How To: Azure Databricks Cost Estimation Guide
Azure Databricks is a powerful cloud-based analytics platform that integrates seamlessly with various Azure data storage and AI services. However, as with any cloud service, it’s essential to understand the cost implications before deploying a solution. This guide will walk you through the steps to estimate the costs associated with Azure Databricks.
Note: This walks you through an example using the Microsoft Azure Pricing Calculator. This can be used to calculate your estimated hourly or monthly costs for using various services from within Azure. Reference Options Azure Databricks Pricing
ALSO !!!!!!! Prices are estimates only and are not intended as actual price quotes. Contact an Azure sales specialist for more information on pricing or to request a price quote.
Starting with the Azure Pricing Calculator
Going to the Microsoft Azure Pricing Calculator, you can select various products or view example scenario architectures as a Guide. For our example, enter Databricks in the search box, and a selector button with Azure Databricks is displayed.
Selecting Databricks will bring up the pricing worksheet, as pictured below.
Let’s divide up each section.
Keep in Mind when Selecting Your Region
When selecting the Region drop-down, you will see the pictured regions. You want to keep in mind a few key points when selecting a region.
You may have data residency requirements. Can your data reside outside your country?
You may want to use other services that may not be offered in the region you want. Ensure the Azure services you need are available in the data center region you’re considering.
Beware of Bandwidth costs. Bandwidth refers to data moving in and out of Azure data centers, as well as data moving between Azure data centers. See the link for details.
1. Understand the Pricing Model
Azure Databricks offers two main pricing tiers:
- Standard, which is suitable for smaller workloads and offers a set of core functionalities.
- Premium, which provides advanced security, RBAC, and other enterprise-grade features. This is the higher tier, and it provides a range of extra features, such as advanced machine learning algorithms.
When choosing between these two tiers, it is important to understand the pricing model used by Azure Databricks. These are:
User-based Pricing: This model charges based on the number of users who need access to your Databricks instance. This is an ideal pricing model for teams requiring access to a shared data store, without additional compute resources.
Job-based Pricing: This model charges based on the number of jobs that are run in Databricks. This is an ideal pricing model for teams who regularly need to process large amounts of data or those who run multiple analytical queries and simulations on their data.
2. Calculate Your Estimated Cost
Once you have chosen the right pricing model for your needs, the next step is to calculate your estimated cost. To do this, you will need to consider a few key factors:
Number of users: The number of users accessing your Databricks instance will determine the price per user. This can range from just a few dollars per user per month for the Standard tier, to several hundred dollars per user per month for the Premium tier.
Number of Jobs: The number of jobs you will run in Databricks will also affect your estimated cost. For example, if you have a lot of data processing jobs, then the cost may be higher than if you only had a few jobs.
Consider Your Usage: It is also important to consider the amount of usage that you will need from your Databricks instance. For example, if you are running a large data processing job, you may need more than one user and more than one job to complete it. This can increase the overall cost of your Databricks instance.
Look for Discounts: Many cloud providers offer discounts for their services, so make sure to look out for any special offers that may be available. Some providers also offer volume discounts, so it may be worth exploring this option if you are planning on running a lot of jobs. The Costing Model also has Pay as your go and reserved instances, which can save you money depending on your workload.
Once you have calculated your estimated cost, you can use this information to compare different Databricks pricing options and decide which one is the most cost-effective for your needs. You can also use this information to plan ahead and budget more accurately for your Databricks deployment.
3. Interactive vs. Automated Workspaces in Azure Databricks Costing
The cost of Azure Databricks is primarily determined by the number of Databricks Units (DBUs) consumed. DBUs are a virtual currency used to pay for processing time.
In Azure Databricks, understanding the distinction between Interactive and Automated Workspaces is crucial for functionality and cost optimization.
Interactive Workspaces are primarily designed for collaborative analytics, serving as a shared environment where data scientists, analysts, and other stakeholders can work together in real time. They can explore data, build models, and share insights, all within a unified workspace. This interactive nature often requires more immediate and flexible compute resources, leading to variable costs based on the intensity and duration of the sessions.
On the other hand, Automated Workspaces are tailored for running jobs without the need for manual intervention. These workspaces are ideal for scheduled tasks, ETL (Extract, Transform, Load) processes, and other automated workflows. The costs can be more predictable since these jobs are typically predefined and run at set intervals or triggers.
However, the scale and complexity of the jobs can influence the overall expense. In essence, while Interactive Workspaces are about fostering collaboration and real-time data exploration, Automated Workspaces focus on efficiency and automation, each with its unique cost implications.
4. Estimate Cluster Costs
The primary cost driver for Databricks is the compute clusters:
- Standard Clusters: These are general-purpose clusters suitable for most workloads.
- High Concurrency Clusters: Optimized for sharing resources among multiple users.
- GPU Clusters: Designed for machine learning and other GPU-intensive tasks.
Remember, you’re billed for the number of virtual machines in your cluster and the time they run.
5. Factor in Data Storage and Transfer Costs
While Databricks processing is a significant cost, don’t forget about:
- Data Storage: Costs associated with storing data in Azure Blob Storage, Azure Data Lake, etc.
- Data Transfer: Costs related to transferring data in and out of Azure.
6. Use the Azure Pricing Calculator
Azure provides a Pricing Calculator that can help you get a detailed estimate:
- Navigate to the Azure Pricing Calculator.
- Add ‘Azure Databricks’ to your estimate.
- Adjust parameters based on your expected usage.
7. Monitor and Optimize Costs
After deploying your Databricks solution:
- Set Up Alerts: Use Azure Cost Management to set up alerts for unexpected cost spikes.
- Review Regularly: Periodically review your Databricks usage and adjust resources as necessary.
- Optimize Clusters: Terminate unused clusters and consider using auto-termination settings to shut down clusters after periods of inactivity.
Understanding and Utilizing DBU (Databricks Unit) in Costing
A DBU (Databricks Unit) is a standardized measure used by Azure Databricks to quantify the computational power and processing capability consumed during operations. Think of it as a virtual currency representing your Databricks workloads’ cost. The number of DBUs consumed varies based on the type, size, and duration of the tasks performed.
To determine costs, users should monitor the number of DBUs their tasks are consuming, which can be done through the Azure Databricks workspace.
The total cost is then calculated by multiplying the number of DBUs consumed by the price per DBU, which can differ based on the pricing tier (Standard or Premium) and region. For instance, if an analytics job consumes 10 DBUs and the cost per DBU is $0.20, the total cost for that job would be $2. By understanding and tracking DBU consumption, users can gain better insights into their Azure Databricks expenditure and optimize their workloads for cost efficiency.
Your Most Important Step – Monitoring and Validating Your Cost Estimates for Databricks !!!!!!
Monitoring and validating your Databricks cost estimates is an ongoing process. By staying proactive and leveraging Azure’s built-in tools, you can ensure that your estimates are accurate, helping you manage and optimize your Databricks investments effectively.
Ensuring that your Azure Databricks cost estimates align with actual expenditures is vital for budgeting and financial planning. Here’s a guide to help you monitor and validate your Databricks cost estimates:
1. Familiarize Yourself with Azure Cost Management
Azure Cost Management provides detailed insights into your cloud spending. It breaks down costs by resource, service, and other parameters, allowing you to pinpoint where your Databricks expenses are coming from.
2. Set Up Budgets and Alerts
Within Azure Cost Management, you can set up budgets for your Databricks workloads. By defining a budget, you can receive alerts when your spending approaches or exceeds the set limit, ensuring you’re always aware of any cost overruns.
3. Regularly Review DBU Consumption
As DBUs (Databricks Units) are central to Databricks costing, regularly monitor your DBU consumption. Check for any unexpected spikes or prolonged high usage, which can significantly impact costs.
4. Compare Estimated vs. Actual Costs
At the end of each billing cycle, compare your initial cost estimates with the actual charges on your Azure bill. Look for discrepancies and analyze the reasons behind any significant variances.
5. Monitor Data Transfer and Storage Costs
Beyond DBUs, data transfer and storage can contribute to Databricks costs. Ensure you’re tracking data ingress and egress and the costs associated with storing data in Azure Blob Storage or Azure Data Lake.
6. Validate Cluster Configuration
Periodically review the configuration of your Databricks clusters. Ensure that auto-termination settings are enabled to shut down inactive clusters and that you’re not over-provisioning resources.
7. Seek Feedback from Teams
Engage with the teams using Databricks to understand their usage patterns. They might provide insights into specific jobs or tasks that are consuming more resources than anticipated.
8. Adjust and Refine Estimates
Based on your monitoring and validation efforts, adjust your future cost estimates. Incorporate lessons learned from previous months to make your predictions more accurate.
Conclusion
Estimating Azure Databricks costs requires a clear understanding of your expected workload, data storage, and transfer needs. By following this guide and regularly monitoring your expenses, you can ensure that you’re getting the most value out of your Azure Databricks investment.
