Migrating From Azure to Databricks

[Pratikmsbsvm]

Hi Techie,

May someone please help me with Pros and Cons from migrating my Realtime streaming solution from Azure to Databricks.

which component I can replaced with Databricks and what benefit I can get out of it.

Current Architecture:-

 Many Thanks 

[lingareddy_Alva · Accepted solution]

Hi @Pratikmsbsvm 

Looking at your current Azure streaming architecture, I can help you understand the pros and cons of migrating to Databricks. Let me break this down by component and overall considerations:

Components That Can Be Replaced with Databricks
Azure Stream Analytics → Databricks Structured Streaming
- What changes: Replace ASA with Spark Structured Streaming in Databricks
- Benefits: More flexible transformations, custom logic, ML integration, better debugging
- Considerations: Requires more development effort, need Spark expertis

Azure Data Lake Gen2 → Databricks Delta Lake:
- What changes: Use Delta Lake format on your existing ADLS Gen2 storage
- Benefits: ACID transactions, time travel, schema evolution, better performance
- Considerations: Delta Lake works great with ADLS Gen2, minimal migration needed

Azure SQL Database → Databricks SQL + Delta Lake
- What changes: Move analytical workloads to Delta Lake, keep transactional data in SQL DB
- Benefits: Better performance for analytics, unified data platform
- Considerations: May still need SQL DB for transactional systems

Pros of Migration to Databricks
Technical Benefits

Unified Platform: Single platform for streaming, batch, ML, and analytics
Advanced Analytics:
- Built-in ML capabilities, easy model deployment
- Better Performance: Optimized Spark engine, Delta Lake optimizations
- Flexibility: Custom transformations, complex event processing
- Scalability: Auto-scaling clusters, better resource utilization

Operational Benefits
- Simplified Architecture: Fewer moving parts, unified monitoring
- Cost Optimization: Pay-per-use model, automatic cluster termination
- Developer Productivity: Notebooks, collaborative environment, version control
- Data Governance: Unity Catalog for centralized metadata and security

Cons of Migration to Databricks
Complexity & Skills
- Learning Curve: Team needs Spark/Python/Scala expertise
- Development Overhead: More complex than drag-and-drop ASA
- Debugging: Streaming jobs can be harder to troubleshoot

Operational Challenges
- Monitoring: Need to set up comprehensive monitoring for Spark jobs
- Latency: May have slightly higher latency than ASA for simple transformations
- Maintenance: More infrastructure to manage and tune

Cost Considerations
- Compute Costs: Can be higher if not properly optimized
- Learning Investment: Time and training costs for team upskilling.

Migration Strategy Recommendations:
Hybrid Approach (Recommended)
1. Keep: Event Hubs, ADLS Gen2, existing applications
2. Replace: Stream Analytics with Databricks Structured Streaming
3. Enhance: Add Delta Lake format, ML capabilities
4. Gradual: Migrate one pipeline at a time

Components to Retain
- Azure Event Hubs: Excellent integration with Databricks
- ADLS Gen2: Works perfectly with Databricks Delta Lake
- Power BI: Native integration with Databricks SQL
- Existing Applications: Can connect to Databricks via JDBC/REST APIs

When Migration Makes Sense
Migrate if you need:
- Complex transformations or custom business logic
- Real-time ML inference
- Advanced analytics capabilities
- Better cost optimization for large-scale processing
- Unified platform for multiple data workloads

Stay with current setup if:
- Simple aggregations and transformations are sufficient
- Team lacks Spark expertise and timeline is tight
- Current solution meets all performance requirements
- Minimal budget for platform changes.

[Pratikmsbsvm]

Thanks LRALVA. May you please help me with cost part. I am not have Prod level costing knowledge. Thanks.

[lingareddy_Alva]

Hi @Pratikmsbsvm 

Stream Analytics has no upfront costs - you only pay for the streaming
units you consume with no commitment or cluster provisioning required.

Databricks Cost Structure
Two-Layer Pricing Model
1. Azure VM Compute Costs (what you pay Azure)
2. Databricks Units (DBUs) (what you pay Databricks)

Typical Costs for Streaming Workloads
Small to Medium Streaming Job:
- VM Costs: $200-500/month (Standard_DS3_v2 cluster)
- DBU Costs: $300-800/month (depending on tier and usage)
- Total: $500-1,300/month

Large Streaming Job:
- VM Costs: $800-2,000/month (larger clusters)
- DBU Costs: $1,000-3,000/month
- Total: $1,800-5,000/month

Cost Optimization Strategies for Databricks
1. Cluster Optimization

# Use spot instances (60-90% cost savings)
"azure_attributes.availability": "SPOT_WITH_FALLBACK_AZURE"

# Auto-termination to avoid idle costs
"autotermination_minutes": 30

# Right-size clusters based on workload
"autoscale": {"min_workers": 2, "max_workers": 8}

2. Workload Optimization
- Batch vs Streaming: Use batch processing where real-time isn't critical
- Resource Pooling: Share clusters across multiple workloads
- Delta Lake: Reduce storage costs with compression and optimization
3. Pricing Tier Selection
- Standard: For basic streaming workloads
- Premium: Only if you need advanced security/governance
- Consider Reserved Instances: For predictable workloads

Break-Even Analysis:
When Databricks Becomes Cost-Effective:
You'll likely save money with Databricks if:
- You're running 15+ streaming units in ASA
- You need complex transformations (reducing development time)
- You're already planning ML/advanced analytics initiatives
- You can consolidate multiple ASA jobs into shared Databricks clusters

Recommendation
Start Small: Begin with a pilot migration of your most complex streaming job to Databricks while keeping simple aggregations in ASA.
This hybrid approach lets you:
- Compare actual costs vs projections
- Build team expertise gradually
- Minimize migration risk
- Optimize for the best cost/benefit ratio per workload

[vaibhavs120]

I completely agree with @lingareddy_Alva on the costing part. One small point I would like to mention is We should only enable SPOT instances (60-90% cost savings) in Development/non-critical(PROD) environment. This option works great and is indeed cost effective but not good for mission critical workloads. I used this for one of my daily load and sometimes the process terminates abruptly. @lingareddy_Alva please correct me if I am wrong here.

[lingareddy_Alva]

I agree with you @vaibhavs120 , thanks for bringing this up.