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ELT with Airflow and Databricks

Databricks is a popular unified data and analytics platform built around fully managed Apache Spark clusters. Using the Astro Databricks provider package, you can create a Databricks Workflow from Databricks notebooks and run the Databricks Workflow in an Airflow DAG. This lets you use Airflow's orchestration features in combination with Databricks' cheapest compute. To get data in and out of Databricks, you can use the open-source Astro Python SDK, which greatly simplifies common ELT tasks like loading data and creating pandas DataFrames from data in your warehouse.

This example uses a DAG to extract data from three local CSV files containing the share of solar, hydro and wind electricity in different countries over several years, run a transformation on each file, load the results to S3, and create a line chart of the aggregated data.

DAG graph screenshot

After the DAG runs, a graph appears in the include directory which shows the combined percentage of solar, hydro and wind energy in a country you selected.

SHW graph


For more detailed instructions on using Databricks with the Astro Databricks provider, see the Databricks tutorial.

Before you start

Before you try this example, make sure you have:

Clone the project

Clone the example project from the Astronomer GitHub.

Edit the project

The project contains a file called .env_example which includes placeholder values for your credential details. After you clone the project, create a file called .env, copy the contents of .env_example into it, and delete .env_example. This is required because only .env is included in the list of files which Git will ignore when creating commits (.gitignore), and cloning an .env file directly will cause it to not be ignored the next time you push it to a GitHub repository.

In, replace the DATABRICKS_LOGIN_EMAIL, S3_BUCKET and AWS_REGION variables with your own values. Change the COUNTRY to analyze a different country. For available countries, refer to the source data in the include folder.

Run the project

To run the example project, first make sure Docker Desktop is running. Then, open your project directory and run:

astro dev start

This command builds your project and spins up 4 Docker containers on your machine to run it. After the command finishes, open the Airflow UI at https://localhost:8080/ and trigger the renewable_analysis_dag DAG using the play button.

Project contents

Data source

This example analyzes the share of solar, wind and hydro electricity for different countries. The full source data including other electricity modalities can be found in this Kaggle dataset derived from Our World in Data (License CC BY 4.0).

The subset of data used in this example can be found in the include folder of the accompanying GitHub repository.

Project code

This project consists of one DAG, renewable_analysis_dag, which performs an ELT process. It uses the Astro Python SDK and Astro Databricks provider to orchestrate two Databricks notebooks in a Databricks Workflow.

First, three separate CSV files, each containing the energy percentages by country for solar, hydro, and wind power respectively, are loaded into new tables in the data warehouse using the Astro Python SDK load file operator. The operator uses dynamic task mapping to create a mapped task instance for each dictionary in the list provided to the .expand_kwargs method. These tasks are then executed in parallel.

in_tables = aql.LoadFileOperator.partial(
"input_file": File(path=SOLAR_CSV_PATH),
"output_table": Table(conn_id=DB_CONN_ID, name="solar"),
"input_file": File(path=HYDRO_CSV_PATH),
"output_table": Table(conn_id=DB_CONN_ID, name="hydro"),
"input_file": File(path=WIND_CSV_PATH),
"output_table": Table(conn_id=DB_CONN_ID, name="wind"),

Next, the select_countries task uses the Astro Python SDK @aql.transform decorator to execute a SQL query on each of the three tables created in the in_tables task. The query selects rows only for the specified COUNTRY defined at the start of the DAG code and saves the results in three new tables.

The .map method converts the output of select_countries to a list of dictionaries containing the in_table and output_table parameters for each task instance using a lambda expression. The .expand_kwargs method then dynamically maps tasks based on each pair of in_table and output_table.

def select_countries(in_table, country):
return """SELECT * FROM {{ in_table }} WHERE "Entity" = {{ country }};"""

### [...]

country_tables = select_countries.partial(country=COUNTRY).expand_kwargs(
lambda x: {
"in_table": x,
"output_table": Table(conn_id=DB_CONN_ID, name=f"{}_country"),

The third task uses the Astro Python SDK ExportToFileOperator to export data from the three temporary tables to an object storage.

Again, this task uses a lambda expression, .map and dynamic task mapping to create separate tasks and export a file for each table generated by the previously mapped tasks. This allows you to customize the number of files the DAG analyzes with only small changes to DAG code.

save_files_to_S3 = aql.ExportToFileOperator.partial(
lambda x: {
"input_data": x,
"output_file": File(

Using the Astro Python SDK in the first three steps allows you to easily switch between data warehouses and object storage solutions, simply by changing the connection ID. After these extract steps are completed, a task group created by the Astro Databricks provider DatabricksWorkflowTaskGroup completes the transformations:

task_group = DatabricksWorkflowTaskGroup(

with task_group:
notebook_1 = DatabricksNotebookOperator(
notebook_2 = DatabricksNotebookOperator(
notebook_1 >> notebook_2

This task group contains three tasks:

  • launch: This task is automatically created by the task group and launches a job cluster with the specifications provided in the job_cluster_spec.
  • join_data: This task executes the first notebook in the Databricks Workflow, which joins the data from the three CSV files created in the object storage into a single CSV file. View the full notebook code here. Note that if you are using an object storage other than S3 you will need to make changes to the code enclosed in # --------- AWS S3 specific --------- #.
  • transform_data: This task executes the second notebook in the Databricks Workflow, which fetches the CSV created by the join_data task, creates a new column SHW% by summing the % points for solar, hydro, and wind energy for each year, and saves the result as a CSV in the object storage. View the full notebook code here. Just like in the previous notebook, you might have to adjust this for other object storage solutions.

After the task group is completed, two tasks run in parallel.

The delete_intake_files_S3 task uses the S3DeleteObjectsOperator to clean up files which are no longer needed in the S3 bucket. If you use a different object storage solution, you will need to define this task using an operator from the corresponding provider. See the Astronomer Registry to explore available providers and operators.

delete_intake_files_S3 = S3DeleteObjectsOperator(

The create_graph task uses the Astro Python SDK @aql.dataframe decorator to load the information from the final CSV into a pandas DataFrame. The task creates a line plot of the % of solar, hydro, and wind energy in the specified country over time using seaborn and matplotlib, then saves the graph in the include folder of the project repository.

def create_graph(df: pd.DataFrame):
sns.lineplot(x="Year", y="SHW%", data=df)
plt.title(f"% of Solar, Hydro and Wind in the {COUNTRY}")
plt.ylabel("Combined SHW (in %)")

# [...]

load_file_to_db = aql.load_file(
input_file=File(path=DATABRICKS_RESULT_FILE_PATH, conn_id=AWS_CONN_ID),

Finally, the Astro Python SDK aql.cleanup() task removes all temporary tables in the data warehouse that were created by Astro Python SDK tasks.

See also

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