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| CREATE OR REPLACE VIEW `tulisanmedium.digitalviews.top_10_most_film_by_director` | |
| OPTIONS( | |
| description="Top 5 most film by director for digital content purpose. requested by digital team" | |
| ) | |
| AS | |
| select | |
| count(distinct title) number_of_films | |
| ,director | |
| ,string_agg(distinct title order by title limit 5) as films | |
| from |
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| AWSTemplateFormatVersion: '2010-09-09' | |
| Transform: 'AWS::Serverless-2016-10-31' | |
| Description: An AWS Serverless Specification template describing your function. | |
| Resources: | |
| dogreat: | |
| Type: 'AWS::Serverless::Function' | |
| Properties: | |
| Handler: lambda_function.lambda_handler | |
| Runtime: python3.6 | |
| CodeUri: s3://do-great-bucket/source.zip |
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| version: 0.2 | |
| env: | |
| variables: | |
| INPUT_TEMPLATE: "appspec.yaml" | |
| S3_BUCKET: "do-great-bucket" | |
| phases: | |
| install: | |
| commands: | |
| - wget https://bootstrap.pypa.io/get-pip.py | |
| - python get-pip.py |
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| PUT series/_mappings/movie | |
| { | |
| "properties": { | |
| "film_to_franchise": { | |
| "type": "join", | |
| "eager_global_ordinals": true, | |
| "relations": { | |
| "franchise": "film" | |
| } | |
| }, |
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| PUT series/movie/1?routing=1 | |
| { | |
| "id": "1", | |
| "film_to_franchise": { | |
| "name": "franchise" | |
| }, | |
| "title": "Star Wars" | |
| } | |
| PUT series/movie/1196?routing=1 | |
| { |
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| curl -XPOST "http://localhost:8083/connectors" -H 'Content-Type: application/json' -d' | |
| { | |
| "name": "debezium", | |
| "config": { | |
| "connector.class": "io.debezium.connector.mysql.MySqlConnector", | |
| "database.hostname": "localhost", | |
| "database.port": "3306", | |
| "database.user": "root", | |
| "database.password": "", | |
| "database.server.id": "184054", |
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| import json | |
| from kafka import KafkaConsumer | |
| from google.cloud import bigquery | |
| if __name__ == "__main__": | |
| # bigwuery configuration | |
| bigquery_client = bigquery.Client() | |
| dataset_ref = bigquery_client.dataset('DATASET_NAME') | |
| table_ref = dataset_ref.table('TABLE_NAME') |
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| import json | |
| from kafka import KafkaConsumer | |
| from google.cloud import bigquery | |
| if __name__ == "__main__": | |
| # bigwuery configuration | |
| bigquery_client = bigquery.Client() | |
| dataset_ref = bigquery_client.dataset('DATASET_NAME') | |
| table_ref = dataset_ref.table('TABLE_NAME') |
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| import json | |
| import apache_beam as beam | |
| import apache_beam.transforms.window as window | |
| from apache_beam.options.pipeline_options import PipelineOptions | |
| from apache_beam.options.pipeline_options import StandardOptions | |
| from apache_beam.io.gcp.internal.clients import bigquery | |
| if __name__ == "__main__": | |
| # create pipeline |
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| import argparse | |
| def list_subscriptions_in_topic(project_id, topic_name): | |
| from google.cloud import pubsub_v1 | |
| publisher = pubsub_v1.PublisherClient() | |
| topic_path = publisher.topic_path(project_id, topic_name) | |
| for subscription in publisher.list_topic_subscriptions(topic_path): | |
| print(subscription) | |
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