Krunal 
Big data processing requires the right tools. Two popular options are Apache Spark and Databricks.
But which one should you pick?
This guide breaks down both platforms. You’ll learn what each does, how they differ, and which fits your needs.
What is Apache Spark?
Apache Spark is free, open-source software. It processes massive amounts of data fast.
UC Berkeley’s AMPLab created it in 2009. Spark uses in-memory computing. This makes it much faster than older tools.
What Spark Does:
Spark handles multiple data tasks:
- Batch processing for large data sets
- Real-time stream processing
- Machine learning with MLlib
- Graph analysis through GraphX
- SQL queries via Spark SQL
Spark works on your own servers or in the cloud. You control everything.
What is Databricks?
Databricks is a paid cloud platform. The same people who built Spark created it.
Think of it as Spark with extras. It adds helpful features and removes hassle.
Databricks runs on AWS, Azure, or Google Cloud. The platform manages everything for you.
What Databricks Adds:
You get more than basic Spark:
- Fully managed Spark clusters
- Easy team collaboration tools
- Delta Lake for better data storage
- MLflow for machine learning projects
- Auto-scaling that adjusts resources
- Built-in security and compliance
The interface is simple. Teams work together easily.
Key Differences Between Spark and Databricks
Cost Structure
Apache Spark: Free software. But you pay for servers, storage, and staff time.
Databricks: Monthly subscription. Infrastructure is included. No server management needed.
Setup and Maintenance
Apache Spark: You install and configure everything. Updates are manual. You handle all problems.
Databricks: Ready to use immediately. Updates happen automatically. Support team helps with issues.
Technical Knowledge Required
Apache Spark: Needs skilled engineers. You must understand clusters, memory, and performance tuning.
Databricks: Simpler interface. Less technical knowledge required. Good for mixed teams.
Extra Features
Apache Spark: Core processing only. Add-ons require manual setup.
Databricks: Includes Delta Lake, MLflow, and collaboration tools. Everything works together.
Resource Management
Apache Spark: Manual scaling. You predict needs and adjust servers.
Databricks: Automatic scaling. Resources grow or shrink based on workload.
When to Choose Apache Spark
Pick Spark if you:
- Want complete control over infrastructure
- Have skilled data engineers on staff
- Prefer open-source solutions
- Need to minimize subscription costs
- Already have server infrastructure
- Want to customize everything
Spark works well for tech-heavy teams. It offers maximum flexibility.
When to Choose Databricks
Pick Databricks if you:
- Want quick setup without hassle
- Need team collaboration features
- Lack infrastructure management staff
- Value automatic scaling
- Want integrated machine learning tools
- Need enterprise security features
Databricks suits businesses focused on results, not server management.
Cost Comparison
Apache Spark Costs:
You pay for:
- Server hardware or cloud instances
- Network bandwidth
- Storage systems
- Staff to manage everything
- Monitoring tools
- Security software
Total costs vary widely. Small setups start around $500/month. Large deployments exceed $50,000/month.
Databricks Costs:
Databricks charges based on:
- Number of processing units used
- Amount of data stored
- Features you enable
- Cloud provider you choose
Prices start around $1,000/month. Enterprise plans cost more but include support.
Databricks often costs less overall. You avoid hiring infrastructure specialists.
Performance Differences
Both use the same core Spark engine.
Apache Spark: Performance depends on your setup. Bad configuration slows everything down.
Databricks: Optimized automatically. The platform tunes settings for you.
For most users, Databricks runs faster. Engineers spent years optimizing it.
Data Storage Options
Apache Spark: Works with any storage. You choose HDFS, S3, or databases. Setup takes time.
Databricks: Includes Delta Lake. This adds:
- ACID transactions for data safety
- Time travel to view old versions
- Faster queries through optimization
- Schema enforcement to prevent errors
Delta Lake makes data more reliable. It’s built into Databricks.
Machine Learning Support
Apache Spark: Includes MLlib library. You build everything manually.
Databricks: Adds MLflow on top of MLlib. This helps:
- Track experiments easily
- Compare model versions
- Deploy models faster
- Share work with teammates
MLflow saves weeks of custom coding.
Security Features
Apache Spark: Security is your job. You configure:
- Network firewalls
- User authentication
- Data encryption
- Access controls
This requires security expertise.
Databricks: Security comes built-in:
- Automatic encryption
- Role-based access control
- Compliance certifications
- Audit logging
- Integration with identity systems
Enterprise security is ready to go.
Team Collaboration
Apache Spark: Engineers work in separate code files. Sharing progress is manual.
Databricks: Built for teamwork:
- Shared notebooks for code
- Comments and discussions
- Version control built-in
- Real-time collaboration
Data scientists and analysts work together easily.
Learning Curve
Apache Spark: Steep learning curve. New users need months to become productive.
Resources needed:
- Scala or Python knowledge
- Understanding of distributed systems
- Linux server skills
- Cluster management experience
Databricks: Gentler learning curve. Teams get productive in days.
The visual interface helps. Documentation is excellent.
Migration and Portability
Apache Spark: Runs anywhere. Move between clouds easily. No vendor lock-in.
Databricks: Tied to specific clouds. Migration takes planning. But Databricks supports AWS, Azure, and Google Cloud.
Open-source fans prefer Spark’s freedom. Databricks users value convenience.
Support and Community
Apache Spark: Large open-source community. Free help through forums. No official support.
Databricks: Professional support included. Get help from Spark’s creators. Fast response times.
Critical systems need guaranteed support.
Real-World Use Cases
Companies Using Apache Spark:
Tech giants with big engineering teams:
- Netflix for recommendation engines
- Uber for real-time pricing
- Airbnb for search optimization
These companies have hundreds of engineers.
Companies Using Databricks:
Businesses focused on insights:
- Shell for energy analytics
- Comcast for customer data
- Condé Nast for content analysis
These teams want results, not infrastructure headaches.
Making Your Decision
Ask yourself:
Budget Questions:
- Can we afford subscription fees?
- Do we have infrastructure already?
- What’s our total cost of ownership?
Team Questions:
- How skilled are our engineers?
- Do we need collaboration tools?
- Can we hire infrastructure specialists?
Business Questions:
- How fast do we need results?
- Is vendor lock-in acceptable?
- What security level do we need?
Most businesses choose Databricks. It’s faster to deploy and easier to use.
Tech-heavy startups often pick Spark. They want control and have the skills.
The Bottom Line
Apache Spark and Databricks both excel at big data processing.
Spark offers freedom and control. It’s perfect for teams with strong technical skills.
Databricks provides convenience and speed. It suits teams focused on business results.
Your choice depends on:
- Team capabilities
- Budget constraints
- Time to value
- Control requirements
Neither option is wrong. Pick what fits your situation best.
Want help deciding? Consider your team’s strengths and your project timeline.
Most organizations find Databricks worth the cost. It removes obstacles and accelerates results.
But if you have skilled engineers and want maximum control, Spark delivers.
Frequently Asked Questions
Can I use Databricks for free?
Databricks offers a Community Edition. It’s limited but good for learning.
Does Databricks require Spark knowledge?
Basic Spark concepts help. But Databricks simplifies many complex tasks.
Can I migrate from Spark to Databricks?
Yes. Most Spark code runs on Databricks with minimal changes.
Which is better for machine learning?
Databricks includes MLflow. This makes ML projects easier to manage.
Is Databricks worth the cost?
For most teams, yes. Time saved often exceeds subscription costs.
Can small companies use Databricks?
Yes. Start small and scale up. Many startups use Databricks successfully.
Does Spark work in the cloud?
Yes. Run Spark on AWS, Azure, or Google Cloud. You manage everything.
Which option scales better?
Both scale well. Databricks handles scaling automatically. Spark requires manual work.
Last updated: February 2026