Category: Analytics

Amazon SageMaker has announced an integration with Amazon QuickSight, bringing together data in SageMaker seamlessly with QuickSight capabilities like interactive dashboards, pixel perfect reports and generative business intelligence (BI)—all in a governed and automated manner. In this post, we walk through the complete process of integrating Amazon QuickSight with Amazon SageMaker Unified Studio, demonstrating how teams can move from raw data to published dashboards in a secure and governed environment.

We are happy to announce the general availability of the integration of Amazon OpenSearch Service with Amazon Relational Database Service (Amazon RDS) and Amazon Aurora. This new integration eliminates complex data pipelines and enables near real-time data synchronization between Amazon Aurora (including Amazon Aurora MySQL-Compatible Edition and Amazon Aurora PostgreSQL-Compatible Edition) and Amazon RDS databases (including Amazon RDS for MySQL and Amazon RDS for PostgreSQL), and Amazon OpenSearch Service, unlocking advanced search capabilities such as hybrid search, ranked results, and faceted search on transactional databases.

This post demonstrates how AWS Glue R type, G.12X, and G.16X workers help you scale up your AWS Glue for Apache Spark jobs.

Amazon Web Services (AWS) and Collibra have built a new integrated solution that demonstrates the integration between the Collibra Platform and the next generation of Amazon SageMaker. In this post, we take a closer look at the integration, describe the use cases it enables, walk through the architecture, and show how to implement the solution in your environment.

This post demonstrates how the new jobs experience works in SageMaker Unified Studio.

Today, we are excited to launch a new visual workflows builder in SageMaker Unified Studio. With the new visual workflow experience, you don’t need to code the Python DAGs manually. Instead, you can visually define the orchestration workflow in SageMaker Unified Studio, and the visual definition is automatically converted to a Python DAG definition that is supported in Airflow.This post demonstrates the new visual workflow experience in SageMaker Unified Studio.

Revenue NSW, Australia’s principal revenue management agency, successfully modernized its analytics infrastructure using AWS services. In this blog post, we show how the organization transformed its on-premises data environment into a unified, scalable cloud-based solution using Amazon Redshift, AWS Database Migration Service, Amazon AppFlow, and AWS Glue.

In this post, we explore how to maximize Amazon Redshift query performance through nested materialized views and implementing cascading refresh strategies. We demonstrate how to create materialized views based on other materialized views, enabling a hierarchical structure of precomputed results that significantly enhances query performance and data processing efficiency, particularly useful for reusing precomputed joins with different aggregate options.

In this post, we explore how Furuno Electric built a comprehensive data management foundation using Amazon DataZone and other AWS services to transform from a traditional manufacturing company to a data-driven business.

In this post, we review how to set up Redshift Serverless to use geospatial data contained within a data lake to enhance maps in ArcGIS Pro. This technique helps builders and GIS analysts use available datasets in data lakes and transform it in Amazon Redshift to further enrich the data before presenting it on a map.