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.

EMR on EKS is a deployment option in EMR that allows you to automate the provisioning and management of open-source big data frameworks on EKS. It can be an effective way of running spark jobs to manage big data (as well as non-big data) workloads. In this post, we’ll discuss EMR on EKS with simple and elaborated examples.

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.