Part 2 – The Use Cases
Usage Scenarios and Recommendations
Choosing between Databricks, EMR or opensource Spark-on-Kubernetes can be a confusing and daunting task for developers and managers alike. In the part 1 of this blog, I highlighted some of the strengths and weaknesses each option could provide. Now, let’s apply those dimensions to specific scenarios. Depending on your use case and/or organization, one platform may emerge as the most suitable:
1. Burst ETL Pipelines (Infrequent, High-Compute Jobs)
Scenario: You have an ETL job that runs once an hour, processing a huge batch of data in a short time. This hourly log processing job spins up 100 cores for 5 minutes and then shuts down. Here, fast startup and low idle cost are critical. You don’t want to pay for a big cluster all day if it’s only used briefly.
Requirements:
- Keep all processing and data within an approved boundary (private VPC) with controlled or no public egress.
- Encrypt in transit and at rest with customer-managed keys; enforce fine-grained access control.
- Ensure full auditability with immutable logs and lineage.
- Use change-managed, reproducible deployments (policy-as-code) with defined disaster recovery objectives.
Winner – Databricks: Databricks is a good fit due to fast cluster launch options (especially with job clusters or pools). You can schedule jobs on a job cluster that starts when the job starts and terminates after, minimizing idle time. Startup overhead is low, and Databricks can auto-terminate clusters after a few minutes of inactivity. This means you pay only for the runtime of your job plus a small DB overhead. Additionally, Databricks’ autoscaling will handle sudden spikes in required compute within the job. The downside is the DBU cost for many ephemeral runs might accumulate. If cost is less of a concern than reliability and simplicity, Databricks works well. It essentially provides serverless-like convenience for bursty workloads.
Runner-Up – Spark on Kubernetes: Spark-on-K8s is well-suited for bursty workloads if you already have a Kubernetes cluster available. You submit the Spark application on demand; with cluster autoscaling enabled, the platform adds nodes for the job and removes them afterward. The advantage is no separate cluster to create each time; the overhead is just starting a Kubernetes pod, which is typically a matter of seconds. This approach shines when you have many short jobs throughout the day, as Kubernetes can pack them efficiently onto a pool of nodes, scaling up and down as necessary. The result is high resource utilization and low idle cost. The trade-off is you must maintain that K8s cluster. If you have one already for other services, leveraging it for Spark can make a lot of sense for bursty jobs. Spark on K8s is excellent for bursts provided you have the infrastructure and automation to support on-demand Spark submissions.
Solid Option – Amazon EMR: Amazon EMR has traditionally handled this scenario by spinning up transient clusters via scripts, EventBridge triggers, and/or AWS Step Functions. This is the correct solution if the cluster startup time (several minutes) isn’t a concern. If you need to minimize time-to-first-task, EMR on EC2 is less ideal. While more opinionated and less customizable than EMR on EC2, EMR Serverless lets you submit Spark jobs without managing clusters; resources spin up for the job and scale down automatically with per-second billing. This is a good solution for those who need immediate cluster start-ups without the cost overhead of Databricks; but keep in mind, EMR Serverless is a less developed platform. Additionally, expect a brief cold-start rather than an instantaneous execution. That being said, this can be ideal for infrequent jobs because you pay nothing when your operation is idle. If your bursts are very brief but intense, EMR on EC2 may not be the best fit; but EMR Serverless (however limited) could be a strong alternative for this use case.
Recommendation: If your priority is ease and speed and you don’t mind a bit of extra machine cost, Databricks is the simplest choice for bursty ETL. If you’re already running Kubernetes, Spark-on-K8s gives you agility and cost efficiency but be prepared to invest in automation to fully benefit from it. If your priority is cost optimization, can tolerate longer start-up times and you’re AWS-centric, EMR on EC2 could be a solution as well; that said, EMR Serverless is now a strong contender for this use case, though it still lacks some low-level customizability and control compared to the other two options.
2. Long-Running Streaming Jobs
Scenario: You have streaming ETL jobs (using Spark Structured Streaming, for example) that run 24/7, ingesting data from sources like Kafka or IoT feeds, and updating datasets or dashboards continuously. These jobs require stability and low-latency processing, and they run indefinitely.
Requirements:
- Streams must be able to auto-recover on failure and resume from checkpoints without data loss.
- Latency should remain steady under load (if there are increases in data throughput).
- Scaling should be predictable (stateful jobs often need a controlled restart to change parallelism).
- Costs should be optimized for an always-on cluster.
Winner – Spark on Kubernetes: Treating the stream as a first-class production service plays to Kubernetes’ strengths. Using Kubeflow as the control plane, package the streaming application packaged as a container and defined as a Spark Application or as a spark-submit step. Kubeflow Pipelines then orchestrates the create, verify, promote, and maintain for the long-running job. A “deploy” pipeline can parameterize image tag, checkpoint location (S3/MinIO), state store settings, and parallelism, then apply the manifest, wait on health signals (e.g., running state) and gate rollout using canary logic, green–blue logic, etc. At runtime, if a driver or executor pod fails, Kubernetes reschedules it and Structured Streaming resumes from checkpoints; maintenance and parallelism changes happen via predictable, pipeline-driven restarts. This pairing delivers consistent 24/7 operations, efficient co-location with other services, and explicit cost controls (reserved nodes for drivers, safe spot for executors) in exchange for owning the Spark+K8s operational service. While this option does have a greater amount of upfront complexity, it is the winner over the long term.
Runner-Up – Amazon EMR: For an always-on stream in AWS, a long-lived EMR on EC2 cluster offers deep integrations with Kinesis/S3, EMRFS consistency for S3 sinks, and fine-grained instance selection (memory-optimized for heavy state, IO-optimized for write-intensive flows). Observability is straightforward via CloudWatch, Spark UI, and the History Server, with IAM-native security and networking controls. Costs are predictable with Reserved Instances or Savings Plans, making EMR attractive when the stream truly runs 24/7. While autoscaling is available, stateful Structured Streaming typically favors sizing for peak plus headroom and using checkpointed, controlled restarts to change parallelism. You can pace upgrades for stability and design high availability with strong checkpoints and recovery runbooks, or maintain a warm standby if your recovery time objective demands it. EMR fits AWS-centric teams that want full control and stable, integrated 24/7 streaming without adopting Kubernetes.
Solid Option – Databricks: A strong managed option for 24/7 streaming. Autoloader simplifies incremental ingestion and schema evolution from object storage, while Delta Live Tables adds declarative pipelines, built-in quality rules, and automated recovery, which is useful for long uptimes. Streams typically run on a continuous job cluster with executor autoscaling to absorb throughput variation. Monitoring/alerts come via the Spark UI, query metrics (lag, batch duration), and Delta Live Tables event logs. As with any stateful Structured Streaming job, significant parallelism changes are best handled with a controlled restart from checkpoints. The primary trade-off is cost: an always-on cluster accrues continuous DBU and compute charges; but you can mitigate this by right-sizing instances, constraining min/max autoscaling, and, when appropriate, using Delta Live Tables to manage resources more efficiently. If developer velocity and operational simplicity are paramount, Databricks delivers a smooth path to reliable streaming with minimal platform plumbing.
Recommendation: For 24/7 streaming with strict reliability, steady latency under load, predictable scaling via controlled restarts, and optimized always-on cost, prioritize Spark-on-Kubernetes orchestrated by Kubeflow. You gain repeatable workflows, checkpoint-backed auto-recovery, and explicit cost levers (e.g., reserved nodes for drivers, safe spot for executors). If you’re AWS-centric or want native integrations with Kinesis/S3, EMR on EC2 is a good second choice. Databricks is a solid option if your main concern is simplicity and developer velocity, but this comes at a higher machine operating cost over the long term.
3. Regulated Environments (Financial Services, Healthcare, etc.)
Scenario: You work in a highly regulated industry where data privacy, auditability, and compliance are paramount. For instance, a bank processing transactions with personally identifiable information (PII), or a healthcare analytics company dealing with patient data. The priority here is governance, security isolation, and meeting regulatory standards, often more so than raw performance or cost.
Requirements:
- Workloads must remain within a defined security boundary (private subnets/VPC-only or on-prem).
- Fine-grained access control and policy-as-code guardrails.
- Comprehensive audit trails for data and admin actions.
Winner – Amazon EMR: Many regulated organizations choose Amazon EMR because it runs inside your own Amazon Web Services account, giving you direct control and keeping all data within your security boundary. Data can remain in Amazon’s object storage with encryption, and you can rely on Amazon Web Services’ established compliance programs to satisfy regulators (including eligibility for United States health-privacy requirements and the option to sign a business associate agreement). You can place clusters in private network segments with no internet access, reachable only through approved access points such as bastion hosts, and enforce strict identity and access management so only specific roles can launch clusters or read particular datasets. Encryption keys are handled through the provider’s key management service, letting you decide who can decrypt. For auditing, service logs can stream to your security monitoring system to detect and investigate access. If you need additional governance, you can layer open-source tools such as Apache Ranger or Apache Atlas. The security model is familiar: when an auditor asks who can access data and how it is protected, you can point to access policies, encryption controls, network firewalls, and detailed logs. The main trade-off is collaboration features. EMR is primarily an infrastructure service rather than a shared notebook workspace, but many regulated teams prefer that separation. If your organization already trusts Amazon Web Services and wants everything to stay inside that boundary, EMR is an excellent fit.
Runner-Up – Databricks: Databricks combines strong governance with a collaborative workspace, which is why it is widely used in finance and healthcare, including deployments in government-only regions on major clouds. Unity Catalog provides centralized, fine-grained permissions down to tables and columns, data masking, and end-to-end lineage, while the platform records detailed audit events for notebooks, queries, and job runs. Many organizations deploy Databricks in a single-tenant network inside their own cloud account with private connectivity, so traffic stays within their security boundary. If your regulators allow a third-party managed service that holds recognized security and privacy certifications, Databricks can accelerate delivery while meeting compliance obligations. The trade-off is accepting a managed management layer: some metadata, such as notebooks and logs, is stored by the service, so you should place it in an approved region and align retention policies. For teams that need collaboration under tight controls, Databricks strikes a practical balance between governed access and productivity.
Solid Choice – Spark on Kubernetes: Spark on Kubernetes fits organizations that must keep processing on self-managed infrastructure or fully isolated cloud networks. You can run the cluster in a private data center or an isolated cloud environment with no public internet access, integrate with your company’s identity system, and enforce network policies that separate sensitive workloads. There is no outside vendor handling runtime data, because Spark and Kubernetes are open source and under your control, which can satisfy strict residency or isolation rules. The trade-off is that you must prove compliance yourself: set up encryption in transit and at rest, enable and retain audit logs for the platform and jobs, document access paths, and maintain the evidence that auditors will request. Many large firms build an internal Kubernetes platform that security has pre-approved, then run Spark on top so jobs inherit those controls. This route gives maximum ownership and assurance, but it also carries the most operational work; where policy allows, some teams choose managed services to reduce the audit and maintenance burden.
Recommendation: If your priority is to minimize audit friction and keep everything inside a boundary you already control, choose Amazon EMR first: it runs in your own Amazon Web Services account, supports private networking and customer-managed encryption keys, and produces uniform logs that are easy to present to auditors. If you need governed collaboration and faster delivery, and your risk team allows a compliant managed service, Databricks is a strong second. Unity Catalog, masking, lineage, and detailed audit events come with a productive workspace, at the cost of accepting a managed control layer and steady platform fees. Choose Spark on Kubernetes when policy requires self-managed or on-premises operation, or when you want ultimate ownership of residency and controls; but plan for much heavier operational and certification lift (audit pipelines, hardened images, documented change management, etc.).
Conclusion
Choosing the “best” ETL engine isn’t about a universal winner, it’s about matching the platform to the job, the governance posture, and your team’s operating model. In the bursty world of short, high-compute batches, Databricks offers the fastest time from trigger to results with simple operations and autoscaling, and that convenience is often worth the premium. For always-on, low-latency streams, Spark on Kubernetes, orchestrated with Kubeflow, wins on long-term control, predictable rollouts, and cost discipline once you’re willing to invest in solid platform engineering. In tightly regulated settings where isolation, auditability, and clear lines of control dominate, Amazon EMR shines by keeping processing inside your own cloud account with familiar security building blocks and well-understood compliance evidence.
If you take one thing from these blogs, let it be this simple decision rubric: start with your constraints, governance and data boundaries, latency and uptime expectations, and the skills you already have. Let those drive the platform choice, not the other way around. Most mature teams mix and match: Databricks for rapid, bursty pipelines and collaboration; EMR for production jobs that must remain fully inside the cloud account; Spark on Kubernetes where container standardization and maximum ownership pay dividends. Use the scenarios here as a template for a quick pilot: validate startup times, steady-state cost, and audit readiness against your real data and service-level goals. With that evidence in hand, you can pick (and combine) the right engines to deliver a scalable, efficient, and compliant data platform, now and as your needs evolve.
Below is a quick comparison summary to help in your decision-making:
Quick Comparison Summary: Databricks vs EMR vs Spark-on-Kubernetes
| Platform | Where it Shines |
|---|---|
| Databricks | Burst ETL: winner for short, high-compute jobs with minimal idle and quick start/stop. Solid but costlier for 24/7 streaming in exchange for simplicity. |
| Amazon EMR | Regulated environments: winner when you must keep all workloads inside your account with strong auditability. Good second choice for 24/7 streams with stable, sized-for-peak clusters. |
| Spark-on-K8s | 24/7 streaming: winner for long-term control, predictable restarts, and cost discipline once the platform is in place. Strong runner-up for burst if you already run K8s. |
References
- Apache Spark. “Structured Streaming Programming Guide.”
https://spark.apache.org/docs/3.5.1/structured-streaming-programming-guide.html
- Apache Spark. “Running Apache Spark on Kubernetes.”
https://spark.apache.org/docs/latest/running-on-kubernetes.html
- Kubernetes SIG Autoscaling. “Cluster Autoscaler.”
https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler
- Kubeflow. “Kubeflow Pipelines Overview.”
https://www.kubeflow.org/docs/components/pipelines/
- Kubeflow Spark Operator. “Spark Operator for Kubernetes.”
https://github.com/kubeflow/spark-operator
- Amazon Web Services. “What Is Amazon EMR?”
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-what-is-emr.html
- Amazon Web Services. “Amazon EMR Serverless – User Guide.”
https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/what-is-emr-serverless.html
- Amazon Web Services. “Encrypt data at rest and in transit with Amazon EMR.”
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-data-encryption.html
- Amazon Web Services. “Configure networking in a VPC for Amazon EMR (public and private subnets).”
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-plan-vpc-subnet.html
- Amazon Web Services. “Supported streaming connectors (Kafka) on EMR.”
https://docs.aws.amazon.com/emr/latest/EMR-Serverless-UserGuide/jobs-spark-streaming-connectors.html
- Amazon Web Services. “Using the Spark Structured Streaming Amazon Kinesis Data Streams connector on EMR.”
https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-spark-structured-streaming-kinesis.html
- Amazon Web Services. “Amazon EMR Serverless now supports HIPAA, HITRUST, SOC, and PCI DSS workloads.”
https://aws.amazon.com/about-aws/whats-new/2023/02/amazon-emr-serverless-hipaa-hitrust-soc-pci-dss-workloads/
- Databricks. “Compute (Clusters) – Management & Auto-Termination.”
https://docs.databricks.com/aws/en/compute/clusters-manage
- Databricks. “Autoscaling for Compute.”
https://docs.databricks.com/aws/en/compute/configure#automatic-termination-and-autoscaling
- Databricks. “Cluster Pools (Reuse/Pre-Warm Compute).”
https://docs.databricks.com/aws/en/compute/pools
- Databricks. “Job Clusters (Per-Run Compute).”
https://docs.databricks.com/aws/en/jobs/compute
- Databricks. “Auto Loader (Incremental File Ingestion).”
https://docs.databricks.com/aws/en/ingestion/auto-loader/index
- Databricks. “Monitoring Structured Streaming Queries.”
https://docs.databricks.com/aws/en/structured-streaming/stream-monitoring
- Databricks. “Unity Catalog (Central Governance, Permissions, Lineage).”
https://docs.databricks.com/aws/en/data-governance/unity-catalog/index
- Databricks. “Audit Logs.”
https://docs.databricks.com/aws/en/administration-guide/account-settings/audit-logs
- Databricks. “Compliance on AWS (HIPAA and more).”
https://docs.databricks.com/aws/en/security/privacy/
- Databricks. “Private Connectivity (AWS PrivateLink options).”
https://docs.databricks.com/aws/en/security/network/front-end-private-link
- Databricks. “Delta Live Tables (Pipelines) – API & Quickstart.”
https://docs.databricks.com/api/workspace/pipelines
and
https://docs.databricks.com/aws/en/notebooks/source/dlt-wikipedia-sql.html
- Apache Spark. “Structured Streaming + Kafka Integration.”
https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html
- 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 by NetApp. “Kubernetes Cost Optimization with Spot Ocean.”
https://spot.io/product/ocean/
- Bernardo Pretti. “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