![]() Once you have uploaded it you should see it in the main Apache Airflow UI. The rest of the DAG is the same as the blog post, and you should deploy this to your DAGS folder via your preferred method (I use a very simple CI/CD system which you can replicate for yourself in my blog post, A simple CI/CD system for your Amazon Managed Workflows for Apache Airflow development workflow If you prefer, you could change the configuration of MWAA to look for variables in AWS Secrets Manager, and then manage these values via CDK perhaps - for this post I am keeping it simple and just using standard variables through the Apache Airflow UI. MWAA stores these securely in the MWAA metstore database. Once you have done this, you should have a list of the variables with the values listed. Once amended you can then import these into MWAA via the Apache Airflow UI. ![]() You WILL need to modify the last three variables (redshift_iam_arn, redshift_secret_arn and s3_bucket_name) using the values that were output as part of the Redshift cluster build. Once you have changed this values for your own environment, you can deploy the stack. The password will be auto generated and stored in AWS Secrets Manager.įinally, make sure you adjust your environment details (region/account) to reflect your own environment. The next three configure the Amazon Redshift environment, providing the cluster name (redshiftclustername), the default database that will be created (redshiftdb) and then the name of the Redshift admin user name (redshiftusername). The next one (mwaadag) is the location of the MWAA Dags folder, the (mwaa-sg) is the name of the security group for your MWAA environment, which the deployment will amend to add an additional ingress rule for Redshift, and finally (mwaa-vpc-id) the VPC id which is used to populate the Redshift subnet group to enable connectivity. This should not exist or the deployment will fail. I have commented the code, but the first parameter (redshifts3location) is the name of the NEW S3 bucket you will create. To make this easy, I have created a CDK app that builds everything you need.Įnter fullscreen mode Exit fullscreen mode The first thing we need to do is setup the Amazon Redshift cluster. Make sure you cleanup/delete all the resources after you have finished! When I ran this and took a look at my AWS bill, it was around $50 for the 5-6 hours I was playing around putting this blog post together. NOTE! You will see some output in this walkthrough that contains aws credentials (aws_access/secret_keys) but don’t worry these are not real ones! You will find source code for this post at the usual place, my residence over on GitHub ![]() A MWAA environment up and running - may I suggest you check out some of my earlier blog post like this one if you are familiar with AWS CDK or this one, if you are not.The latest/up to date aws cli - at least version 1.19.73 / 2.24.An AWS account with the right level of privileges.In that second part we meet the main actor, the RedshiftToS3Transfer operator, and get to know how to set it up and get it going. What I hope you will learn by reading this post is a) how to replicate the original workflow from the launch post for yourself, and b) an additional step of taking the tables from that Amazon Redshift database and exporting them to Amazon S3, a common use case that data engineers are asked to do. As is often the way, diving into that post (creating a workflow to take some source files, transform them and then move them into Amazon Redshift) led me down some unexpected paths to here, this post. I found the perfect catalyst in the way of the original launch post of Amazon Managed Workflows for Apache Airflow (MWAA). ![]() Inspired by a recent conversation within the Apache Airflow open source slack community, I decided to channel the inner terrier within me to tackle this particular issue, around getting an Apache Airflow operator (the protagonist for this post) to work.
0 Comments
Leave a Reply. |