chamikaramj commented on code in PR #36641: URL: https://github.com/apache/beam/pull/36641#discussion_r2487860054
########## sdks/python/apache_beam/yaml/examples/transforms/blueprint/iceberg_to_iceberg_streaming.yaml: ########## @@ -0,0 +1,65 @@ +# coding=utf-8 +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +# A pipeline that reads Append CDC events from Medallion Bronze Iceberg dataset +# and writes to Silver Iceberg dataset. + +pipeline: + type: chain + transforms: + # Step 1: Read CDC Append Records from Iceberg Table + - type: ReadFromIcebergCDC + name: ReadFromBronzeDataset + config: + table: "shipment_dataset_bronze.shipments" + catalog_name: "shipment_data" + catalog_properties: + type: "rest" + uri: "https://biglake.googleapis.com/iceberg/v1beta/restcatalog"; Review Comment: I think it's good to consider developing the main pipeline and testing separately. * Main pipeline should be something parameterized that we can use for templates / blueprints. This is our source of truth and contains the main code we need to to keep correct via testing etc. This should not have specific parameters (references to specific databases etc.) unless we want to make that the default for the template/blueprint. * A close enough pipeline that we want to test dynamically derived from above. In some cases this can be the original pipeline (for example, Kafka which can be run for a limited time). But for Pub/Sub we either need to inject mock transforms and/or cancel the pipeline using a runner specific mechanism (for example, Dataflow cancel operation [1]). [1] https://docs.cloud.google.com/dataflow/docs/guides/stopping-a-pipeline -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
