We will discuss how to configure the Kafka consumer to seek offsets by timestamp where topic partitions are dynamically assigned by subscription. Docker Compose is used for building a single node Kafka cluster and running multiple consumer instances.
The data build tool (dbt) is an effective data transformation tool and it supports key AWS analytics services - Redshift, Glue, EMR and Athena. In the last part of the dbt on AWS series, we discuss data transformation pipelines using dbt on Amazon Athena. 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 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.
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 2 of the dbt on AWS series, we discuss data transformation pipelines using dbt on AWS Glue. 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 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.