![]() ![]() ![]() The interface has updated and you should see When you go to setup your Postgres database, do not click on Storage.You do not need to setup a load balancer for this flow.For your VM machine type, use E2 series.When you create the subnet, make sure to select a region that makes sense to your infastructure.You will need to enable the Compute Engine API.Make sure to create the instance in the desired project (whether an existing one or a new one).Key Notes not mentioned in Jostein Leira's Post: User account in Github/Gitlab/Bitbucket etc with permissions to create repositories and associate ssh deploy keys with them.User account in dbt Cloud with sufficient permissions to create database connections, repositories, and API keys. ![]() You will also need sufficient permissions (or a friend who has them :) ) to obtain an API token and secret from the Fivetran Admin console as described here In this example, we use Google Sheets as the connector source data. User account in Fivetran with permissions to create new connectors.User with access to run database operations in Snowflake.Google offers $300 in credits for new accounts, which should be more than enough to get this up and running. Source data configured in Fivetran - this guide uses Google Sheets as the source.Fivetran account with permission to upload data to Snowflake.Snowflake account with database, warehouse etc.It runs a simple aggregation of the input source data to summarize the average HP per pokemon catch_number. The dbt job run against this data is defined in this repository. "dbt_job_name": "pokemon_aggregation_job" Airflow XComs are used to share state among the tasks defined in the job. This is a simplified workflow meant to illustrate the coordination role Airflow can play between a data loading system like Fivetran and dbt. If you are already using Airflow, you may want to skip the implementation guide below and focus on the key parts of the python code which enable this workflow. It would also be feasible to log interim job status data using this setup, though we did not build it into the current python codebaseīelow is a system diagram with a brief description of each step in the process captures relevant data from a job run which could be shipped to downstream logging & analytics services.configurable approach which can be extended to handle additional Fivetran connectors and dbt job definitions.demonstrates orchestrating Fivetran and dbt in an event-driven pipeline.avoids complexity of re-creating dbt DAG in Airflow, which we've seen implemented at a few clients.Airflow code can be run from a managed service like Astronomer.logical isolation of data load (Fivetran), data transform (dbt) and orchestration (Airflow) functions.The code provided in this repository are intended as a demonstration to build upon and should not be utilized as a production-ready solution. The final step extracts the manifest.json from the dbt run results to capture relevant metadata for downstream logging, alerting and analysis. We use the Fivetran and dbt Cloud APIs to accomplish this, with Airflow managing the scheduling / orchestration of the job flow. In this example, our focus is on coordinating a Fivetran sync for loading data to a warehouse, and then triggering a dbt run in an event-driven pipeline. This is one way to orchetstrate dbt in coordination with other tools, such as Fivetran for data loading. If you have any questions about this, feel free to ask in our dbt Slack community. The purpose of this github repo is to provide an example of what an orchestration pipeline for Fivetran + dbt managed by Airflow would look like. ![]()
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