Category: Amazon Athena

Glue Data Catalog views is a new feature of the AWS Glue Data Catalog that customers can use to create a common view schema and single metadata container that can hold view-definitions in different dialects that can be used across engines such as Amazon Redshift and Amazon Athena. In this blog post, we will show how you can define and query a Data Catalog view on top of open source table formats such as Iceberg across Athena and Amazon Redshift. We will also show you the configurations needed to restrict access to the underlying database and tables. To follow along, we have provided an AWS CloudFormation template.

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.

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 […]

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 […]

Many organizations use external identity providers (IdPs) such as Okta or Microsoft Azure Active Directory to manage their enterprise user identities. These users interact with and run analytical queries across AWS analytics services. To enable them to use the AWS services, their identities from the external IdP are mapped to AWS Identity and Access Management […]

Apache Hudi was originally developed by Uber in 2016 to bring to life a transactional data lake that could quickly and reliably absorb updates to support the massive growth of the company’s ride-sharing platform. Apache Hudi is now widely used to build very large-scale data lakes by many across the industry. Today, Hudi is the […]

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.

This is a guest post co-authored with Kanehito Miyake, Engineer at Fujitsu Japan.  Fujitsu Limited was established in Japan in 1935. Currently, we have approximately 120,000 employees worldwide (as of March 2023), including group companies. We develop business in various regions around the world, starting with Japan, and provide digital services globally. To provide a […]

In this post, we discuss how to implement bucketing on AWS data lakes, including using Athena CTAS statement and AWS Glue for Apache Spark. We also cover bucketing for Apache Iceberg tables.

Many organizations operate data lakes spanning multiple cloud data stores. This could be for various reasons, such as business expansions, mergers, or specific cloud provider preferences for different business units. In these cases, you may want an integrated query layer to seamlessly run analytical queries across these diverse cloud stores and streamline your data analytics […]