As organizations increasingly adopt modern data stacks, the combination of dbt and AWS Analytics services emerged as a powerful pairing for analytics engineering at scale. This session will explore proven strategies and hard-learned lessons for optimizing this technology stack to use dbt-athena, dbt-redshift, and dbt-glue to deliver reliable, performant data transformations. We will also cover case studies, best practices, and modern lakehouse scenarios with Apache Iceberg and Amazon S3 Tables.

Delta Lake is an open source project that helps implement modern data lake architectures commonly built on cloud storages. With Delta Lake, you can achieve ACID transactions, time travel queries, CDC, and other common use cases on the cloud.

There are a lot of use cases of Delta tables on AWS. AWS has invested a lot in this technology, and now Delta Lake is available with multiple AWS services, such as AWS Glue Spark jobs, Amazon EMR, Amazon Athena, and Amazon Redshift Spectrum. AWS Glue is a serverless, scalable data integration service that makes it easier to discover, prepare, move, and integrate data from multiple sources. With AWS Glue, you can easily ingest data from multiple data sources such as on-prem databases, Amazon RDS, DynamoDB, MongoDB into Delta Lake on Amazon S3 even without expertise in coding.

This session will demonstrate how to get started with processing Delta Lake tables on Amazon S3 using AWS Glue, and querying from Amazon Athena, and Amazon Redshift. The session also covers recent AWS service updates related to Delta Lake.

Talk by: Noritaka Sekiyama and Akira Ajisaka

Here’s more to explore: Why the Data Lakehouse Is Your next Data Warehouse: https://dbricks.co/3Pt5unq Lakehouse Fundamentals Training: https://dbricks.co/44ancQs

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Discover how to harness AWS Glue for your ETL and data analysis workflows with "Serverless ETL and Analytics with AWS Glue." This comprehensive guide introduces readers to the capabilities of AWS Glue, from building data lakes to performing advanced ETL tasks, allowing you to create efficient, secure, and scalable data pipelines with serverless technology. What this Book will help me do Understand and utilize various AWS Glue features for data lake and ETL pipeline creation. Leverage AWS Glue Studio and DataBrew for intuitive data preparation workflows. Implement effective storage optimization techniques for enhanced data analytics. Apply robust data security measures, including encryption and access control, to protect data. Integrate AWS Glue with machine learning tools like SageMaker to build intelligent models. Author(s) The authors of this book include experts across the fields of data engineering and AWS technologies. With backgrounds in data analytics, software development, and cloud architecture, they bring a depth of practical experience. Their approach combines hands-on tutorials with conceptual clarity, ensuring a blend of foundational knowledge and actionable insights. Who is it for? This book is designed for ETL developers, data engineers, and data analysts who are familiar with data management concepts and want to extend their skills into serverless cloud solutions. If you're looking to master AWS Glue for building scalable and efficient ETL pipelines or are transitioning existing systems to the cloud, this book is ideal for you.