AWS Big Data Blog

In this post, we explore how PackScan uses Amazon cloud-based services to drive real-time visibility, improve logistics efficiency, and support the seamless movement of packages across Amazon’s Middle Mile network.

Airties is a wireless networking company that provides AI-driven solutions for enhancing home connectivity. This post explores the strategies the Airties team employed during their migration from Apache Kafka to Amazon Kinesis Data Streams, the challenges they overcame, and how they achieved a more efficient, scalable, and maintenance-free streaming infrastructure.

In this post, we present Riskified’s journey toward enabling self-service streaming SQL pipelines. We walk through the motivations behind the shift from Confluent ksqlDB to Apache Flink, the architecture Riskified built using Amazon Managed Service for Apache Flink, the technical challenges they faced, and the solutions that helped them make streaming accessible, scalable, and production-ready.

In this post, we show you how to create Iceberg tables in Amazon SageMaker Unified Studio and stream data to these tables using Firehose. With this integration, data engineers, analysts, and data scientists can seamlessly collaborate and build end-to-end analytics and ML workflows using SageMaker Unified Studio, removing traditional silos and accelerating the journey from data ingestion to production ML models.

OpenSearch UI has been adopted by thousands of customers for various use cases since its launch in November 2024. Exciting customer stories and feedback have helped shape our feature improvements. As we complete 6 months since its general availability, we are sharing major enhancements that have improved OpenSearch UI’s capability, especially in observability and security analytics, in this post.

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.

In this post, we showcase how financial planners, advisors, or bankers can now ask questions in natural language. These prompts will receive precise data from the customer databases for accounts, investments, loans, and transactions. Amazon Bedrock Knowledge Bases automatically translates these natural language queries into optimized SQL statements, thereby accelerating time to insight, enabling faster discoveries and efficient decision-making.

In this post, we explore how LaunchDarkly scaled the internal analytics platform up to 14,000 tasks per day, with minimal increase in costs, after migrating from another vendor-managed Apache Airflow solution to AWS, using Amazon Managed Workflows for Apache Airflow (Amazon MWAA) and Amazon Elastic Container Service (Amazon ECS).

In this post, we show how to grant Amazon S3 permissions to IAM Identity Center users and groups using S3 Access Grants. We also test the integration using an IAM Identity Center federated user to unload data from Amazon Redshift to Amazon S3 and load data from Amazon S3 to Amazon Redshift.

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.