The data build tool (dbt) is an effective data transformation tool and it supports key AWS analytics services – Redshift, Glue, EMR and Athena. In part 4 of the dbt on AWS series, we discuss data transformation pipelines using dbt on Amazon EMR on EKS. Subsets of IMDb data are used as source and data models are developed in multiple layers according to the dbt best practices.
The data build tool (dbt) is an effective data transformation tool and it supports key AWS analytics services – Redshift, Glue, EMR and Athena. In part 3 of the dbt on AWS series, we discuss data transformation pipelines using dbt on Amazon EMR. Subsets of IMDb data are used as source and data models are developed in multiple layers according to the dbt best practices.
We will discuss how to set up a remote dev environment on an EMR cluster deployed in a private subnet with VPN and the VS Code remote SSH extension. Typical Spark development examples will be illustrated while sharing the cluster with multiple users. Overall it brings an effective way of developing Spark apps on EMR, which improves developer experience significantly.
We’ll discuss how to provision and manage Spark jobs on EMR on EKS with Terraform. Amazon EKS Blueprints for Terraform will be used for provisioning EKS, EMR virtual cluster and related resources. Also Spark job autoscaling will be managed by Karpenter where two Spark jobs with and without Dynamic Resource Allocation (DRA) will be compared.
We’ll discuss how to implement data warehousing ETL using Iceberg for data storage/management and Spark for data processing. A Pyspark ETL app will be used for demonstration in an EMR local environment. Finally the ETL results will be queried by Athena for verification.
We’ll discuss how to create a Spark local dev environment for EMR using Docker and/or VSCode. A range of Spark development examples are demonstrated and Glue Catalog integration is illustrated as well.
Change data capture (CDC) on Amazon MSK and ingesting data using Apache Hudi on Amazon EMR can be used to build an efficient data lake solution. In this post, we’ll build a Hudi DeltaStramer app on Amazon EMR and use the resulting Hudi table with Athena and Quicksight to build a dashboard.
Change data capture (CDC) on Amazon MSK and ingesting data using Apache Hudi on Amazon EMR can be used to build an efficient data lake solution. In this post, we’ll build CDC with Amazon MSK and MSK Connect.
Change data capture (CDC) on Amazon MSK and ingesting data using Apache Hudi on Amazon EMR can be used to build an efficient data lake solution. As a starting point, we’ll discuss the source database and CDC streaming infrastructure in the local environment.
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