Category: HAQM Athena

In this post, we demonstrate how to use Lake Formation for read access while continuing to use AWS Identity and Access Management (IAM) policy-based permissions for write workloads that update the schema and upsert (insert and update combined) data records into the Iceberg tables.

HAQM SageMaker Lakehouse is a unified, open, and secure data lakehouse that now seamlessly integrates with HAQM 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 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.

With HAQM SageMaker Lakehouse unified data connectivity, you can confidently connect, explore, and unlock the full value of your data across AWS services and achieve your business objectives with agility. This post demonstrates how SageMaker Lakehouse unified data connectivity helps your data integration workload by streamlining the establishment and management of connections for various data sources.

In this post, we use dbt for data modeling on both HAQM Athena and HAQM Redshift. dbt on Athena supports real-time queries, while dbt on HAQM Redshift handles complex queries, unifying the development language and significantly reducing the technical learning curve. Using a single dbt modeling language not only simplifies the development process but also automatically generates consistent data lineage information. This approach offers robust adaptability, easily accommodating changes in data structures.

In this post, we show how to connect to, govern, and run federated queries on data stored in Redshift, DynamoDB (Preview), and Snowflake (Preview). To query our data, we use Athena, which is seamlessly integrated with SageMaker Unified Studio. We use SageMaker Lakehouse to present data to end-users as federated catalogs, a new type of catalog object. Finally, we demonstrate how to use column-level security permissions in AWS Lake Formation to give analysts access to the data they need while restricting access to sensitive information.

ANZ Institutional Division has transformed its data management approach by implementing a federated data platform based on data mesh principles. This shift aims to unlock untapped data potential, improve operational efficiency, and increase agility. The new strategy empowers domain teams to create and manage their own data products, treating data as a valuable asset rather than a byproduct. This post explores how the shift to a data product mindset is being implemented, the challenges faced, and the early wins that are shaping the future of data management in the Institutional Division.

We are excited to announce that the dbt adapter for HAQM Athena is now officially supported in dbt Cloud. This integration enables data teams to efficiently transform and manage data using Athena with dbt Cloud’s robust features, enhancing the overall data workflow experience. In this post, we discuss the advantages of dbt Cloud over dbt Core, common use cases, and how to get started with HAQM Athena using the dbt adapter.

HAQM DataZone recently announced the expansion of data analysis and visualization options for your project-subscribed data within HAQM DataZone using the HAQM Athena JDBC driver. In this post, you learn how the recent enhancements in HAQM DataZone facilitate a seamless connection with Tableau. By integrating Tableau with the comprehensive data governance capabilities of HAQM DataZone, we’re empowering data consumers to quickly and seamlessly explore and analyze their governed data.

HAQM DataZone now launched authentication support through the  HAQM Athena JDBC driver, allowing data users to seamlessly query their subscribed data lake assets via popular business intelligence (BI) and analytics tools like Tableau, Power BI, Excel, SQL Workbench, DBeaver, and more. This integration empowers data users to access and analyze governed data within HAQM DataZone using familiar tools, boosting both productivity and flexibility.