Real-time data streaming with AWS DataBrew

Absolutely, here's a simple Pulumi Program for real-time data streaming with AWS DataBrew using AWS Glue Streams.

This program creates a DataBrew Dataset and a DataBrew Recipe in AWS. Note that it can be extended depending on your specific needs and data source.

Please replace your-bucket-name and your-key to point to your S3 bucket and data file.

using Pulumi;
using Pulumi.AwsNative.S3;
using Pulumi.AwsNative.Databrew;

class Program
{
    static void Main()
    {
        Deployment.Run(() =>
        {
            // Create a bucket
            var bucket = new Pulumi.AwsNative.S3.Bucket("my-bucket");
        
            // Create a DataBrew dataset
            // This dataset will point to a file in S3
            var input = Output.Tuple(bucket.Arn, bucket.BucketName)
                .Apply(t =>
                {
                    return new Dataset("my-dataset", new DatasetArgs
                    {
                        Format = "CSV",
                        Input = new InputArgs
                        {
                            S3InputDefinition = new S3InputDefinitionArgs
                            {
                                Bucket = t.Item2, // the bucket name
                                Key = "your-key"  // the location of the data file in S3
                            }
                        }
                    });
                });
            
            // Create a DataBrew recipe
            var recipe = new Recipe("my-recipe", new RecipeArgs
            {
                Steps = new List<StepArgs>
                {
                    new StepArgs  // a simple step that renames a column
                    {
                        Action = new ActionArgs
                        {
                            Operation = "RENAME",
                            Parameters = new Dictionary<string, object>
                            {
                                ["sourceColumnName"] = "old_column_name",
                                ["targetColumnName"] = "new_column_name"
                            }
                        }
                    }
                }
            });
            
            return new Dictionary<string, object>
            {
                    { "datasetName", input.Name },
                    { "recipeName", recipe.Name }
            };
        });
    }
}

For full information of input parameters configuration and more examples, refer to these resources in Pulumi Registry:

1. aws-native.databrew.Dataset
2. aws-native.databrew.Recipe

Real-time data streaming is possible only if the sources are stream sources like Kafka or Kinesis in AWS, in which case you might want to check out the aws-native.databrew.Job which provides more advanced features including transformations with AWS Glue and AWS Lambda.