Setting Up a Databricks Workspace on AWS
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This blog was written by Padma Karumuri, the Director of Softrams' Data Practice, and Abrham Smith, a Senior Application Database Administrator at Softrams.
Setting up a Databricks workspace on AWS involves more than just creating resources; it’s crucial to follow best practices to ensure optimal performance, security, and manageability. Let’s walk through some recommendations for how to set up your Databricks Workspace.
AWS Services Used
- AWS Management Console
- Amazon S3
- Amazon Redshift
- Amazon Relational Database Service (S3)
- Amazon CloudWatch
- AWS Cost Explorer
Step 1: Initial Planning and Preparation
Start by assessing your requirements. Define the use cases, data sources, and expected workload. Determine the required cluster sizes, instance types, and storage needs based on your workload. You should also estimate costs for Databricks, AWS resources (e.g., EC2, S3), and data transfer. Set up AWS budgets and cost alerts to monitor expenses.
When configuring your AWS account, ensure your account is set up with appropriate billing and cost management configurations. Use AWS Organizations to manage multiple accounts if necessary. This helps in isolating environments (e.g., development, staging, production) and applying centralized policies.
Step 2: Databricks Workspace Deployment
To start setting up your Databricks workspace, first subscribe to Databricks on the AWS Marketplace.
Use the AWS Management Console to deploy Databricks in your preferred AWS region. Choose the region closest to your data sources to minimize latency.
Next, configure the VPC settings:
- Deploy Databricks in a Virtual Private Cloud (VPC) for enhanced security. Use private subnets to keep your clusters isolated from the public internet.
- If one does not already exist, create a VPC with public and private subnets.
- Place Databricks clusters in private subnets. Ensure public subnets are used only for internet-facing resources if necessary.
- Configure a NAT Gateway in a public subnet to allow Databricks clusters to access the internet for updates and external services.
Step 3: Networking and Security
Once your VPC settings have been configured, you can begin configuring security groups. A best practice for defining security groups is to tightly control inbound and outbound traffic. Allow only necessary ports (e.g., 443 for HTTPS) and restrict access to trusted IP addresses or CIDR blocks.
You should also set up IAM roles by attaching these roles to Databricks clusters to enable secure access to AWS resources. A best practice here is to create and use IAM roles with the minimum permissions necessary for Databricks to access AWS services like S3, Redshift, or Relational Database Services (RDS).
Last, enable encryption. Follow encryption best practices by using encryption for data at rest and in transit. Configure server-side encryption for S3 buckets and ensure that data is encrypted when stored and transmitted.
Step 4: Databricks Workspace Configuration
Now, let’s get into configuring the Databricks workspace.
- Admin and User Management: Set up user roles and permissions based on their responsibilities. Use groups to manage permissions efficiently.
- Networking Configuration: Configure Databricks to use your VPC for secure network communication. Set up VPC peering if necessary to connect Databricks with other VPCs or AWS services.
- Cluster Configuration: Choose appropriate instance types and sizes based on workload requirements. Use auto-scaling to dynamically adjust the number of nodes based on the workload.
- Create and Configure Workspaces: Organize your workspace with folders for different projects or teams. This helps in managing access and organizing notebooks and jobs effectively.
Step 5: Data Storage and Management
You should use Amazon S3 as your primary data storage solution. Configure S3 bucket policies to control access securely. Then enable Audit Logging for tracking operations and access.
Use Databricks to mount S3 buckets for easy data access. Ensure proper access controls and use Databricks secret management to securely handle credentials. Use Delta Lake for ACID transactions, scalable metadata handling, and improved performance in data pipelines.
Step 6: Job and Workflow Management
Use Databricks Jobs to automate data processing tasks. Setup job clusters to avoid running jobs on shared clusters, ensuring resource isolation and better performance.
Implement monitoring and alerting for job status. Use Databricks’ built-in monitoring tools and integrate with AWS CloudWatch for comprehensive insights.
Step 7: Monitoring and Optimization
Monitor cluster performance, resource utilization, and job execution. Use Databricks’ built-in tools and integrate with AWS CloudWatch for enhanced monitoring.
Regularly review cluster configurations, optimize Sparkjobs, and adjust instance types or sizes based on performance metrics.
You should also regularly review and optimize your Databricks and AWS costs. Use cost allocation tags and set up AWS Cost Explorer to track expenses.
Step 8: Compliance and Governance
Implement data governance and define data governance policies for data access, quality, and lineage. Ensure compliance with regulatory requirements.
To ensure adherence to security and compliance policies, perform regular audits of access controls, security settings, and usage logs.
Step 9: EC2 Cluster Management
To optimize costs, it’s important to consider using spot instances which allow you to leverage discounts based on leftover capacity.
It could also be beneficial to enable auto-scaling, with many workloads being dynamic; this would ensure your instances can scale up or down based on usage.
Step 10: Budget Management
Last, let’s talk about budgeting. Leveraging alerts in AWS can help you to control costs and monitor spending on Databricks clusters. You can also use budget controls to limit usage.
Resource tagging is also very useful to help track and manage resources within AWS and to keep track of everything related to your Databricks cluster. This can also help identify which teams or projects are leveraging the infrastructure more.
By following these best practices, you’ll establish a robust, secure, and efficient Databricks environment on AWS that effectively meets your data processing and analytics needs.
