Category: AWS Glue

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.

In this blog post, we demonstrate how to use Amazon MWAA for centralized orchestration, while distributing data processing and machine learning tasks across different AWS accounts and Regions for optimal performance and compliance.

This post demonstrates how to implement reliable concurrent write handling mechanisms in Iceberg tables. We will explore Iceberg’s concurrency model, examine common conflict scenarios, and provide practical implementation patterns of both automatic retry mechanisms and situations requiring custom conflict resolution logic for building resilient data pipelines. We will also cover the pattern with automatic compaction through AWS Glue Data Catalog table optimization.

In this post, we show how to develop and test AWS Glue 5.0 jobs locally using a Docker container. This post is an updated version of the post Develop and test AWS Glue version 3.0 and 4.0 jobs locally using a Docker container, and uses AWS Glue 5.0.

In this post, we show you how to use AWS Glue to extract data from Marketo Engage for data processing and enrichment on AWS for use in marketing analytics workflows.

In this post, we will cover how you can use Amazon DataZone to facilitate data collaboration between AWS accounts.

Amazon Web Services (AWS) has been recognized as a Leader in the 2024 Gartner Magic Quadrant for Data Integration Tools. We were positioned in the Challengers Quadrant in 2023. This recognition, we feel, reflects our ongoing commitment to innovation and excellence in data integration, demonstrating our continued progress in providing comprehensive data management solutions.

At Open Universities Australia (OUA), we empower students to explore a vast array of degrees from renowned Australian universities, all delivered through online learning. In this post, we show you how we used AWS services to replace our existing third-party ETL tool, improving the team’s productivity and producing a significant reduction in our ETL operational costs.

In this post, we dive into the transformative features of EMR on Outposts, showcasing its flexibility as a native hybrid data analytics service that allows seamless data access and processing both on premises and in the cloud.

In our previous thought leadership blog post Why a Cloud Operating Model we defined a COE Framework and showed why MuleSoft implemented it and the benefits they received from it. In this post, we’ll dive into the technical implementation describing how MuleSoft used Amazon EventBridge, Amazon Redshift, Amazon Redshift Spectrum, Amazon S3, & AWS Glue to implement it.