Databricks vs AWS EMR vs Apache Spark on Kubernetes: Which for Data Engineering?

Data engineering and ML pipelines need a distributed compute layer. Databricks, AWS EMR, and Spark on Kubernetes are the three main options. They're all built on Apache Spark but the experience, cost, and operational complexity differ dramatically.

What They're All Solving

When you have a dataset too large for a single machine — billions of rows, terabytes of logs, large ML training data — you need distributed processing. Apache Spark splits the work across a cluster of machines.

The question isn't Spark vs something else. It's: how do you want to manage the Spark cluster?

Databricks

Databricks is a cloud-based data platform built around Apache Spark. It abstracts cluster management and adds collaboration, Delta Lake (ACID transactions on data lakes), and MLflow (experiment tracking).

Architecture:

Databricks Control Plane (Databricks-managed)
    ↓
Databricks Data Plane (your cloud account)
    → Auto-scaling Spark clusters on EC2/Azure VMs
    → Delta Lake on S3/ADLS
    → Unity Catalog for data governance

Key features:

• Databricks Notebooks — collaborative Jupyter-style notebooks with real-time co-editing
• Delta Lake — ACID transactions on S3, Z-ordering, time travel queries
• Auto-scaling — clusters scale up/down automatically based on workload
• MLflow — experiment tracking, model registry, deployment

# Databricks SQL example - runs on a cluster you don't manage
from pyspark.sql import SparkSession
 
spark = SparkSession.builder.getOrCreate()
 
# Delta Lake - ACID transactions on S3
df = spark.read.format("delta").load("s3://my-bucket/my-delta-table/")
 
# Time travel
df_yesterday = spark.read.format("delta").option("versionAsOf", 5).load("s3://my-bucket/my-delta-table/")
 
# Stream to Delta
spark.readStream.format("kafka") \
    .option("subscribe", "events") \
    .load() \
    .writeStream.format("delta") \
    .outputMode("append") \
    .start("s3://my-bucket/streaming-output/")

Cost:

• DBU (Databricks Unit) pricing on top of cloud compute
• ~$0.07-0.22 per DBU depending on cluster type
• All-Purpose Cluster (interactive notebooks): 2-3 DBU/hr per node
• Jobs Cluster (automated): 0.5 DBU/hr per node
• Total for a 10-node batch job: ~$5-15/hr

Best for:

• Data engineering + data science teams working together
• Organizations using Delta Lake as their primary storage format
• Teams that need strong governance and data discovery
• Multi-cloud (Databricks runs on AWS, Azure, GCP)

AWS EMR (Elastic MapReduce)

EMR is AWS's managed big data platform. It provisions and manages EC2 instances running Hadoop, Spark, Hive, Presto, and other distributed frameworks.

# Create an EMR cluster
aws emr create-cluster \
  --name "spark-processing-cluster" \
  --release-label emr-7.1.0 \
  --applications Name=Spark Name=Hadoop \
  --instance-type m6g.xlarge \
  --instance-count 5 \
  --ec2-attributes KeyName=my-key \
  --use-default-roles \
  --log-uri s3://my-emr-logs/
 
# Submit a Spark job
aws emr add-steps \
  --cluster-id j-XXXXXXXXXX \
  --steps Type=Spark,Name="Process Data",\
    Args=[--deploy-mode,cluster,s3://my-scripts/process.py,--date,2026-06-24]

EMR Serverless (no cluster management):

# EMR Serverless - no cluster to manage
import boto3
 
emr_serverless = boto3.client("emr-serverless")
 
# Create application once
app = emr_serverless.create_application(
    name="my-spark-app",
    releaseLabel="emr-7.1.0",
    type="SPARK",
    autoStopConfiguration={"enabled": True, "idleTimeoutMinutes": 5},
    autoStartConfiguration={"enabled": True},
)
 
# Submit jobs (scales from 0 automatically)
job = emr_serverless.start_job_run(
    applicationId=app["applicationId"],
    executionRoleArn="arn:aws:iam::123456789:role/EMRServerlessRole",
    jobDriver={
        "sparkSubmit": {
            "entryPoint": "s3://my-scripts/process.py",
            "sparkSubmitParameters": "--conf spark.executor.cores=4 --conf spark.executor.memory=8g",
        }
    },
)

Cost (EMR Serverless):

• $0.052624/vCPU-hour
• $0.0057785/GB-hour memory
• No cluster idle cost (scales to zero)

Best for:

• Teams already deep in the AWS ecosystem
• Cost-conscious teams (no Databricks markup)
• Large batch jobs on a schedule
• Integration with Glue Catalog, Athena, Redshift

Apache Spark on Kubernetes

Run Spark directly on your Kubernetes cluster. No managed service, full control.

# Submit Spark job to Kubernetes
spark-submit \
  --master k8s://https://your-k8s-api:6443 \
  --deploy-mode cluster \
  --name spark-job \
  --conf spark.executor.instances=5 \
  --conf spark.kubernetes.container.image=apache/spark:3.5.0 \
  --conf spark.kubernetes.namespace=spark-jobs \
  --conf spark.kubernetes.driver.pod.name=spark-driver \
  s3a://my-bucket/scripts/process.py

With Spark Operator (Kubernetes-native):

apiVersion: "sparkoperator.k8s.io/v1beta2"
kind: SparkApplication
metadata:
  name: daily-process
  namespace: spark-jobs
spec:
  type: Python
  pythonVersion: "3"
  mode: cluster
  image: "apache/spark-py:3.5.0"
  imagePullPolicy: Always
  mainApplicationFile: "s3a://my-scripts/process.py"
  sparkVersion: "3.5.0"
  restartPolicy:
    type: OnFailure
    onFailureRetries: 3
  executor:
    cores: 2
    instances: 5
    memory: "8g"
    serviceAccount: spark
  driver:
    cores: 1
    memory: "4g"
    serviceAccount: spark
  hadoopConf:
    "fs.s3a.access.key": "AWS_KEY"
    "fs.s3a.secret.key": "AWS_SECRET"

Cost:

• Just EC2/VM costs for the nodes (no service markup)
• Full control over instance types
• Spot instances reduce cost by 60-80%

Best for:

• Teams already running Kubernetes
• Multi-cloud or on-premise environments
• Cost optimization (no managed service markup)
• Teams with strong Kubernetes expertise

Comparison Table

Decision Guide

Choose Databricks if:

• Data science and data engineering teams work closely together
• Delta Lake reliability and ACID transactions are important
• Your team doesn't want to manage infrastructure
• Budget is secondary to velocity

Choose AWS EMR if:

• You're AWS-native and want seamless S3/Glue/Athena integration
• Cost matters but you still want managed service support
• EMR Serverless is a sweet spot for scheduled batch jobs

Choose Spark on Kubernetes if:

• You have strong Kubernetes expertise
• Cost optimization is critical
• Multi-cloud or on-premise requirements
• You want full control over the environment

For most teams starting out: EMR Serverless is the pragmatic middle ground. Zero cluster management, pay per use, and AWS integration. Add Databricks if your data scientists need collaborative notebooks and Delta Lake features.

Resources: Databricks | AWS EMR Serverless | Spark on Kubernetes