Airflow offers numerous integrations with third-party tools, including the Airbyte Airflow Operator and can be run locally using Docker Compose. Airflow pipelines are defined in Python, which are then converted into Directed Acyclic Graphs (DAG). Airflow offers the ability to develop, monitor, and schedule workflows programmatically. Apache AirflowĪpache Airflow is an open-source data orchestration tool. Airbyte's native integration with dbt is used to run the transformations. In this modern data stack example, dbt applies a simple transformation on the ingested data using a SQL query. dbt provides a cloud-hosted option and a CLI, a Python API and integration with Airflow. dbt replaces the usual boilerplate DDL/DML required to transform data with simple modular SQL SELECT statements and handles dependency management. dbtĭbt is an open-source data transformation tool that relies on SQL to build production-grade data pipelines. In this modern data stack example, BigQuery works as the data store. It features a columnar data structure and can query a large volume of data very quickly. Google BigQuery is a highly scalable data warehouse. In this modern data stack example, Airbyte is used to replicate data from a CSV file to BigQuery. Airbyte offers a self-hosted option with Docker Compose that you can run locally. Airbyte can replicate data from applications, APIs, and databases into data warehouses and data lakes. With Airbyte, you can set up a data pipeline in minutes thanks to its extensive collection of pre-built connectors. AirbyteĪirbyte is an open-source data integration tool. Apart from BigQuery, all the other tools are open source.īefore we set up the project, let’s briefly look at each tool used in this example of a modern data stack to make sure you understand their responsibilities. Finally, you’ll use Airflow to schedule a daily job to sync the data from the source. The pipeline uses Airbyte to read a CSV file into BigQuery, transform the data with dbt and visualize the data with Superset. In this tutorial, I demonstrate how to use Docker Compose to quickly set up a modern data stack using Airbyte, BigQuery, dbt, Airflow, and Superset. A business intelligence tool like Superset or Metabase A data transformation tool like dbt or DataformĤ. A data warehouse like Google BigQuery or Snowflakeģ. These tools include but are not limited to:Ģ. A modern data stack is a suite of data tools that reduce the complexity to create a data pipeline. Over the past few years, many tools have emerged that apply to each stage of the data pipeline. Every stage of the data pipeline needs to be as efficient and cost-effective as possible. But to be useful, it needs to be gathered, transformed and visualized. Today data is the most valuable resource of any business.
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