Databricks vs fabric: which one do you actually need

People keep asking which platform is better, databricks or fabric. Wrong question. They're optimized for different things.

Databricks is for people who want control. Fabric is for people who want integration. Both run spark, both handle delta tables, but the philosophy is completely different.

Spent the last two years working with both. Here's what actually matters when picking between them.

The core difference

Databricks: you manage clusters, choose exact instance types, configure everything down to the network level. Full control over the data platform.

Fabric: microsoft manages the infrastructure, you get capacity units, everything runs in one integrated platform. Less control, more simplicity.

Think of databricks like aws ec2. You pick instance types, configure autoscaling, manage networking. Powerful but requires expertise.

Think of fabric like azure app service. You pick a tier, deploy your stuff, let microsoft handle the rest. Simpler but less flexible.

Neither is wrong. Depends what you need.

Where databricks wins

Atomic cost control

This is the biggest advantage. In databricks you pay per cluster per hour. You know exactly what's running and what it costs.

Example:

• Spin up 5 node cluster with i3.xlarge instances
• Run your job for 30 minutes
• Pay for exactly 2.5 hours of compute (5 nodes * 0.5 hours)
• Shut it down, cost stops

You can optimize costs by:

• Using spot instances (60-80% cheaper)
• Right-sizing clusters per job
• Auto-terminating idle clusters
• Using smaller clusters for dev work

In fabric you buy capacity units. Multiple workloads share that capacity. You can't say "run this job on 2 executors and nothing else" to minimize cost.

For cost optimization at scale databricks gives you way more control.

Cluster customization

Databricks lets you configure everything:

# example cluster config
{
  "cluster_name": "etl-cluster",
  "spark_version": "13.3.x-scala2.12",
  "node_type_id": "i3.xlarge",
  "num_workers": 5,
  "autoscale": {
    "min_workers": 2,
    "max_workers": 10
  },
  "spark_conf": {
    "spark.sql.adaptive.enabled": "true",
    "spark.sql.shuffle.partitions": "800",
    "spark.databricks.delta.optimizeWrite.enabled": "true"
  },
  "aws_attributes": {
    "availability": "SPOT_WITH_FALLBACK"
  }
}

You pick the instance type, memory, cores, local ssd. You can create different cluster profiles for different workload types.

Fabric gives you starter pools or custom spark pools with limited configuration options. Can't pick exact compute specs.

Multi-cloud support

Databricks runs on aws, azure, and gcp. Same platform, same notebooks, different cloud.

If you need multi-cloud or plan to migrate clouds this matters. Fabric only runs on azure.

Mature ecosystem

Databricks has been around longer. More features, more integrations, more community knowledge.

Things databricks has that fabric doesn't:

• MLflow built in for model tracking
• Delta live tables for declarative pipelines
• Unity catalog for multi-workspace governance
• Photon engine for faster queries
• More advanced autoscaling

Fabric is catching up but databricks is ahead on pure data platform features.

Better for data science teams

If your team is heavy on machine learning databricks is better. The notebook experience is more mature, MLflow integration is native, model serving is built in.

Fabric has notebooks but they're more focused on data engineering. The ML story exists but isn't as polished.

Where fabric wins

Power bi integration

This is huge if you're already a power bi shop. Fabric and power bi are the same platform.

Direct lake mode: semantic models query delta tables in your lakehouse directly. No import, no directquery latency, just works.

This only exists in fabric. In databricks you'd need to:

1. Export data from delta tables
2. Load into power bi via import or directquery
3. Deal with refresh schedules and connection management

Fabric it's seamless. Build your lakehouse, create a semantic model on top, reports just work.

For organizations with heavy power bi usage this integration alone justifies fabric.

Office 365 look and feel

Fabric looks like power bi which looks like the rest of microsoft 365. Your business users already know the interface.

Databricks has a more technical UI. It's powerful but intimidating for non-technical users.

If you need business users creating dataflows or building reports fabric's familiar interface helps with adoption.

No cluster management

In fabric you don't think about clusters. Click run, it executes, you're done.

No decisions about:

• Instance types
• Autoscaling rules
• Spot vs on-demand
• Cluster startup time
• Idle termination

Microsoft handles it. For teams without deep spark expertise this is valuable. You can focus on the data work not infrastructure.

Unified platform

Fabric includes:

• Data warehouses
• Lakehouses
• Dataflows
• Data pipelines
• Power bi
• Real-time analytics

All in one platform with shared security, shared storage (OneLake), shared capacity.

Databricks is focused on the data engineering and ML parts. For BI and reporting you need to integrate with other tools.

If you want everything in one place fabric is more complete.

Simpler for power bi developers

If your team is power bi developers who need to learn data engineering fabric is the easier path.

They already know power query, dax, the workspace model. Fabric extends what they know instead of requiring a completely new platform.

Databricks requires learning a new interface, new concepts, new workflows. Higher learning curve.

Cost comparison: DBUs vs CUs

Both platforms use capacity units but they work differently.

Databricks DBUs (databricks units):

• Charged per cluster hour
• Rate depends on cluster type (jobs, all-purpose, ml)
• Example: standard all-purpose cluster is ~0.40 DBU per hour
• DBU cost varies by cloud and region (~$0.10-0.15 per DBU)
• You pay cloud compute cost + databricks DBU cost

Fabric CUs (capacity units):

• Buy capacity tier (F2, F8, F16, etc)
• All workloads share that capacity
• Example: F64 is 64 CUs, runs constantly
• Pay flat rate for the tier regardless of usage
• No separate compute cost, it's included

When databricks is cheaper

Sporadic workloads:

If you run jobs a few hours per day databricks is cheaper. Spin up clusters only when needed, pay for actual usage.

Example:

• Run 2 hours of processing per day
• Databricks: pay for 2 hours
• Fabric F16: pay for 24 hours even if idle 22 hours

Highly optimized jobs:

If you can optimize jobs to run fast databricks rewards you with lower costs. Finish in 10 minutes instead of 30, pay for 10 minutes.

Fabric charges by capacity tier not by job duration.

Spot instance usage:

Databricks spot instances are 60-80% cheaper than on-demand. Fabric doesn't have spot equivalent.

For fault-tolerant batch jobs spot instances make databricks way cheaper.

When fabric is cheaper

Consistent heavy usage:

If you max out capacity most of the day fabric is cheaper. Flat rate for unlimited jobs.

Example:

• Running jobs 20 hours per day
• Fabric F16: flat monthly rate
• Databricks: expensive for constant cluster uptime

Mixed workloads:

If you have spark jobs, power bi reports, dataflows, and pipelines all running fabric capacity is shared.

In databricks you'd pay separately for:

• Spark clusters
• Power bi capacity
• Separate BI tool

Fabric bundles everything into the capacity price.

Teams without optimization expertise:

If you can't optimize spark jobs well you'll waste databricks compute. Fabric's flat rate caps your cost even with inefficient code.

Not ideal but limits downside risk.

When to pick databricks

Choose databricks if:

You need fine-grained cost control

Every dollar matters and you have expertise to optimize cluster usage.

You're primarily a data engineering or ML team

Not heavy on BI, focused on pipelines and models. Don't need power bi integration.

You want multi-cloud

Running on multiple clouds or planning to migrate between them.

You need advanced features

MLflow, delta live tables, unity catalog, photon engine matter for your use case.

You have spark expertise

Team is comfortable managing clusters, tuning configurations, optimizing costs.

You're cost optimizing at scale

Processing terabytes daily and can save significant money with spot instances and right-sized clusters.

When to pick fabric

Choose fabric if:

You're already a power bi organization

Heavy power bi usage, want to add data engineering without learning new platform.

You want simplicity over control

Don't want to manage clusters, just want to write notebooks and run jobs.

You need integrated BI and data engineering

Want data warehouse, lakehouse, dataflows, pipelines, and reports in one platform.

Your team is mostly BI developers

People know power query and dax, less comfortable with pure data engineering.

You're Microsoft-committed

Already using azure, office 365, dynamics. Staying in ecosystem makes sense.

You want predictable costs

Flat capacity pricing is easier to budget than variable cluster costs.

Can you use both?

Yes and some organizations do.

Common pattern:

• Fabric for BI workloads and business user self-service
• Databricks for heavy data engineering and ML
• Export from databricks to fabric lakehouse for reporting

This works but adds complexity. You're managing two platforms, two security models, two sets of costs.

Only makes sense if you have specific needs that justify the overhead. Most teams should pick one.

Migration considerations

Moving from databricks to fabric

What transfers:

• Delta tables (same format, direct read)
• Pyspark code (mostly compatible)
• SQL queries (similar syntax)

What doesn't:

• MLflow experiments (need to recreate)
• Jobs scheduling (rebuild in fabric pipelines)
• Cluster configs (need to rethink for fabric capacity)
• Custom libraries (reinstall in fabric)

Medium difficulty migration. Code is mostly reusable, infrastructure needs rebuild.

Moving from fabric to databricks

What transfers:

• Delta tables (databricks can read fabric lakehouses)
• Pyspark code (mostly compatible)
• SQL queries (similar syntax)

What doesn't:

• Power bi direct lake mode (lose this feature)
• Dataflows (rebuild as databricks notebooks or delta live tables)
• Integrated capacity (need to size clusters manually)

Also medium difficulty. Lose power bi integration which might be a dealbreaker.

Neither direction is trivial but both are doable if you realize you picked wrong.

My actual recommendation

After working with both here's what i tell people:

Start with fabric if:

• You're coming from power bi world
• Your use case is BI and reporting with data engineering to support it
• You want to get started quickly

Start with databricks if:

• You're building a data platform from scratch
• Your focus is data engineering and ML not BI
• You have the expertise to manage it

Most organizations reading this are probably power bi shops. For them fabric is the better starting point. Learn the platform, build some lakehouses, see if it meets your needs.

If you hit limitations (need multi-cloud, need better cost control, need advanced ML features) then consider databricks.

Starting with databricks when you really need fabric just makes everything harder. The reverse is also true.

Integration between the two

One thing worth knowing: they integrate reasonably well.

Databricks can read fabric lakehouses via onelake paths. Fabric can read databricks delta tables via external locations.

So if you have one and need features from the other you can connect them without full migration.

Not as clean as using one platform but better than being stuck.

The technical details

Both run spark. Both use delta lake. The core technology is similar.

Differences are in how that technology is packaged and managed.

If you understand spark optimization the concepts transfer between platforms. Same shuffle operations, same partitioning strategies, same delta features.

The lakehouse architecture works the same way. Bronze/silver/gold medallion patterns, delta table optimization, it's all identical.

You're learning transferable skills either way.

Final thoughts

Databricks and fabric both solve the modern data platform problem. They just optimize for different users.

Databricks is for teams who want control and have the expertise to use it. Fabric is for teams who want integration and simplicity.

For power bi developers moving into data engineering fabric is the natural path. You're extending what you know instead of learning a completely new platform.

For data engineering teams building from scratch databricks gives you more power and flexibility.

Neither is wrong. Pick based on your team's skills, your existing tech stack, and what you actually need.

If you're new to fabric start with my intro guide for power bi developers. If you do go with fabric make sure you understand the spark configuration options in the optimization guide.

The choice matters but isn't permanent. You can switch platforms if needed. More important to start building than to agonize over which platform is theoretically better.