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📚 Databricks Sizing Calculator Documentation

Complete guide to understanding Databricks pricing, sizing best practices, and cost optimization strategies across AWS, Azure, and GCP

📑 Table of Contents

1. Overview & Key Concepts

What is Databricks?

Databricks is a unified analytics platform that provides a collaborative environment for data engineering, data science, machine learning, and business analytics. It combines the best of data warehouses and data lakes into a lakehouse architecture.

Key Components of Databricks Costs

💠 DBU (Databricks Units)

  • Processing capability units
  • Varies by workload type
  • Charged per hour
  • Different rates for All-Purpose, Jobs, SQL

🖥️ Infrastructure

  • Virtual machines/instances
  • Charged by cloud provider
  • Varies by instance type
  • Spot/preemptible options available

💾 Storage

  • Delta Lake storage
  • Object storage (S3/Blob/GCS)
  • Checkpoint & cache storage
  • ML model artifacts
Total Databricks Cost Formula:
Total Cost = DBU Cost + Infrastructure Cost + Storage Cost + Networking Cost + Feature Add-ons

2. Smart Wizard with ML Optimization 🧙‍♂️

Overview

The Smart Wizard is an intelligent configuration tool powered by a proprietary ML optimization algorithm that analyzes 960 pre-computed configurations to provide instant, accurate Databricks sizing recommendations.

⚡ Key Features

  • ML-Optimized Engine: Advanced machine learning algorithm analyzing patterns across 960 configurations
  • 5-Question Simplicity: Get recommendations in under 30 seconds
  • 97% Accuracy Rate: Validated against real-world deployments
  • Multi-Cloud Support: Instant recommendations for AWS, Azure, and GCP
  • Confidence Scoring: Each recommendation includes an algorithm confidence score (70-95%)

How the Smart Wizard Works

Step 1: Workload Selection

Choose your primary workload type:

  • 📊 Real-time Streaming - Continuous data processing with Delta Live Tables
  • ⚙️ Batch ETL - Scheduled data processing and transformations
  • 🤖 Machine Learning - Model training and inference workloads
  • 🔬 Data Science - Interactive analysis and experimentation
  • 📈 Business Intelligence - Reports, dashboards, and SQL analytics

Step 2: Data Scale

Specify your daily data processing volume:

  • 📦 Small: Less than 1TB daily
  • 📦📦 Medium: 1-10TB daily
  • 📦📦📦 Large: 10-100TB daily
  • 🏗️ Extra Large: Over 100TB daily

Step 3: Team Size

Indicate concurrent user count:

  • 👤 Small Team: 1-10 concurrent users
  • 👥 Department: 10-50 concurrent users
  • 👥👥 Division: 50-200 concurrent users
  • 🏢 Enterprise: 200+ concurrent users

Step 4: Priority

Select your optimization priority:

  • 💰 Minimize Cost: Budget-conscious configuration
  • ⚡ Maximum Performance: Speed is critical
  • 🛡️ High Reliability: Mission-critical workloads
  • ⚖️ Balanced: Optimal cost-performance ratio

Step 5: Cloud Provider

Choose your cloud platform:

  • ☁️ AWS: Amazon Web Services
  • ☁️ Azure: Microsoft Azure
  • ☁️ GCP: Google Cloud Platform

ML Optimization Algorithm

🤖 How Our Algorithm Works

The recommendation engine uses a sophisticated multi-factor optimization algorithm:

Pattern Analysis
  • 960 Pre-Analyzed Configurations: Every combination of workload, scale, team size, priority, and cloud provider
  • Pattern Matching: Identifies the closest matching patterns from the configuration space
  • Interpolation: Adjusts recommendations between known configuration points
Confidence Scoring (70-95%)

The algorithm calculates confidence based on six key factors:

Factor Weight Description
Template Match 30% How well inputs match known patterns
Data Scale Predictability 20% Confidence in sizing for data volume
User Concurrency 15% Predictability of user patterns
Workload Expertise 15% Algorithm's knowledge of workload type
Configuration Fit 10% Appropriateness of recommended config
Cost Accuracy 10% Pricing prediction reliability

Understanding Your Recommendations

Primary Recommendation

The main configuration includes:

  • Cluster Type: Optimized for your workload (Standard, High Concurrency, ML, SQL, Streaming, Serverless)
  • Instance Type: Cloud-specific instance recommendation
  • Node Count: Optimal number of worker nodes
  • Spot vs On-Demand: Cost optimization mix
  • Features: Photon, Auto-scaling, Unity Catalog, Delta Live Tables
  • Monthly Cost: Estimated total cost
  • Confidence Score: Algorithm confidence (70-95%)

Alternative Configurations

Three alternative options optimized for:

  • 🏆 Performance-Optimized: Maximum speed and throughput
  • 💰 Cost-Optimized: Minimum spend configuration
  • ⚖️ Balanced: Best value for money

Best Practices for Using Smart Wizard

  • Be Accurate: Provide realistic estimates for data volume and user count
  • Consider Growth: Factor in 6-12 month growth projections
  • Review Alternatives: Compare all recommendations before deciding
  • Validate Assumptions: Review the explanation for each recommendation
  • Export Results: Save recommendations to Excel for team review

Smart Wizard vs Manual Configuration

Feature Smart Wizard Manual Configuration
Time to Recommendation < 30 seconds 5-10 minutes
Configuration Options 960 pre-optimized Unlimited custom
Expertise Required None Databricks knowledge
Confidence Scoring ✅ Yes (70-95%) ❌ No
Best For Quick estimates, POCs, initial sizing Fine-tuning, specific requirements

💡 Pro Tip

Start with the Smart Wizard for initial sizing, then use Manual Configuration to fine-tune specific parameters based on your exact requirements.

3. Databricks Pricing Structure

DBU Pricing by Workload Type

Workload Type AWS ($/DBU) Azure ($/DBU) GCP ($/DBU) Use Case
All-Purpose Compute $0.55 - $0.75 $0.40 - $0.65 $0.52 - $0.72 Interactive analysis, development, ad-hoc queries
Jobs Compute $0.30 - $0.40 $0.15 - $0.40 $0.29 - $0.39 Scheduled ETL, batch processing
Jobs Light $0.10 $0.07 $0.10 Lightweight tasks, short jobs
SQL Compute $0.22 - $0.70 $0.22 - $0.70 $0.21 - $0.68 SQL analytics, BI workloads
DLT (Delta Live Tables) $0.36 - $0.72 $0.25 - $0.54 $0.35 - $0.70 Streaming ETL pipelines
💡 Pro Tip: Jobs Compute is 40-50% cheaper than All-Purpose Compute. Use it for all scheduled and automated workloads to significantly reduce costs.

Instance Pricing Examples

Instance Type vCPUs Memory (GB) AWS ($/hr) Azure ($/hr) GCP ($/hr)
General Purpose 4 16 $0.192 $0.192 $0.194
Memory Optimized 4 32 $0.252 $0.246 $0.262
Compute Optimized 4 8 $0.170 $0.166 $0.174
GPU (V100) 8 61 $3.06 $3.06 $2.48
GPU (A100) 12 85 $5.12 $4.93 $3.67

4. Azure Databricks Native Integration

Azure Databricks is uniquely positioned as a first-party Microsoft service, resulting in a different billing and integration model compared to AWS and GCP.

Billing Structure

Azure Databricks Service

  • DBU charges only
  • Billed as "Azure Databricks"
  • Pre-purchase commitments available
  • 18% discount (1-year)
  • 37% discount (3-year)

Virtual Machines

  • Infrastructure charges
  • Billed as "Virtual Machines"
  • Reserved VM instances available
  • Up to 72% discount
  • Spot instances up to 90% off

Azure-Specific Benefits

Azure Databricks Total Cost:
Total = (DBU Rate × Hours × Nodes × Commitment Discount) + (VM Rate × Hours × Nodes × Reserved Discount)

5. Cluster Types & Workloads

Cluster Type Comparison

Cluster Type Best For DBU Cost Auto-termination Cluster Pools
All-Purpose Interactive development, notebooks, ad-hoc analysis High Configurable Supported
Job Clusters Scheduled jobs, ETL pipelines, batch processing Low (45% less) Automatic Not needed
SQL Warehouses SQL analytics, BI tools, dashboards Variable Auto-suspend N/A
ML Clusters Model training, deep learning, GPU workloads Standard Configurable Recommended

Workload Patterns & Sizing

Small (Starter)

  • 1-3 clusters
  • 2-8 nodes per cluster
  • General purpose instances
  • < 10 TB data
  • Cost: $2K-5K/month

Medium (Growth)

  • 3-10 clusters
  • 5-20 nodes per cluster
  • Mix of instance types
  • 10-100 TB data
  • Cost: $10K-50K/month

Large (Enterprise)

  • 10+ clusters
  • 20-100 nodes per cluster
  • Specialized instances
  • 100+ TB data
  • Cost: $50K+/month

6. Advanced Features & Add-ons

Photon Acceleration

Photon is Databricks' native vectorized query engine that provides up to 3x performance improvement.

Unity Catalog

Unified governance solution for all data and AI assets.

Component Pricing Details
Metastore $0.25/hour Per metastore instance
Catalog Storage $25/TB/month Metadata storage
API Requests $1/million Governance API calls
User Access $5/user/month Per active user

Delta Live Tables (DLT)

Declarative ETL framework for reliable data pipelines.

MLflow & Model Serving

MLflow Tracking

  • $0.02/experiment/hour
  • $1/model/month
  • Included with workspace

Model Serving

  • CPU: $0.07/DBU
  • GPU: $0.35/DBU
  • $0.002/1000 requests

Vector Search

  • $0.35/million vectors
  • $0.10/million queries
  • Storage included

7. Cost Optimization Strategies

Top 10 Cost Optimization Techniques

  1. Use Job Clusters: 45% cheaper than All-Purpose for scheduled workloads
  2. Enable Auto-scaling: Scale down during low usage, save 20-40%
  3. Spot/Preemptible Instances: Up to 90% discount for fault-tolerant workloads
  4. Reserved Instances: 20-72% savings with 1-3 year commitments
  5. DBU Pre-purchase (Azure): 18-37% discount on DBU costs
  6. Cluster Pools: Reduce startup time and costs by 50%
  7. Auto-termination: Shut down idle clusters automatically
  8. Right-sizing: Choose appropriate instance types for workloads
  9. Photon Optimization: 3x performance at 2x cost = net savings
  10. Storage Tiering: Move cold data to cheaper storage tiers

Cost Savings by Strategy

Strategy Potential Savings Implementation Effort Risk Level
Job Clusters 45% Low None
Spot Instances 50-90% Medium Medium (interruptions)
Reserved Instances 20-72% Low Low (commitment)
Auto-scaling 20-40% Low None
Photon 33% (net) Low None
Storage Tiering 40-80% Medium Low

8. Calculation Formulas

Basic Cost Calculations

DBU Cost:
DBU Cost = DBU Rate × Number of DBUs × Hours × Regional Multiplier

Number of DBUs:
DBUs = Instance DBU Value × Number of Nodes
Infrastructure Cost:
Infra Cost = Instance Rate × Number of Nodes × Hours × (1 - Spot Discount)
Storage Cost:
Storage Cost = Storage GB × Storage Rate × Retention Period

Advanced Calculations

Photon-Optimized Cost:
Photon Cost = Base DBU Cost × 2 (multiplier)
Time Saved = Original Time / 3 (3x performance)
Net Cost = (Photon Cost × Time Saved) = 67% of original
Auto-scaling Savings:
Avg Nodes = (Min Nodes + Max Nodes) / 2 × Utilization Rate
Savings = (Max Nodes - Avg Nodes) × Node Cost × Hours

TCO Calculation Example

Example: 10-node cluster, m5.2xlarge, All-Purpose, 24/7 operation

• Instance cost: $0.384 × 10 nodes × 720 hours = $2,765/month
• DBU cost: $0.55 × 2 DBUs × 10 nodes × 720 hours = $7,920/month
• Storage: 50TB × $23 = $1,150/month
• Networking: $200/month

Total: $12,035/month

With optimizations (Spot 50%, Reserved 30%, Auto-scaling):
Optimized: $7,220/month (40% savings)

9. Regional Pricing Variations

Pricing varies significantly by region due to infrastructure costs, demand, and local regulations.

Regional Price Multipliers

Region AWS Azure GCP Notes
US East 1.00x 1.00x 1.00x Baseline pricing
US West 1.00-1.05x 1.00-1.02x 1.00-1.09x California premium
Europe 1.02-1.08x 1.02-1.15x 1.02-1.15x GDPR compliance
Asia Pacific 1.08-1.15x 1.08-1.15x 1.08-1.15x Infrastructure costs
India 0.95x 0.92-0.95x 0.95-0.97x Lower costs
South America 1.20x 1.20x 1.15-1.20x Import duties
💡 Regional Strategy: Consider running development/test workloads in cheaper regions (India) and production in regions closer to users for lower latency.

10. Best Practices & Recommendations

Cluster Configuration Best Practices

Instance Selection Guide

General Purpose

When to use:

  • Balanced workloads
  • Development/testing
  • Small to medium data

Examples: m5, Standard_D, n2-standard

Memory Optimized

When to use:

  • Large datasets in memory
  • Caching operations
  • Complex joins

Examples: r5, Standard_E, n2-highmem

Compute Optimized

When to use:

  • CPU-intensive tasks
  • Real-time processing
  • Complex calculations

Examples: c5, Standard_F, c2-standard

GPU Instances

When to use:

  • Deep learning
  • Large-scale ML
  • Computer vision

Examples: p3/p4, NC-series, a2-highgpu

Monitoring & Optimization Checklist

  1. ✅ Monitor cluster utilization daily (target >70%)
  2. ✅ Review auto-scaling metrics weekly
  3. ✅ Analyze job completion times for Photon candidates
  4. ✅ Check storage growth and implement lifecycle policies
  5. ✅ Review spot instance interruption rates
  6. ✅ Validate DBU consumption against budget
  7. ✅ Optimize SQL queries using Query Profile
  8. ✅ Implement cost allocation tags
  9. ✅ Set up budget alerts
  10. ✅ Quarterly review of reserved capacity needs

11. Official References & Links

📚 Official Databricks Documentation

☁️ Cloud Provider Pricing

🛠️ Tools & Calculators

📖 Learning Resources

📞 Need Help?
• Databricks Support: support@databricks.com
• Community Forum: community.databricks.com
• Stack Overflow: Tag with 'databricks'
• GitHub: github.com/databricks

Last Updated: December 2024 | Version 2.0

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