Category: AWS Glue

This blog post introduces Amazon DataZone and explores how VW used it to build their data mesh to enable streamlined data access across multiple data lakes. It focuses on the key aspect of the solution, which was enabling data providers to automatically publish data assets to Amazon DataZone, which served as the central data mesh for enhanced data discoverability. Additionally, the post provides code to guide you through the implementation.

This post is written in collaboration with Clarisa Tavolieri, Austin Rappeport and Samantha Gignac from Zurich Insurance Group. The growth in volume and number of logging sources has been increasing exponentially over the last few years, and will continue to increase in the coming years. As a result, customers across all industries are facing multiple […]

We are excited to announce a new capability of the AWS Glue Studio visual editor that offers a new visual user experience. Now you can author data preparation transformations and edit them with the AWS Glue Studio visual editor. The AWS Glue Studio visual editor is a graphical interface that enables you to create, run, […]

Today, we are pleased to announce a new capability for the AWS Glue Data Catalog: generating column-level aggregation statistics for Apache Iceberg tables to accelerate queries. These statistics are utilized by cost-based optimizer (CBO) in Amazon Redshift Spectrum, resulting in improved query performance and potential cost savings. Apache Iceberg is an open table format that […]

AWS Glue is a serverless data integration service that enables you to run extract, transform, and load (ETL) workloads on your data in a scalable and serverless manner. One of the main advantages of using a cloud platform is its flexibility; you can provision compute resources when you actually need them. However, with this ease […]

This post is co-written with Amit Gilad, Alex Dickman and Itay Takersman from Cloudinary.  Enterprises and organizations across the globe want to harness the power of data to make better decisions by putting data at the center of every decision-making process. Data-driven decisions lead to more effective responses to unexpected events, increase innovation and allow […]

In this post, we delve into the key aspects of using Amazon EMR for modern data management, covering topics such as data governance, data mesh deployment, and streamlined data discovery. One of the key challenges in modern big data management is facilitating efficient data sharing and access control across multiple EMR clusters. Organizations have multiple […]

In this post, we show how to create an AWS Glue job that measures and monitors the data quality of a data pipeline using dynamic rules. We also show how to take action based on the data quality results.

In this post, we explore how to use Zingg’s entity resolution capabilities within an AWS Glue notebook, which you can later run as an extract, transform, and load (ETL) job. By integrating Zingg in your notebooks or ETL jobs, you can effectively address data governance challenges and provide consistent and accurate data across your organization.

We’re excited to announce the launch of the Apache Iceberg on AWS technical guide. Whether you are new to Apache Iceberg on AWS or already running production workloads on AWS, this comprehensive technical guide offers detailed guidance on foundational concepts to advanced optimizations to build your transactional data lake with Apache Iceberg on AWS.