AWS Big Data Blog

The AI search flow builder is available in all AWS Regions that support OpenSearch 2.19+ on OpenSearch Service. In this post, we walk through a couple of scenarios to demonstrate the flow builder. First, we’ll enable semantic search on your old keyword-based OpenSearch application without client-side code changes. Next, we’ll create a multi-modal RAG flow, to showcase how you can redefine image discovery within your applications.

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.

Today, we’re excited to introduce a new unified scheduling feature that simplifies this process. SageMaker Unified Studio allows you to create ETL flows using a visual interface and write SQL analytics queries using query books. In this post, we walk through how to schedule your visual ETL flows and query books with just a few clicks, explore the underlying architecture, and demonstrate how this feature can streamline your data workflow automation.

In this post, we share how Flutter UKI transitioned from a monolithic Amazon Elastic Compute Cloud (Amazon EC2)-based Airflow setup to a scalable and optimized Amazon Managed Workflows for Apache Airflow (Amazon MWAA) architecture using features like Kubernetes Pod Operator, continuous integration and delivery (CI/CD) integration, and performance optimization techniques.

At the BMW Group, our Cloud Efficiency Analytics (CLEA) team has developed a FinOps solution to optimize costs across over 10,000 cloud accounts This post explores our journey, from the initial challenges to our current architecture, and details the steps we took to achieve a highly efficient, serverless data transformation setup.

In this post, we explore how to apply the principle of least privilege to your Amazon MWAA environment by tightening network security using security groups, network access control lists (ACLs), and virtual private cloud (VPC) endpoints. We also discuss the Amazon MWAA execution and deployment roles and their respective permissions.

This series of posts demonstrates how you can onboard and access existing AWS data sources using SageMaker Unified Studio. This post focuses on onboarding existing AWS Glue Data Catalog tables and database tables available in Amazon Redshift.

In this post we discuss integrating additional vital data sources such as Amazon Simple Storage Service (Amazon S3) buckets, Amazon Relational Database Service (Amazon RDS), Amazon DynamoDB, and Amazon EMR clusters. We demonstrate how to configure the necessary permissions, establish connections, and effectively use these resources within SageMaker Unified Studio. Whether you’re working with object storage, relational databases, NoSQL databases, or big data processing, this post can help you seamlessly incorporate your existing data infrastructure into your SageMaker Unified Studio workflows.

Natural Intelligence (NI) is a world leader in multi-category marketplaces. In this blog post, NI shares their journey, the innovative solutions developed, and the key takeaways that can guide other organizations considering a similar path. This article details NI’s practical approach to this complex migration, focusing less on Apache Iceberg’s technical specifications, but rather on the real-world challenges and solutions encountered during the transition to Apache Iceberg, a challenge that many organizations are grappling with.

Amazon SageMaker Lakehouse now supports attribute-based access control (ABAC) with AWS Lake Formation, using AWS Identity and Access Management (IAM) principals and session tags to simplify data access, grant creation, and maintenance. In this post, we demonstrate how to get started with SageMaker Lakehouse with ABAC.