Automating Databricks Jobs with AWS Secrets Manager and AWS Lambda Functions

Overview

In the era of big data, orchestrating and automating data workflows is essential for efficient data processing and analysis. Databricks, a unified analytics platform, is widely used for big data processing. At the same time, AWS Secrets Manager and AWS Lambda Functions provide secure and scalable solutions for managing credentials and automating tasks. In this guide, we will delve into the intricacies of data orchestration, focusing on automating Databricks jobs using AWS Secrets Manager and Lambda Functions.

Databricks is built on top of AWS and is natively compatible with all the AWS offerings, and all our clients are avid AWS users. In this blog, we will explore leveraging AWS Lambda and Databricks to tackle two use cases: an event-based ETL automation and serving Machine Learning model results trained with Apache Spark.

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Introduction to the services used

Databricks Job

Databricks jobs allow you to schedule and automate the execution of notebooks, libraries, and scripts. Understanding the basics of Databricks job execution is crucial for effective automation.

AWS Secrets Manager

AWS Secrets Manager is a service that helps protect access to your applications, services, and IT resources without upfront investment and ongoing maintenance costs. Learn how Secrets Manager can securely store and manage sensitive information.

AWS Lambda Functions

AWS Lambda lets you run your code without provisioning or managing servers. Explore how Lambda Functions can be leveraged to trigger Databricks jobs seamlessly.

Prerequisites

Setting up a Databricks Account

Walk through creating a Databricks account, setting up a workspace, and understanding the key features needed for automation.

Creating an AWS Account

If you don’t have an AWS account, this section guides you through creating one and setting up the necessary services.

Configuring Access and Permissions

Ensure that the necessary access and permissions are configured in Databricks and AWS to facilitate seamless communication between the platforms.

Integrating Databricks with AWS Secrets Manager

Creating a Secret in AWS Secrets Manager

Step-by-step instructions on creating a secret in AWS Secrets Manager to securely store sensitive information like API keys, database credentials, etc.

Configuring Databricks Workspace to Access Secrets

Learn how to configure your Databricks workspace to access secrets stored in AWS Secrets Manager, ensuring a secure and seamless integration.

Creating AWS Lambda Functions

Setting Up an AWS Lambda Function

A detailed walkthrough on setting up the AWS Lambda function, choosing the appropriate runtime, and configuring triggers.

Writing Python Scripts for Databricks Automation

Develop Python scripts that will serve as the bridge between Lambda and Databricks, allowing for job automation.

api_version = '/api/2.1'
api_command = '/jobs/run-now'  # Databricks job run API
url = f"https://{instance_id}{api_version}{api_command}"
token = secret["Name_of _secret"]

api_version = '/api/2.1'

api_command = '/jobs/run-now' # Databricks job run API

url = f"https://{instance_id}{api_version}{api_command}"

token = secret["Name_of _secret"]

Configuring Lambda Environment Variables

Understand how to use environment variables in Lambda for storing configuration details and secrets.

step4

Establishing Trust Between Databricks and AWS Lambda

Setting Up AWS IAM Roles and Policies

Create IAM roles and policies to establish trust between Databricks and Lambda, ensuring secure communication.

Granting Permissions for AWS Lambda to Access Databricks

Configure permissions to grant AWS Lambda the necessary access to trigger and manage Databricks jobs.

Writing an Automation Script

Understanding the Databricks API

Explore the Databricks API and understand how it can be utilized for job automation.

# Decrypts secret using the associated KMS key.
    secret = get_secret_value_response['SecretString']
    secret=eval(secret)
    instance_id = secret["dtb-prod-instance-id"]
    api_version = '/api/2.1'
    api_command = '/jobs/run-now'
    url = f"https://{instance_id}{api_version}{api_command}"
    token = secret["dtb-prod-workspace-token"]
    params = {
        "job_id": job_id
    }
    res = requests.post(
     url = url,
     headers = {'Authorization': 'Bearer %s' % token},
      json = params
    )
    print(res)
    print(json.dumps(json.loads(res.text), indent = 2))
    try:
        response = s3.get_object(Bucket=bucket, Key=key)
        print("CONTENT TYPE: " + response['ContentType'])
        print("object fetched")
        return response['ContentType']
    except Exception as e:
        print(e)
        print('Error getting object {} from bucket {}. Make sure they exist and your bucket is in the same region as this function.'.format(key, bucket))
        raise e

# Decrypts secret using the associated KMS key.

secret = get_secret_value_response['SecretString']

secret=eval(secret)

instance_id = secret["dtb-prod-instance-id"]

api_version = '/api/2.1'

api_command = '/jobs/run-now'

url = f"https://{instance_id}{api_version}{api_command}"

token = secret["dtb-prod-workspace-token"]

params = {

"job_id": job_id

}

res = requests.post(

url = url,

headers = {'Authorization': 'Bearer %s' % token},

json = params

)

print(res)

print(json.dumps(json.loads(res.text), indent = 2))

try:

response = s3.get_object(Bucket=bucket, Key=key)

print("CONTENT TYPE: " + response['ContentType'])

print("object fetched")

return response['ContentType']

except Exception as e:

print(e)

print('Error getting object {} from bucket {}. Make sure they exist and your bucket is in the same region as this function.'.format(key, bucket))

raise e

Building a Script for Job Automation

Step-by-step guide on building an automation script that utilizes the Databricks API to trigger jobs based on specific events or schedules.

step6

Handling Error Scenarios

Implement error-handling mechanisms to ensure the reliability of your automation script.

Testing and Troubleshooting

Running Test Jobs in Databricks

Learn how to test jobs in Databricks to validate the effectiveness of your automation.

step7

Monitoring AWS Lambda Execution Logs

Explore techniques for monitoring Lambda execution logs to identify and troubleshoot issues.

Common Issues and Troubleshooting Tips

Address common challenges and gain insights into troubleshooting tips for a smoother automation experience.

Deployment

Deploying Automation in a Production Environment

Guidelines for deploying your automated solution in a production environment, considering factors like scalability and reliability.

Ensuring Reliability and Error Handling

Implement measures to ensure the reliability of your automation solution and handle errors gracefully.

Real-world Use Cases and Examples

Case Study 1: Batch Processing

Explore a real-world use case where Databricks jobs are automated for batch processing, improving efficiency and reducing manual intervention.

Case Study 2: Real-time Data Pipelines

Learn how automation can be applied to create real-time data pipelines using Databricks and AWS services.

Case Study 3: Machine Learning Model Deployment

Discover how automated Databricks jobs can be utilized in deploying and managing machine learning models.

Conclusion

Automating Databricks jobs with AWS Secrets Manager and AWS Lambda Functions presents a transformative approach to data engineering, ushering in a new era of efficiency, security, and scalability.

Throughout this comprehensive guide, we navigated the intricacies of Databricks job automation, exploring the synergy between AWS Secrets Manager and AWS Lambda Functions.

Drop a query if you have any questions regarding Heterogeneous Databricks jobs and we will get back to you quickly.

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About CloudThat

CloudThat is a leading provider of Cloud Training and Consulting services with a global presence in India, the USA, Asia, Europe, and Africa. Specializing in AWS, Microsoft Azure, GCP, VMware, Databricks, and more, the company serves mid-market and enterprise clients, offering comprehensive expertise in Cloud Migration, Data Platforms, DevOps, IoT, AI/ML, and more.

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FAQs

1. Why is data orchestration crucial for organizations leveraging big data analytics?

ANS: – Data orchestration is vital because it ensures the seamless coordination and automation of data-related tasks, optimizing workflows for efficiency, scalability, and security. In big data analytics, orchestrating data workflows becomes essential to effectively manage diverse data sources, formats, and complex analytics requirements.

2. How does Databricks enhance the analytics process, and why integrate it with AWS services?

ANS: – Databricks is a unified analytics platform that combines data engineering, machine learning, and collaborative data science in a single cloud-based platform. Integrating Databricks with AWS services, such as Secrets Manager and Lambda Functions, enhances its capabilities by providing secure credential management and serverless automation.

3. What is the role of AWS Secrets Manager in the orchestration process?

ANS: – AWS Secrets Manager is a fully managed service that helps securely store and manage sensitive information like API keys and database credentials. In orchestrating Databricks jobs, Secrets Manager is crucial in securely handling credentials required for accessing data sources and other services.

WRITTEN BY Sunil H G

Sunil H G is a highly skilled and motivated Research Associate at CloudThat. He is an expert in working with popular data analysis and visualization libraries such as Pandas, Numpy, Matplotlib, and Seaborn. He has a strong background in data science and can effectively communicate complex data insights to both technical and non-technical audiences. Sunil's dedication to continuous learning, problem-solving skills, and passion for data-driven solutions make him a valuable asset to any team.