How to Optimize Costs When Running Databricks on AWS

Databricks on AWS is a powerful platform for big data analytics, machine learning, and data engineering. However, as the usage of Databricks grows, the associated costs can quickly spiral without effective cost management strategies. From provisioning compute resources to managing storage, there are several ways to optimize your cloud spending while ensuring your workloads run efficiently.

This blog post explores practical strategies and best practices for reducing costs when using Databricks on AWS.

Understand Key Cost Components

Before exploring cost optimization strategies, it’s essential to understand the key cost components associated with running Databricks on AWS:

Databricks Units (DBUs): A DBU is a unit of computation, representing the processing capability required to run Databricks workloads. The cost of DBUs depends on the type of cluster (standard or high concurrency) and how long the cluster is running.

EC2 Instances (Compute): Compute costs for Databricks on AWS are influenced by the selection of EC2 instance types and the number of instances used.

S3 Storage: Databricks usually stores data in Amazon S3, with storage costs determined by the volume of data and the selected S3 storage classes.

Understanding these components will allow you to tailor your approach for cost optimization across different areas.

Choose the Right EC2 Instance Type

Selecting the right EC2 instance type is one of the most effective ways to manage costs. AWS offers a wide range of EC2 instance types, and choosing the right one for your workload is critical for both performance and cost efficiency.

Best Practices:

Use Appropriate Instance Types: If you’re running workloads that require significant compute power (e.g., large-scale data processing), consider using C5 or C6 instances, which are optimized for compute-heavy tasks. For workloads that require substantial memory (e.g., machine learning or large data transformations), use R5 or X1 instances.

Start with Smaller Instances: It’s often better to start with smaller instance types (e.g., t3.medium or m5.large) and scale up as needed based on performance requirements. Over-provisioning instances at the outset can lead to unnecessary costs.

Spot Instances: Spot instances are a great way to save on compute costs. pot instances can be as much as 90% cheaper than on-demand instances, but they may be interrupted by AWS. Databricks allows you to use Spot Instances for non-essential workloads, such as batch jobs or data preprocessing.

Optimize Storage Costs with S3 and Delta Lake

Large-scale data storage can become a significant cost driver when using Databricks on AWS. Fortunately, AWS provides several tools to optimize storage costs.

Strategies for Reducing Storage Costs:

Use S3 Lifecycle Policies: AWS S3 allows you to set up lifecycle policies that automatically transition data to cheaper storage tiers, such as S3 Infrequent Access (IA) or S3 Glacier, after a certain period. This reduces the cost of storing historical or less-frequently accessed data.

Optimize with Delta Lake: Delta Lake by Databricks is an open-source storage layer that enhances Apache Spark and S3 with features like ACID transactions, time travel, and schema enforcement. Delta Lake’s optimized data storage format minimizes the number of files and reduces the number of read and write operations, which can reduce both storage and processing costs.

Compression Formats: Use columnar storage formats like Parquet or ORC, which are optimized for analytical queries and offer built-in compression. By reducing the data size, these formats help to decrease storage expenses.

Leverage AWS Cost Management Tools

AWS provides powerful cost management and monitoring tools that can help you optimize spending when running Databricks on AWS.

Key Tools for Cost Optimization:

AWS Cost Explorer: You can view cost trends by service, region, or tag, which can help identify areas where costs are higher than expected. You can also forecast future costs to help with budget planning.

AWS Budgets: Set custom cost and usage budgets for Databricks-related services, such as EC2 instances, DBUs, and S3 storage. AWS Budgets sends notifications when usage or costs exceed predefined thresholds, allowing you to take corrective action before expenses spiral out of control.

Optimize Spark Jobs and Pipelines

Efficient use of resources at the job level can make a huge difference in Databricks’ cost optimization. Poorly optimized Spark jobs can lead to excessive compute resource usage, slow processing, and high DBU consumption.

Tips for Optimizing Spark Jobs:

Tune Spark Configurations: Optimize your Spark job configurations, such as memory settings, shuffle partitions, and the number of executors, to ensure efficient resource usage. Improper configurations can lead to unnecessary overhead and wasted compute capacity.

Use Spark SQL: For some workloads, switching from RDD-based operations to Spark SQL can be more efficient. SQL queries are optimized in Spark, reducing execution time and resource consumption compared to traditional RDD transformations.

Limit Data Shuffling: Excessive data shuffling between Spark stages can increase compute and storage costs due to the high I/O requirements. Reduce unnecessary shuffling by tuning partitioning strategies and ensuring data is co-located when possible.

Monitor and Audit Your Usage Regularly

Cost optimization is a continuous process that demands regular monitoring and review. Regularly track your Databricks usage to ensure that you’re not incurring unnecessary costs.

Best Practices for Monitoring:

Monitor Cluster Activity: Use Databricks’ built-in monitoring tools to track cluster activity and utilization. If you notice that a cluster is consistently underutilized, consider reducing its size or terminating it to save costs.

Track DBU Usage: Keep track of the number of DBUs consumed by your workloads, and identify which jobs or teams are consuming the most. If certain jobs are particularly expensive, consider optimizing them or splitting them into smaller, more efficient tasks.

Conclusion

Optimizing costs when running Databricks on AWS involves making smart decisions at every stage of the data pipeline and resource provisioning. By understanding the key cost components, choosing the right instance types, using autoscaling, optimizing storage, and leveraging AWS cost management tools, you can ensure that your Databricks workloads run efficiently without breaking the bank.

Keep in mind that cost optimization is a continuous process that requires consistent monitoring and adjustments. By applying these strategies, you can maximize the value you get from Databricks while minimizing unnecessary cloud expenses.

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.

CloudThat is the first Indian Company to win the prestigious Microsoft Partner 2024 Award and is recognized as a top-tier partner with AWS and Microsoft, including the prestigious ‘Think Big’ partner award from AWS and the Microsoft Superstars FY 2023 award in Asia & India. Having trained 650k+ professionals in 500+ cloud certifications and completed 300+ consulting projects globally, CloudThat is an official AWS Advanced Consulting Partner, Microsoft Gold Partner, AWS Training Partner, AWS Migration Partner, AWS Data and Analytics Partner, AWS DevOps Competency Partner, AWS GenAI Competency Partner, Amazon QuickSight Service Delivery Partner, Amazon EKS Service Delivery Partner,  AWS Microsoft Workload Partners, Amazon EC2 Service Delivery Partner, Amazon ECS Service Delivery Partner, AWS Glue Service Delivery Partner, Amazon Redshift Service Delivery Partner, AWS Control Tower Service Delivery Partner, AWS WAF Service Delivery Partner and many more.

To get started, go through our Consultancy page and Managed Services Package, CloudThat’s offerings.