AWS Big Data Blog
How Flutter UKI optimizes data pipelines with AWS Managed Workflows for Apache Airflow
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.
How BMW Group built a serverless terabyte-scale data transformation architecture with dbt and Amazon Athena
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.
Best practices for least privilege configuration in Amazon MWAA
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.
Access your existing data and resources through Amazon SageMaker Unified Studio, Part 1: AWS Glue Data Catalog and Amazon Redshift
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.
Access your existing data and resources through Amazon SageMaker Unified Studio, Part 2: Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR
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.
Melting the ice — How Natural Intelligence simplified a data lake migration to Apache Iceberg
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
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.
Accelerate data pipeline creation with the new visual interface in Amazon OpenSearch Ingestion
Today, we’re launching a new visual interface for OpenSearch Ingestion that makes it simple to create and manage your data pipelines from the AWS Management Console. With this new feature, you can build pipelines in minutes without writing complex configurations manually. In this post, we walk through how these new features work and how you can use them to accelerate your data ingestion projects.
Read and write Apache Iceberg tables using AWS Lake Formation hybrid access mode
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.
Accelerate your analytics with Amazon S3 Tables and Amazon SageMaker Lakehouse
Amazon SageMaker Lakehouse is a unified, open, and secure data lakehouse that now seamlessly integrates with Amazon S3 Tables, the first cloud object store with built-in Apache Iceberg support. In this post, we guide you how to use various analytics services using the integration of SageMaker Lakehouse with S3 Tables.