Part 1 – The Tools
Big Data ETL. How to choose between Databricks, Amazon EMR, or running Spark on Kubernetes?
Each option leverages Apache Spark for distributed data processing, but they differ in cost, performance characteristics, security features, and developer experience. This blog intends to provide a cloud-agnostic comparison to help data engineers and decision-makers like you pick the best solution for your needs.
I’ll evaluate each option based on: Cost, Performance, and Productivity. From batch pipelines to streaming data, each option has its strengths and weaknesses, which can alter why you may choose one over the other.
Cost Considerations
Choosing an ETL platform starts with understanding the respective associated costs with development and production. The cost models of Databricks, EMR, and Spark-on-Kubernetes differ significantly.
Databricks
Databricks is a fully managed platform with a premium pricing model. Databricks charges for both compute resources in the cloud plus an additional usage-based fee measured in Databricks Units (DBUs). This pay-as-you-go model means you’re billed per second of usage, correlating cost with workload performance. The upside is you only pay for what you use and can optimize costs by reducing job runtimes. However, the DBU fees can add up for large-scale or long-running jobs, making Databricks potentially pricier for heavy workloads.
Example: A company running many concurrent ETL pipelines may see significant platform fees on top of machine costs. On the other hand, Databricks does support using spot instances on cloud VMs (e.g. AWS Spot) to reduce infrastructure costs, but you’re still paying the Databricks premium for its managed service. Additionally, spot instances may not be ideal for jobs requiring absolute stability, and consistent execution times, as spot instances can be taken away at any point of execution.
Amazon EMR
Amazon EMR is a pay-for-what-you-use model (similar to Databricks) with fine-grained control over infrastructure.
EMR charges primarily for the underlying AWS resources (EC2 instances, storage) and a small per-instance EMR service fee. Notably, you can leverage AWS pricing options, including reserved instances and Spot Instances, to dramatically cut costs. This makes EMR very cost-efficient if you manage it well.
For example, using transient clusters or auto-terminate settings to avoid idle time, and using spot instances for batch jobs that can handle interruptions can help users save on machine & platform costs.
There are no additional software license fees beyond AWS infrastructure costs and EMR’s minimal overhead, which keeps overall costs low if your team is willing to handle more of the management overhead.
Example: An EMR cluster with a few nodes might cost only dollars per hour on EC2, and if you utilize spot instances at 90% off, a short ETL job might cost pennies. The trade-off, however, is the operational overhead of managing these EMR clusters. Overall, EMR can be the most budget-friendly for steady or predictable workloads—especially batch workloads that only require the clusters at specific times, as clusters can be spun up and torn down on-demand.
Spark on Kubernetes
For the brave, Spark on Kubernetes is a do-it-yourself approach that can yield the lowest infrastructure costs at scale, assuming constant amounts of large data. Running Spark on your own Kubernetes cluster means you pay only for the compute resources, with no vendor mark-up, because you are managing the cluster and service yourself. If implemented correctly, Spark on K8s is often the cheapest option for large-scale workloads in the long run. However, achieving these savings requires substantial engineering effort, especially at the start. You’ll need to spin up and maintain the K8s cluster and Spark operator, handle scaling, and ensure efficient scheduling. There may also be hidden costs like Kubernetes control plane fees (in managed K8s services) or engineering labor.
Example: A startup on a tight budget might run Spark on an existing Kubernetes cluster to avoid new service fees, containerizing ETL jobs and using K8s auto-scalers to add nodes only when needed.
In summary, Databricks offers simplicity at a premium cost, EMR offers pay-as-you-go with many cost optimization levers, and Spark-on-Kubernetes offers maximum cost control (and potential savings) at the expense of more operational overhead. Organizations should model their total cost of ownership. If you have spiky workloads and limited ops staff, paying extra for a managed platform like Databricks might be worth it. If you have steady workloads and some cloud expertise, EMR might be best. And if you have the expertise and an existing cluster, Spark-on-Kubernetes may be right for your organization.
Performance Factors (Latency, Autoscaling, Shuffle)
Performance for ETL engines can be broken down into cluster startup latency, runtime efficiency (e.g. how well they handle heavy shuffle operations), and autoscaling behavior for dynamic workloads. Here’s how the three compare:
Cluster & Job Startup Latency:
- Databricks is known for relatively fast startup times for Spark jobs, especially using Job Clusters or pool features.
Databricks manages a pool of ready-to-go instances, reducing cold start time.
In many cases, a job cluster can start in under a couple of minutes, and recent serverless offerings can start tasks in seconds. - Amazon EMR historically has slower startup as you provision a brand-new cluster for each job.
A traditional EMR cluster might take several minutes (5–10 minutes) to spin up all EC2 nodes and initialize Yarn/HDFS.
AWS has introduced newer alternatives to mitigate this, including EMR on EKS and EMR Serverless, which avoid provisioning dedicated clusters each time. - Spark on Kubernetes can excel here: with the Kubernetes cluster already running, launching a new Spark driver pod is quick (seconds to tens of seconds).
Spark-on-K8s allows you to skip the lengthy cluster bootstrap that Yarn-based platforms often require.
Summary: For bursty, short jobs, a Kubernetes approach can eliminate the ~10-minute overhead that comes with creating transient clusters on Yarn. If time is less of a concern, EMR on EC2 is an adequate solution. In practice, if a low latency job start is critical (e.g., an hourly ETL where 5 minutes of overhead is significant), you’d lean toward Databricks with pools or a continuously running Kubernetes cluster to avoid repeated cold starts.
Autoscaling Behavior: All three solutions support scaling, but the mechanisms differ.
- Databricks provides workload-level autoscaling, finely tuned for Spark jobs.
It can add or remove executors on the fly during a job’s different stages.
This granular scaling (including vertical scaling in some cases) helps handle spiky, unpredictable workloads efficiently. - EMR scales at the cluster level.
You can add or remove EC2 instances based on metrics or a schedule.
EMR’s managed scaling monitors CPU/memory and adjusts cluster size, which is ideal for steady long jobs that may need more compute resources partway through execution. - Spark on Kubernetes can autoscale in two ways: Spark’s dynamic allocation feature requests new executor pods when needed, and Kubernetes’ cluster autoscaler adds new VM nodes if current ones are full.
Dynamic allocation now works reliably on K8s, achieving effects similar to Yarn.
One nuance: Kubernetes autoscaling is declarative, so it may not react instantly to rapid surges, but it generally catches up effectively.
Summary: Overall, Databricks tends to handle rapid autoscaling most transparently as it is optimized for Spark workloads, EMR offers stable scaling for large batch jobs, and Spark-on-K8s offers flexibility (you can configure custom pod scaling logic) but requires careful tuning.
Shuffle and Runtime Efficiency: Under the hood, all three run Apache Spark, so the core data processing performance is similar. In fact, recent benchmarks show “no performance difference between Spark on Kubernetes and Spark on Yarn” when configured properly. That said, each platform may introduce optimizations.
- Databricks uses a proprietary optimized Spark runtime (with enhancements like Photon engine for SQL, improved shuffle algorithms, and IO caching), which can significantly speed up certain workloads.
Databricks’ runtime can outperform open-source Spark in heavy ML or aggregation tasks by leveraging these optimizations.
Additionally, read and write operations are natively optimized, making performance bottlenecks from unoptimized code less frequent. - Amazon EMR also offers improvements over stock Spark.
AWS has reported that EMR’s tuned Spark is several times faster than vanilla Spark in some benchmarks.
EMR benefits from optimized connectors (e.g., EMRFS for S3 I/O) and the ability to adopt newer Spark versions or AWS-specific patches quickly. - Spark on Kubernetes uses open-source Spark, so performance depends on Spark version and configuration choices.
Spark 3.x on K8s has matured significantly, offering features like pusher-based shuffle and improved memory management.
A well-tuned Spark-on-K8s job can match Databricks or EMR performance on similar hardware, though it may require more manual tuning (e.g., memory config, shuffle storage).
One advantage of Kubernetes is the ability to share a cluster among multiple Spark jobs while maintaining isolation via namespaces, improving utilization compared to transient cluster models.
Bottom line: All three options can handle large-scale ETL performance needs. Databricks offers the easiest path to high performance with minimal tuning (thanks to its optimized engine and smart autoscaling) but you pay for it. EMR offers solid performance, especially for I/O-heavy workloads tightly integrated with AWS storage and is a good fit for long-running stable clusters. Spark on K8s can achieve equal performance to managed services, and even enable new efficiency by consolidating workloads, but it demands expertise to reach that potential.
Team Workflow & Productivity (Ease of Use, IDE Integration, CI/CD)
The developer experience can make or break an ETL platform choice. Consider how your data engineers and scientists will develop, debug, and deploy jobs on each platform:
- Databricks: Prioritizes ease of use and collaboration. The hallmark of Databricks is its notebook-based workspace, supporting multiple languages (Python, SQL, Scala, R) in a single shared environment.
Teams benefit from real-time co-editing, built-in visualizations, Git integration, and managed workflows for scheduling jobs, alerts, and CI/CD integration.
Databricks Connect enables local IDE development with remote execution. Overall, it delivers an all-in-one, highly collaborative environment ideal for notebook-driven teams or data scientists who prefer not to manage cluster internals. - Amazon EMR: Offers flexibility with two main interfaces: EMR Notebooks and EMR Studio.
EMR Notebooks are simple, managed Jupyter notebooks, whereas EMR Studio provides a more complete Spark IDE with debugging tools, Git integration, and IAM-based secure access.
Many EMR users prefer traditional IDEs (IntelliJ, PyCharm, VS Code) combined with spark-submit, orchestration tools like Airflow or Step Functions, and CI/CD pipelines.
This approach gives significant freedom but requires AWS and Spark expertise. EMR aligns well with teams already comfortable with AWS tooling and DevOps-driven workflows. - Spark on Kubernetes: Designed for teams adopting infrastructure-as-code and modern CI/CD pipelines.
There is no native UI or notebook environment unless the team deploys one (e.g., JupyterHub).
Developers write Spark applications in any IDE, containerize them with Docker, and deploy via Kubernetes jobs or the Spark Operator.
This provides consistency across environments and strong GitOps integration but demands Kubernetes and Docker knowledge.
Setup, debugging, and developer tooling require more effort, making it ideal for DevOps-centric teams that want maximum control and treat data pipelines as code.
To summarize the productivity angle:
- Databricks is the leader in out-of-the-box productivity and collaboration, ideal for data science teams or analytics-heavy organizations.
- EMR provides a middle ground — flexible, integrates with many tools, and with EMR Studio it is becoming more user-friendly, though still more hands-on than Databricks.
- Spark on K8s gives engineers ultimate control with full CI/CD integration, but requires significant upfront configuration and Kubernetes expertise.
The choice may come down to your team’s skillset and preferred workflow: if you want a managed interactive workspace, Databricks wins; if your team loves AWS tooling, EMR will feel natural; if your org is cloud-native with containers, K8s might slot right in.
Conclusion
There isn’t a universal winner when it comes to these platforms. Each one optimizes for a different blend of cost, performance, and productivity. Databricks maximizes delivery speed and governed collaboration; Amazon EMR keeps processing fully inside your cloud account with familiar security and strong cost levers; Spark-on-Kubernetes delivers portability and ownership once you invest in platform engineering. The practical way to choose is to start from your non-negotiables. What are you data boundaries and compliance, workload shape (bursty batch vs always-on streaming), and the skills and release process your team already uses? After answering these questions, you will have a clearer picture of which will better suit your use case. Furthermore, model your total cost of ownership and run a small pilot to measure time-to-first-task, autoscaling behavior, shuffle-heavy runtime, and day-two operations on your real datasets. Most mature teams end up using more than one engine: Databricks for rapid, short-lived pipelines and collaborative analytics; EMR for production jobs that must remain entirely within the account; and Spark-on-Kubernetes where container standardization and long-running services benefit from explicit control. The one which best suits you is inherently tied to your needs and use cases. In Part 2, this lens is applied to concrete scenarios to show where each option truly shines.
Quick Comparison Summary: Databricks vs EMR vs Spark-on-Kubernetes
| Platform | Pros (Highlights) | Cons (Watchouts) |
|---|---|---|
| Databricks | – Fast time-to-value with job clusters, pools & auto-terminate (great for burst runs) – Optimized runtime & features (Delta/Photon, Autoloader, DLT) that reduce plumbing – Strong governance & collaboration (Unity Catalog, lineage, notebook workspace) – Autoscaling absorbs short-lived spikes without cluster babysitting |
– Premium pricing (DBUs on top of compute), especially for always-on streams – Some vendor lock-in & proprietary surface area to learn – Less control over low-level infra |
| Amazon EMR | – Runs fully inside your AWS account & VPC (tightest control/traceability) – Deep AWS integration (S3/IAM/KMS/CloudWatch) and familiar auditing – Highly configurable Spark/Hadoop stack; predictable costs with Reserve Instances/Savings Plans – EMR Serverless option for on-demand jobs without cluster management |
– More hands-on ops for EMR on EC2 (tuning, upgrades, lifecycle) – Transient EC2 clusters have slower cold starts than DB job clusters/K8s pods – Collaboration UX less rich than Databricks |
| Spark-on-K8s | – Maximum control & portability (containers, any cloud/on-prem; no platform fees) – Excellent bin-packing & autoscaling for many short jobs; high utilization – First-class with modern DevOps (CI/CD, GitOps, canary/blue-green rollouts) – Pairs well with Kubeflow for declarative deploy/verify/promote of long-running apps |
– Highest platform complexity: you own Kubernetes + Spark ops, monitoring, recovery – You must assemble governance (RBAC, network policies, audit logs) yourself – Smaller “managed” ecosystem vs Databricks/EMR |
References
- Chaos Genius. “AWS EMR vs Databricks: 9 Essential Differences (2025).”
https://www.chaosgenius.io/blog/aws-emr-vs-databricks/
- Junaid Effendi. “Benchmarking Spark — Open Source vs EMRs.”
https://www.junaideffendi.com/p/benchmarking-spark-open-source-vs
- Spot.io. “Kubernetes Cost Optimization with Spot Ocean.”
https://spot.io/product/ocean/
- Pretti, Bernardo. “Deploying Apache Spark Clusters: A Comparison of EC2, EMR, Databricks & More.”
https://medium.com/@prettibernardo/deploying-apache-spark-clusters-a-comparison-of-ec2-emr-databricks-more-d6d16d7de710
- Spot.io. “The Pros and Cons of Running Apache Spark on Kubernetes.”
https://spot.io/blog/the-pros-and-cons-of-running-apache-spark-on-kubernetes/
- Apache Spark. “Running Spark on Kubernetes.”
https://spark.apache.org/docs/latest/running-on-kubernetes.html
- AWS Documentation. “What is Amazon EMR?”
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-what-is-emr.html
- AWS. “Amazon EMR Pricing.”
https://aws.amazon.com/emr/pricing/
- AWS Documentation. “What is Amazon EMR Serverless?”
https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/emr-serverless.html
- AWS. “Amazon EMR Serverless.”
https://aws.amazon.com/emr/serverless/
- AWS Documentation. “What is Amazon EMR on EKS?”
https://docs.aws.amazon.com/emr/latest/EMR-on-EKS-DevelopmentGuide/emr-eks.html
- AWS Big Data Blog. “Amazon EMR on Amazon EKS provides up to 61% lower costs and up to 68% performance improvement for Spark workloads.”
https://aws.amazon.com/blogs/big-data/amazon-emr-on-amazon-eks-provides-up-to-61-lower-costs-and-up-to-68-performance-improvement-for-spark-workloads/
- Databricks Docs (AWS). “Connect to serverless compute.”
https://docs.databricks.com/aws/en/compute/serverless/
- Databricks. “Pricing.”
https://www.databricks.com/product/pricing
- Databricks Docs (AWS). “Compute.”
https://docs.databricks.com/aws/en/compute/
- Databricks Docs (AWS). “High-level architecture.”
https://docs.databricks.com/aws/en/getting-started/high-level-architecture
- Kubeflow Docs. “An overview for Spark Operator.”
https://www.kubeflow.org/docs/components/spark-operator/overview/