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Mastering the Databricks API: Working with Data Pipelines at Scale
Databricks APIs enable programmatic interaction with Databricks, allowing users to automate workflows, manage clusters, execute jobs, and access data. These APIs support authentication via personal access tokens, OAuth, or Azure Active Directory.
In this article, I will walk you through an in-depth, hands-on journey through the Databricks REST API. Whether you're new to Databricks or looking to optimize your existing workflows, this guide will help you master key operations, including authentication, job management, and integration with external systems.
By the end of this article, you will have a clear blueprint for:
- Setting up secure API authentication using personal access tokens.
- Utilizing API endpoints to create, update, and run jobs.
- Applying best practices to enhance performance, optimize costs, and handle errors.
- Integrating Databricks with other platforms and orchestration tools.
If you are totally new to Databricks and want to pick it up quickly, read out blog post, How to Learn Databricks: A Beginner’s Guide to the Unified Data Platform, which will help you understand the core features and applications of Databricks, and also provide you a structured path to start your learning.
Why Use the Databricks API?
The Databricks REST API unlocks powerful capabilities for developers and data engineers, enabling them to manage resources programmatically and streamline workflows. Here’s how the Databricks API can make a big difference:
Reduced manual overhead in job scheduling and cluster management
The Databricks API allows users to automate tasks such as job scheduling and cluster management. This reduces the manual effort required to manage these processes, freeing up time for more strategic activities. By automating repetitive tasks, organizations can improve efficiency and reduce the likelihood of human error, which can lead to costly mistakes or downtime.
Seamless integrations with external systems
The Databricks API is designed for flexibility, enabling integration with external platforms such as orchestration tools like Apache Airflow or Azure Data Factory.
You can also use Databricks API for monitoring and alerting systems such as Prometheus or Datadog. Databricks API is also used in data ingestion pipelines from tools like Kafka or RESTful data services. These integrations help build end-to-end workflows that bridge data engineering and analytics across multiple systems.
Scalability for thousands of automated pipelines
The Databricks API supports the creation and management of thousands of automated pipelines. It effortlessly scales to manage thousands of jobs running in parallel and dynamic cluster scaling based on workload demand. This scalability ensures that your infrastructure adapts seamlessly to business needs, even at enterprise scale.
Core API Operations and Examples
The Databricks REST API offers a wide array of operations for managing jobs, clusters, files, and access permissions. Below, I will explore key API operations with practical examples to help you build and automate robust workflows.
Jobs
Jobs are the backbone of automated workflows in Databricks. The API allows you to create, manage, and monitor jobs seamlessly.
Creating a job
Use the POST /API/2.1/jobs/create
endpoint to define a new job. For example, the following JSON payload creates a new Databricks job named My Job
and runs a notebook located at /path/to/notebook
. The payload then uses an existing cluster with the ID cluster-id
, avoiding the need to create a new one. If the job fails, the payload will send an email notification to user@example.com
.
{
"name": "My Job",
"existing_cluster_id": "cluster-id",
"notebook_task": {
"notebook_path": "/path/to/notebook"
},
"email_notifications": {
"on_failure": ["user@example.com"]
}
}
Listing and getting job details
You can use the endpoint GET /api/2.1/jobs/list
to retrieve a list of all jobs in your workspace. For example, the following sample command will list all the jobs:
curl -n -X GET \
https://<databricks-instance>.cloud.databricks.com/api/2.1/jobs/list
Triggering runs
Use the endpoint POST /api/2.1/jobs/run-now
to trigger an immediate run of a job.
curl -n -X POST \
https://<databricks-instance>.cloud.databricks.com/api/2.1/jobs/run-now?job_id=123456789
Deleting jobs and runs
While the latest API version does not have a direct endpoint for deleting jobs, you can manage job runs and their lifecycle through other endpoints. Use the Delete a Job: POST /api/2.1/jobs/delete
endpoint to delete a job and POST /api/2.1/jobs/runs/cancel
to cancel a run.
Monitoring runs, error handling, and logs
Use GET /api/2.1/jobs/runs/get
to monitor a job’s status. Retrieve logs or handle errors using the run_state
field.
Clusters
Clusters provide the computing resources for running jobs. The API simplifies cluster lifecycle management.
Creating and editing clusters
Create a cluster with POST /api/2.1/clusters/create
. For example, you can have the following in the request body.
{
"cluster_name": "Example Cluster",
"spark_version": "12.2.x-scala2.12",
"node_type_id": "i3.xlarge",
"num_workers": 4
}
Then, run the sample command below to create this cluster.
curl -n -X POST \
-H 'Content-Type: application/json' \
-d '{"cluster_name": "My Cluster", "spark_version": "7.3.x-scala2.12", "node_type_id": "i3.xlarge", "num_workers": 2}' \
https://<databricks-instance>.cloud.databricks.com/api/2.1/clusters/create
Starting, restarting, resizing, and terminating clusters
Use the following APIs to:
-
Start Cluster:
POST /api/2.1/clusters/start
-
Restart Cluster:
POST /api/2.1/clusters/restart
-
Resize Cluster:
PATCH /api/2.1/clusters/edit
-
Terminate Cluster:
POST /api/2.1/clusters/delete
Best practices for ephemeral vs. all-purpose clusters
Consider the following best practices for:
- Ephemeral Clusters: Use for jobs that require dynamic resource allocation and can be terminated after job completion.
- All-Purpose Clusters: Suitable for continuous workloads where clusters are reused across multiple jobs.
DBFS (Databricks File System)
DBFS enables file storage and retrieval in Databricks.
Uploading and downloading files
You can use the API POST /api/2.1/dbfs/upload
to upload files to DBFS in chunks (multipart) or when handling large files (>1MB). The GET /api/2.1/dbfs/read
API is used to download files.
Listing directories and handling large file operations
Use GET /api/2.1/dbfs/list
to explore directories and manage files programmatically. For large file uploads, use chunked uploads with POST /api/2.1/dbfs/put
and the overwrite
parameter.
Secrets and authentication
The Secrets API ensures secure storage and retrieval of sensitive information.
Storing credentials safely with the Secrets API
Use the POST /api/2.1/secrets/create
endpoint to create a new secret for storing sensitive information.
For example, you may create the request body in JSON as follows:
{
"scope": "my-scope",
"key": "my-key",
"string_value": "my-secret-value"
}
Then, create the secret using the following command:
curl -n -X POST \
-H 'Content-Type: application/json' \
-d '{"scope": "my-scope", "key": "my-key", "string_value": "my-secret-value"}' \
https://<databricks-instance>.cloud.databricks.com/api/2.1/secrets/create
Retrieving secrets for integration
Use the GET /API/2.1/secrets/get
endpoint to retrieve secrets for external integrations.
Permissions and access controls
The Databricks platform enables granular control over access to jobs and resources.
Setting and updating permissions
Use the POST /api/2.1/permissions/jobs/update
endpoint to assign roles like owners or viewers. For example, the following payload is used to update permissions:
{
"access_control_list": [
{
"user_name": "user@example.com",
"permission_level": "CAN_MANAGE"
}
]
}
Role-Based Access Control (RBAC)
Use Databricks' built-in role-based access control (RBAC) to manage permissions across different roles within your organization.
Databricks API Rate Limits, Error Handling, and Troubleshooting
When working with the Databricks REST API, it’s important to account for rate limits, handle errors gracefully, and address common challenges to ensure reliable performance and smooth operations. In this section, I will cover the strategies for optimizing API usage and troubleshooting effectively.
Rate limits
The Databricks API enforces rate limits to ensure fair usage and maintain service reliability.
- Official Guidelines for Request Frequency: This limit restricts the number of API calls a user can make in a minute. Exceeding this limit can result in HTTP 429 errors. The Requests Per Day (RPD) caps the total number of requests a user can make in 24 hours.
- Strategies for Handling 429 Errors and Back-Off Approaches: Implement a retry mechanism with exponentially increasing delays between retries. This helps prevent overwhelming the server and reduces the likelihood of hitting the rate limit again immediately. Also, monitor request patterns to consolidate frequent calls where possible to reduce the overall request count.
Common issues and solutions
The following are the common issues you may experience with Databricks APIs and how to solve them.
- Authentication Pitfalls: This error often occurs due to invalid or expired tokens. To solve the issue, always ensure tokens are valid and properly configured. Verify token validity and regenerate if necessary. Ensure service principals are correctly configured and granted appropriate roles in Azure Databricks.
- Data Format or JSON Errors: Always validate JSON data before sending it in API requests. Use try-catch blocks to handle parsing errors gracefully. Using tools like JSON validators to check data format before sending requests will also help solve the error.
- Debugging Job Failures: Use the Databricks API to retrieve job logs for debugging purposes. Implement robust logging and monitoring to quickly identify and address job failures.
Performance bottlenecks
If you encounter performance bottlenecks, you should consider the following solutions:
- Minimizing Overhead in Repeated Calls: Implement caching for frequently accessed data to reduce the number of API calls. Also, use batch operations to minimize overhead by combining multiple requests into a single call.
- Using Caching or Batch Operations: Store frequently accessed data in a cache layer to reduce repeated API calls. You can also combine multiple requests into a single API call to reduce overhead and improve performance.
Advanced Workflows & Parallel Processing
The Databricks REST API is highly versatile, supporting both simple, single-threaded workflows and complex parallel processing. By leveraging advanced workflows, you can optimize efficiency and integrate seamlessly with external systems. Let us look at how to implement these features.
As a note, always configure your environment variables using:
-
DATABRICKS_HOST
: This variable stores the URL of your Databricks instance, which is necessary for constructing API requests. -
DATABRICKS_TOKEN
: This variable holds your access token, simplifying the process of including it in API calls.
Use Case 1: Single-threaded API ingestion
Calling a single API endpoint sequentially is effective for smaller tasks or simple automation. The following is a practical example using the requests library in Python.
import requests
import os
# Set up environment variables
DATABRICKS_HOST = os.getenv("DATABRICKS_HOST")
DATABRICKS_TOKEN = os.getenv("DATABRICKS_TOKEN")
# API request to list jobs
url = f"{DATABRICKS_HOST}/api/2.1/jobs/list"
headers = {"Authorization": f"Bearer {DATABRICKS_TOKEN}"}
response = requests.get(url, headers=headers)
if response.status_code == 200:
jobs = response.json().get("jobs", [])
for job in jobs:
print(f"Job ID: {job['job_id']}, Name: {job['settings']['name']}")
else:
print(f"Error: {response.status_code} - {response.text}")
Use Case 2: Parallel or distributed API calls
Parallel processing can significantly improve efficiency for larger-scale data ingestion or when dealing with multiple endpoints. The example below shows how to use Apache Spark to distribute API calls concurrently.
from pyspark.sql import SparkSession
from pyspark.sql.functions import udf
import requests
# Initialize Spark session
spark = SparkSession.builder.appName("ParallelAPI").getOrCreate()
# Sample data for parallel calls
data = [{"job_id": 123}, {"job_id": 456}, {"job_id": 789}]
df = spark.createDataFrame(data)
# Define API call function
def fetch_job_details(job_id):
url = f"{DATABRICKS_HOST}/api/2.1/jobs/get"
headers = {"Authorization": f"Bearer {DATABRICKS_TOKEN}"}
response = requests.get(url, headers=headers, params={"job_id": job_id})
if response.status_code == 200:
return response.json().get("settings", {}).get("name", "Unknown")
return f"Error: {response.status_code}"
# Register UDF
fetch_job_details_udf = udf(fetch_job_details)
# Apply UDF to DataFrame
results_df = df.withColumn("job_name", fetch_job_details_udf("job_id"))
results_df.show()
Consider the following tips for scalability and cost trade-offs.
- Scalability: Spark allows you to scale horizontally by adding more nodes to your cluster. This is particularly useful for handling large volumes of API requests.
- Cost Trade-Offs: While parallel processing reduces execution time, it increases resource usage and costs. Ensure that your cluster configuration balances performance needs with cost constraints.
- Cluster Configuration Tips: Choose nodes that balance CPU and memory based on your workload. You should also ensure to enough workers to handle the volume of requests without overwhelming the cluster. Optimize Spark settings, such as spark.executor.memory and spark.driver.memory, to prevent memory issues.
Orchestration and integration
Integrating Databricks with external orchestrators like Airflow or Dagster allows you to manage complex workflows and automate job execution across different systems. The example below shows how to use Airflow to trigger a Databricks job.
from airflow import DAG
from airflow.providers.databricks.operators.databricks import DatabricksRunNowOperator
from datetime import datetime
# Define default arguments for the DAG
default_args = {
"owner": "airflow", # Owner of the DAG
"depends_on_past": False, # Do not depend on past DAG runs
"retries": 1, # Number of retry attempts in case of failure
}
# Define the DAG
with DAG(
dag_id="databricks_job_trigger", # Unique DAG ID
default_args=default_args, # Apply default arguments
schedule_interval=None, # Manually triggered DAG (no schedule)
start_date=datetime(2023, 1, 1), # DAG start date
) as dag:
# Task to trigger a Databricks job
run_job = DatabricksRunNowOperator(
task_id="run_databricks_job", # Unique task ID
databricks_conn_id="databricks_default", # Connection ID for Databricks
job_id=12345, # Replace with the actual Databricks job ID
)
# Set task dependencies (if needed)
run_job
For CI/CD pipelines, tools like Git integration with Databricks can automate the deployment of code and assets across different environments. This ensures that changes are tested and validated before being deployed to production.
# Example of using Databricks REST API in a CI/CD pipeline
import requests
def deploy_to_production():
# Assuming you have a personal access token
token = "your_token_here"
headers = {"Authorization": f"Bearer {token}"}
# Update job or cluster configurations as needed
url = "https://your-databricks-instance.cloud.databricks.com/api/2.1/jobs/update"
payload = {"job_id": "123456789", "new_settings": {"key": "value"}}
response = requests.post(url, headers=headers, json=payload)
if response.status_code == 200:
print("Deployment successful.")
else:
print(f"Deployment failed. Status code: {response.status_code}")
Practical Example: Creating a Multi-Step Pipeline
Now that we have seen the different applications of Databricks API let me walk you through an example of an automated pipeline. This pipeline will ingest data from an external API, transform the data in Databricks, and then write the results to a Databricks table. This scenario is a typical end-to-end workflow for modern data engineering tasks. If you need to refresh your knowledge about the main features of Databricks, including data ingestion, I recommend checking out our tutorial on the 7 Must-know Concepts For Any Data Specialist.
Authenticating via a personal access token
To interact with the Databricks REST API, you need to authenticate using a Personal Access Token (PAT). Follow these steps to generate the PAT.
- Log into your Databricks workspace.
Databricks Workspace. Image by Author.
- Go to Settings > User Settings > Access Tokens.
Generating Access Token in Databricks. Image by Author.
- Generate a new token and save it securely.
If you need to refresh your knowledge about Databricks SQL, I recommend reading our Databricks SQL tutorial to learn how to set up SQL Warehouse from the Databricks web interface.
Set up the environment variables using either of the following:
Using the terminal
export DATABRICKS_HOST=https://<your-databricks-instance>
export DATABRICKS_TOKEN=<your-access-token>
Using Python's requests library for API calls
import os
import requests
DATABRICKS_HOST = os.getenv("DATABRICKS_HOST")
DATABRICKS_TOKEN = os.getenv("DATABRICKS_TOKEN")
headers = {"Authorization": f"Bearer {DATABRICKS_TOKEN}"}
Setting up a Databricks Job with multiple tasks
A Databricks job can consist of multiple tasks, each performing a step in the pipeline. For this example, consider the following:
- Task 1: Fetch data from an external API and write to DBFS.
- Task 2: Transform the data in a Databricks notebook.
- Task 3: Write the transformed data to a Delta table.
The following example uses the POST /api/2.1/jobs/create
endpoint to create the job.
{
"name": "Multi-Step Pipeline",
"tasks": [
{
"task_key": "fetch_data",
"existing_cluster_id": "your-cluster-id",
"notebook_task": {
"notebook_path": "/Users/example_user/fetch_data"
}
},
{
"task_key": "transform_data",
"depends_on": [{"task_key": "fetch_data"}],
"existing_cluster_id": "your-cluster-id",
"notebook_task": {
"notebook_path": "/Users/example_user/transform_data"
}
},
{
"task_key": "write_results",
"depends_on": [{"task_key": "transform_data"}],
"existing_cluster_id": "your-cluster-id",
"notebook_task": {
"notebook_path": "/Users/example_user/write_results"
}
}
]
}
Triggering the workflow and retrieving job results/outputs
Trigger the job using POST /api/2.1/jobs/run-now
curl -n -X POST \
https://$DATABRICKS_HOST/api/2.1/jobs/run-now?job_id=your_job_id
Monitor job status using GET /api/2.1/jobs/runs/get
with the run_id
to track progress.
response = requests.get(f"{DATABRICKS_HOST}/api/2.1/jobs/runs/get", headers=headers, params={"run_id": run_id})
print(response.json())
Access the output logs, or results from the tasks using GET /api/2.1/jobs/runs/get-output
.
Logging and error handling
For logging, configure job logs to be written to DBFS or a monitoring tool such as Datadog or Splunk. Also, include detailed logging in each task's notebook for better traceability.
Similarly, implement try-except blocks in notebooks to catch and log exceptions. Use the Databricks Jobs API to retrieve job logs for debugging failed runs. The example below shows error handling in a notebook.
try:
# Code to fetch data from API
response = requests.get(api_url)
# Process data
except Exception as e:
print(f"An error occurred: {e}")
# Log error and exit
You can also integrate with email or Slack notifications using the Databricks Alerts API or third-party tools.
Best Practices and Recommendations
Implementing best practices in Databricks can significantly enhance security, reduce costs, and improve overall system reliability. The following are the key strategies you should consider when using Databricks API and the critical areas.
Security and governance
Avoid storing plain tokens in code or notebooks. Instead, use Databricks Secret to store sensitive information like API keys or credentials securely using the Databricks Secrets API. This ensures that sensitive data is not exposed in plain text within code or notebooks. Always use environment variables to pass tokens or secrets to scripts, but ensure these variables are securely managed and not committed to version control.
Use Role-Based Access Control (RBAC) to define permissions at the workspace, cluster, and job levels and limit access to sensitive APIs to only necessary users or service accounts.
Cost management
For effective cost management, consider using Single-Node Clusters for continuous, low-load tasks. However, they can be cost-efficient if not fully utilized. Also, ephemeral job clusters are automatically created and terminated for jobs, ensuring resources are not idle.
Due to dynamic pricing for cloud resources, off-peak scheduling can significantly reduce costs. Use the Jobs API to define job schedules during cost-effective time windows.
Lastly, monitor cluster usage by regularly checking cluster utilization to ensure resources are not wasted. Configure clusters to auto-terminate after a specified idle time to prevent unnecessary costs.
Continuous testing and monitoring
For automated tests and job definitions, use unit tests to implement unit tests for job logic to catch errors early. Monitor job runs and log performance metrics to leverage the Jobs API to track job runs programmatically and analyze historical run data:
Conclusion and Next Steps
Using the Databricks REST API allows teams to automate workflows, optimize costs, and seamlessly integrate with external systems, transforming how data pipelines and analytics are managed. I encourage you to start by testing a basic API call, such as creating a job, to build confidence with the platform. You can also explore other advanced topics like Unity Catalog for fine-grained governance or dive into real-time streaming use cases.
Also, I recommend that you refer to the Official Databricks REST API Docs to help answer outstanding questions, and that you explore GitHub repositories on the Databricks CLI and Databricks SDK for Python to learn more about streamlined development.
If you want to explore Databricks' foundational concepts, I highly recommend taking our Introduction to Databricks course. This course teaches you about Databricks as a data warehousing solution for Business Intelligence. I also recommend checking out our Databricks Certifications In 2024 blog post to learn how to obtain Databricks certifications, explore career benefits, and choose the right certification for your career goals.
Start Learning Databricks
Databricks API FAQs
What is the Databricks REST API?
The Databricks API allows programmatic access to manage Databricks resources like jobs, clusters, secrets, and the workspace.
How do I authenticate with the Databricks API?
Use Personal Access Tokens (PATs) for secure authentication. Avoid embedding tokens in code; store them in Databricks Secrets or environment variables.
What are the differences between API versions (v2.0 vs. v2.1)?
Version 2.1 introduces enhanced functionality, including improved job management features. It's recommended to migrate to v2.1.
Does the API support multi-step workflows?
Yes, the Jobs API supports workflows with dependent tasks and multi-task jobs.
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