Deploying Databricks on AWS: Essential Tips and Recommendations for Cloud Engineers (Part 3)

Deploying Databricks on AWS: Essential Tips and Recommendations for Cloud Engineers (Part 3)

Introduction:

In this final part of our Best Practices and Guidance for Cloud Engineers to Deploy Databricks on AWS series, we will focus on automation. We will discuss the three types of API endpoints used in a Databricks on AWS deployment and provide examples using common infrastructure as code (IaC) tools like CloudFormation and Terraform. We will also share some general best practices for automation. If you are new to the series, we recommend reading the previous parts to understand the architecture and benefits of Databricks on AWS. Automation is crucial for cloud engineers, and APIs are the backbone of cloud automation. There are different types of API endpoints for Databricks on AWS, including AWS, Databricks Account, and Databricks Workspace endpoints. These endpoints allow for the creation, management, and deployment of various resources and configurations. In a standard deployment process, you will interact with each endpoint, starting with creating the backbone infrastructure on AWS, then registering AWS resources with the Databricks account API, and finally using the workspace endpoint for workspace activities. There are several common IaC tools available, such as Terraform and AWS CloudFormation, that can be used for Databricks on AWS deployments. Terraform is a popular choice, offering a flexible way to deploy and manage infrastructure across different cloud environments. The Databricks provider for Terraform allows seamless integration with your existing infrastructure. AWS CloudFormation is another option, especially suitable for teams with limited DevOps experience, as it provides a GUI-based approach to quickly spin up Databricks workspaces. When using IaC tools, it’s essential to follow best practices. One of the key practices is iteration, as deployment and code refinement will take time. It’s essential to start with a proof of concept and iterate towards a production-ready setup.

Full Article: Deploying Databricks on AWS: Essential Tips and Recommendations for Cloud Engineers (Part 3)

Automation: The Backbone of Cloud Engineering

Automation plays a crucial role in cloud engineering, enabling efficient deployment and management of various cloud services. In the context of deploying Databricks on AWS, there are three types of API endpoints that are used: AWS, Databricks Account, and Databricks Workspace.

Types of API Endpoints for Databricks on AWS Deployments

1. AWS Endpoint: The AWS endpoint allows the creation of essential infrastructure resources such as S3 buckets, IAM roles, VPCs, subnets, and security groups. These resources serve as the backbone infrastructure for the Databricks workspace.

2. Databricks Account Endpoint: At the highest level of the Databricks organization hierarchy is the Databricks account. Through the account endpoint, various configurations related to cloud resources, workspace settings, identities, and logs can be created.

3. Databricks Workspace Endpoint: The workspace endpoint is used for all activities related to the created Databricks workspace. This includes the creation, maintenance, and deletion of clusters, secrets, repos, notebooks, and jobs.

Deployment Process

In a standard deployment process, the three endpoints mentioned above are interacted with in a specific order. First, the AWS endpoint is used to create the backbone infrastructure for the Databricks workspace. Then, the Databricks account API is utilized to register the AWS resources created as configurations. Finally, the workspace endpoint is used to perform workspace activities such as creating clusters and assigning permissions.

Commonly Used Infrastructure as Code (IaC) Tools

While various IaC tools can be used for deploying Databricks on AWS, two commonly used tools are HashiCorp Terraform and AWS CloudFormation.

1. HashiCorp Terraform: Terraform is a popular IaC tool that offers a flexible way to deploy, manage, and destroy infrastructure across cloud environments. The Databricks provider for Terraform allows seamless integration with existing Terraform infrastructure. Databricks provides example modules for deploying multiple workspaces with different configurations, such as VPC, PrivateLink, and IP Access Lists.

2. AWS CloudFormation: AWS CloudFormation enables the management of AWS resources using a recipe-like approach. Databricks has collaborated with AWS to publish an open-source Quick Start leveraging CloudFormation. This Quick Start provides a baseline for creating Databricks workspaces using native functions and API calls to Databricks endpoints.

Best Practices

Some best practices for deploying Databricks on AWS using Terraform or CloudFormation include:

1. Follow standard Terraform code structure practices, such as separating code into different environments and utilizing off-the-shelf modules.

2. Utilize the Databricks Terraform Experimental Exporter tool to extract specific components of a Databricks workspace into Terraform code. This allows for easy replication of workspaces in different regions or for staging and testing purposes.

3. Consider your team’s familiarity and experience with different tools when choosing between Terraform and CloudFormation. Terraform is suitable for teams already using it or managing a multi-cloud setup, while CloudFormation provides a GUI-based approach for teams with little DevOps experience.

In conclusion, automation is a vital aspect of cloud engineering, and deploying Databricks on AWS can be efficiently achieved using API endpoints, IaC tools like Terraform or CloudFormation, and following best practices. These practices ensure streamlined deployment and management of Databricks workspaces on AWS.

Summary: Deploying Databricks on AWS: Essential Tips and Recommendations for Cloud Engineers (Part 3)

In the final part of our Best Practices and Guidance for Cloud Engineers to Deploy Databricks on AWS series, we focus on automation. We discuss the three types of API endpoints used in a Databricks on AWS deployment: AWS, Databricks – Account, and Databricks Workspace. We then walk through the deployment process, starting with creating the backbone infrastructure on AWS and then moving on to creating the workspace and performing workspace activities. We also discuss two commonly used infrastructure as code (IaC) tools: HashiCorp Terraform and AWS CloudFormation. Finally, we highlight best practices for using IaC, emphasizing the importance of iteration and refinement in the deployment process.

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