Grafana resource management. We will also show how to securely store tokens using AWS Secrets Manager and automate token rotation with AWS Lambda.