Category: AWS Glue

As builders, sometimes you want to dissect a customer experience, find problems, and figure out ways to make it better. That means going a layer down to mix and match primitives together to get more comprehensive features and more customization, flexibility, and freedom. In this post, we introduce Log Lake, a do-it-yourself data lake based on logs from CloudWatch and AWS CloudTrail.

The post illustrates the construction of a comprehensive CDC system, enabling the processing of CDC data sourced from Amazon Relational Database Service (Amazon RDS) for MySQL. Initially, we’re creating a raw data lake of all modified records in the database in near real time using Amazon MSK and writing to Amazon S3 as raw data. Later, we use an AWS Glue exchange, transform, and load (ETL) job for batch processing of CDC data from the S3 raw data lake.

In the era of big data, data lakes have emerged as a cornerstone for storing vast amounts of raw data in its native format. They support structured, semi-structured, and unstructured data, offering a flexible and scalable environment for data ingestion from multiple sources. Data lakes provide a unified repository for organizations to store and use […]

ATPCO is the backbone of modern airline retailing, enabling airlines and third-party channels to deliver the right offers to customers at the right time. ATPCO’s reach is impressive, with its fare data covering over 89% of global flight schedules. In this post, using one of ATPCO’s use cases, we show you how ATPCO uses AWS services, including Amazon DataZone, to make data discoverable by data consumers across different business units so that they can innovate faster. We encourage you to read Amazon DataZone concepts and terminologies first to become familiar with the terms used in this post.

After careful consideration, we have made the decision to close new customer access to AWS Data Pipeline, effective July 25, 2024. AWS Data Pipeline existing customers can continue to use the service as normal. AWS continues to invest in security, availability, and performance improvements for AWS Data Pipeline, but we do not plan to introduce […]

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 […]