12/31/2023 0 Comments Airflow performance testing![]() With Airflow you can use operators to transform data locally (PythonOperator, BashOperator.), remotely (SparkSubmitOperator, KubernetesPodOperator…) or in a data store (PostgresOperator, BigQueryInsertJobOperator.). This follows the traditional ETL pipeline architecture where the transformation logic happens between the extract and load steps. PostgresToGCSOperator -> GCSToLocalFilesystemOperator -> PythonOperator -> LocalFilesystemToGCSOperator -> GCSToBigQueryOperator You end up creating the following example of an Airflow ETL DAG. For example, there is no operator to retrieve data from Segment, but you can use the SegmentHook to interact with Segment’s API.Īt first, you may be tempted to build an ETL pipeline where you extract your data from Postgres to a file storage, transform the data locally with the PythonOperator and then load the transformed data to BigQuery. When you find no operator to interact with your data you may search for Airflow Hooks to connect with an external system. Your company is already using Airflow, so you start searching for Airflow ETL operators to extract, transform and load data within the built-in operators and provider packages. From this point, you may want to centralize all your business data in a single data warehouse, such as Google BigQuery. The marketing, sales and product teams have their data stored on third-party applications such as Google Ads, Salesforce and Segment. Imagine you have your application data stored in a Postgres database. □ The Good Airflow Operators for ETL pipelines The good news is that you can easily integrate Airflow with Airbyte and dbt. Notably, using Airbyte for the extract and load steps and dbt for the transformation step. The suggested alternative is to keep using Airflow to schedule and monitor ELT pipelines, but use other open-source projects that are better suited for the extract, load and transform steps. Finally, we argue why Airflow ETL operators won’t be able to cover the long tail of integrations for your business data. Then, we share some challenges you may encounter when attempting to load data incrementally with Airflow DAGs. In this article, we review how to use Airflow ETL operators to transfer data from Postgres to BigQuery with the ETL and ELT paradigms. ![]() But is using Airflow for your ETL pipelines a good practice today? I’ve also used Airflow transformation operators to preprocess data for machine learning algorithms. For example, I’ve previously used Airflow transfer operators to replicate data between databases, data lakes and data warehouses. Many data teams also use Airflow for their ETL pipelines. Apache Airflow is a popular open-source workflow management platform. ![]()
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