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 1 of the dbt on AWS series, we discuss data transformation pipelines using dbt on Redshift Serverless. 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 limitations of the Lambda invoke function operator of Apache Airflow and create a custom Lambda operator. The custom operator extends the existing one and it reports the invocation result of a function correctly and records the exact error message from failure.

We'll discuss how to build a serverless data processing application using the Serverless Application Model (SAM). A Lambda function is developed, which is triggered whenever an object is created in a S3 bucket. 3rd party packages are necessary for data processing and they are made available by Lambda layers.

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.

We'll continue the discussion of a Change Data Capture (CDC) solution with a schema registry and its deployment to AWS. All major resources are deployed in private subnets and VPN is used to access them in order to improve developer experience. The Apicurio registry is used as the schema registry service and it is deployed as an ECS service. In order for the connectors to have access to the registry, the Confluent Avro Converter is packaged together with the connector sources. The post ends with illustrating how schema evolution is managed by the schema registry.

We'll discuss a Change Data Capture (CDC) architecture with a schema registry. As a starting point, a local development environment is set up using Docker Compose. The Debezium and Confluent S3 connectors are deployed with the Confluent Avro converter and the Apicurio registry is used as the schema registry service. A quick example is shown to illustrate how schema evolution can be managed by the schema registry.

We'll discuss how to set up a development infrastructure on AWS with Terraform. Terraform is used as an effective way of managing resources on AWS. An Aurora PostgreSQL cluster is created in a private subnet and SoftEther VPN is configured to access the database from the developer machine.