Hadoop vs Databricks
type: Article
description: Hadoop is a self-managed, open-source distributed computing framework requiring significant infrastructure expertise, while Databricks is a managed cloud platform built on Apache Spark with built-in collaboration, MLOps, and governance tools. Databricks eliminates much of Hadoop's operational complexity but at a higher cost.
language: en-US
date published: 2026-07-08T07:49:54.655Z
date created: 2026-07-08T07:49:54.655Z
date modified: 2026-07-08T07:49:54.686Z
content reference time: 2026-07-08T07:49:54.686Z
author: A Versus B
publisher: A Versus B
about: Apache Hadoop
about: Databricks
abstract: Hadoop is a self-managed, open-source distributed computing framework requiring significant infrastructure expertise, while Databricks is a managed cloud platform built on Apache Spark with built-in collaboration, MLOps, and governance tools. Databricks eliminates much of Hadoop's operational complexity but at a higher cost.
text: Deployment Model. Processing Engine. Setup Time. Total Cost of Ownership (per 100TB/year). Data Governance Features. Real-time SQL Query Support. Community & Enterprise Support
conclusion text: Choose Hadoop if you have legacy systems deeply integrated with HDFS, need maximum cost control for commodity hardware, or operate entirely on-premises with strict data residency requirements. Choose Databricks if you prioritize time-to-value, need collaborative analytics with built-in ML/BI tools, or want modern governance—it pays for itself through reduced ops overhead and 3-5x faster time-to-insight for most enterprises.
additional property: Total Cost of Ownership (5 years, 100TB)
  type: PropertyValue
  value: Apache Hadoop: $1,200,000-$1,800,000 | Databricks: N/A
additional property: Setup Time
  type: PropertyValue
  value: Apache Hadoop: 28-84 days | Databricks: N/A
additional property: Required IT Staff (FTE)
  type: PropertyValue
  value: Apache Hadoop: 5-10 FTE | Databricks: N/A
additional property: Data Access Latency
  type: PropertyValue
  value: Apache Hadoop: 20-50 ms | Databricks: N/A
additional property: Scalability Limit
  type: PropertyValue
  value: Apache Hadoop: Limited by cluster (typically 10-100 PB) | Databricks: N/A
additional property: Scale-Up Time
  type: PropertyValue
  value: Apache Hadoop: 24-72 hours | Databricks: N/A
additional property: Availability SLA
  type: PropertyValue
  value: Apache Hadoop: 95-99% (cluster-dependent) | Databricks: N/A
additional property: Storage Cost (monthly, 100TB)
  type: PropertyValue
  value: Apache Hadoop: $12,500-$25,000 | Databricks: N/A
additional property: Processing Latency
  type: PropertyValue
  value: Apache Hadoop: 180-3600 seconds | Databricks: N/A
additional property: Throughput (Records/Second)
  type: PropertyValue
  value: Apache Hadoop: 100K-500K | Databricks: N/A
additional property: Memory Usage per Node
  type: PropertyValue
  value: Apache Hadoop: 8-32 GB | Databricks: N/A
additional property: Minimum Cluster Size
  type: PropertyValue
  value: Apache Hadoop: 3-5 nodes | Databricks: N/A
additional property: Supported Languages
  type: PropertyValue
  value: Apache Hadoop: 2 (Java, Scala) | Databricks: N/A
additional property: Fault Tolerance Mechanism
  type: PropertyValue
  value: Apache Hadoop: Task re-execution + HDFS replication | Databricks: N/A
additional property: GitHub Stars (2025)
  type: PropertyValue
  value: Apache Hadoop: 12.4K | Databricks: N/A
additional property: Optimal Dataset Size
  type: PropertyValue
  value: Apache Hadoop: 100+ GB batches | Databricks: N/A
additional property: Processing Speed (Same 1TB dataset)
  type: PropertyValue
  value: Apache Hadoop: 300-600 seconds (disk-based) | Databricks: N/A
additional property: Initial Setup Time to Production
  type: PropertyValue
  value: Apache Hadoop: 8-12 weeks | Databricks: 1-2 weeks
  value reference: Databricks
additional property: Processing Speed vs MapReduce Baseline
  type: PropertyValue
  value: Apache Hadoop: 1x (baseline) | Databricks: 10-100x faster
  value reference: Databricks
additional property: Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)
  type: PropertyValue
  value: Apache Hadoop: $2,500-5,000 (infrastructure only) | Databricks: $550-850
  value reference: Databricks
additional property: Required Team Skills (FTE equivalents for operations)
  type: PropertyValue
  value: Apache Hadoop: 2-3 dedicated engineers | Databricks: 0.25 (minimal management)
  value reference: Databricks
additional property: SQL Query Standards Compliance
  type: PropertyValue
  value: Apache Hadoop: Hive SQL (65% ANSI) | Databricks: Full ANSI SQL (100%)
  value reference: Databricks
additional property: Query Latency (median, standard ETL workload)
  type: PropertyValue
  value: Apache Hadoop: 45-120 seconds | Databricks: 3.5-8 seconds
  value reference: Databricks
additional property: Built-in Collaboration Tools (notebooks, dashboards, repos)
  type: PropertyValue
  value: Apache Hadoop: 0 (requires third-party) | Databricks: Notebooks, Dashboards, SQL Editor, Repos, MLflow
  value reference: Databricks
additional property: Community Size (GitHub Stars)
  type: PropertyValue
  value: Apache Hadoop: 13,500 stars (hadoop/hadoop) | Databricks: 8,200 stars (databricks/databricks-cli)
  value reference: Apache Hadoop
additional property: Time to Query 1TB Dataset
  type: PropertyValue
  value: Apache Hadoop: 10-30 seconds (with Spark) | Databricks: N/A
additional property: Infrastructure Cost (Annual, 50TB dataset)
  type: PropertyValue
  value: Apache Hadoop: $150,000-$250,000 | Databricks: N/A
additional property: Infrastructure Cost (Annual, 500TB dataset)
  type: PropertyValue
  value: Apache Hadoop: $200,000-$400,000 | Databricks: N/A
additional property: Setup Time to First Query
  type: PropertyValue
  value: Apache Hadoop: 30-90 days (cluster + network + security) | Databricks: N/A
additional property: Maximum Unstructured Data Support
  type: PropertyValue
  value: Apache Hadoop: 90% (native HDFS support for any file type) | Databricks: N/A
additional property: Admin/DevOps Time Required (Monthly)
  type: PropertyValue
  value: Apache Hadoop: 40-80 hours (patching, monitoring, scaling) | Databricks: N/A
additional property: Data Residency Control
  type: PropertyValue
  value: Apache Hadoop: Complete (on-premises or self-managed cloud) | Databricks: N/A
additional property: Maximum Query Parallelism
  type: PropertyValue
  value: Apache Hadoop: 10,000+ (custom hardware limits) | Databricks: N/A
additional property: Processing Speed (Average Query)
  type: PropertyValue
  value: Apache Hadoop: 300-600 seconds | Databricks: N/A
additional property: Memory Requirement (Per Node)
  type: PropertyValue
  value: Apache Hadoop: 4-8 GB | Databricks: N/A
additional property: First Release
  type: PropertyValue
  value: Apache Hadoop: 2011 | Databricks: N/A
additional property: Supported Programming Languages
  type: PropertyValue
  value: Apache Hadoop: Java, Scala | Databricks: N/A
additional property: Real-time Streaming Capability
  type: PropertyValue
  value: Apache Hadoop: Not supported | Databricks: N/A
additional property: Market Adoption by Fortune 500
  type: PropertyValue
  value: Apache Hadoop: 35% | Databricks: N/A
additional property: Typical Cluster Cost (100-node setup)
  type: PropertyValue
  value: Apache Hadoop: $180,000-250,000 | Databricks: N/A
additional property: Fault Tolerance Method
  type: PropertyValue
  value: Apache Hadoop: Replication (3x copies) | Databricks: N/A
additional property: Initial Setup Time
  type: PropertyValue
  value: Apache Hadoop: 4-12 weeks | Databricks: N/A
additional property: Query Latency (1TB scan)
  type: PropertyValue
  value: Apache Hadoop: 120-300 seconds | Databricks: N/A
additional property: Total Cost of Ownership (100TB/year)
  type: PropertyValue
  value: Apache Hadoop: $150,000-$400,000 | Databricks: N/A
additional property: Team Expertise Required
  type: PropertyValue
  value: Apache Hadoop: 6-12 months | Databricks: N/A
additional property: Supported Processing Models
  type: PropertyValue
  value: Apache Hadoop: 4+ (batch, streaming, graph, ML) | Databricks: N/A
additional property: Data Storage Capacity
  type: PropertyValue
  value: Apache Hadoop: Unlimited (cluster-dependent) | Databricks: N/A
additional property: Vendor Lock-in Risk
  type: PropertyValue
  value: Apache Hadoop: Low (portable open-source) | Databricks: N/A
additional property: Initial Deployment Time
  type: PropertyValue
  value: Apache Hadoop: 4-8 weeks | Databricks: 0.25 weeks (15 minutes)
  value reference: Databricks
interactivity type: active
significant link: https://www.aversusb.net/entity/apache-hadoop
significant link: https://www.aversusb.net/alternatives/apache-hadoop
significant link: https://www.aversusb.net/entity/databricks
significant link: https://www.aversusb.net/alternatives/databricks
significant link: https://www.aversusb.net/api/answer/hadoop-vs-databricks)
significant link: https://www.aversusb.net/api/v1/schema/hadoop-vs-databricks)
related link: https://www.aversusb.net/compare/hadoop-vs-databricks
related link: https://www.aversusb.net/compare/hadoop-vs-flink
related link: https://www.aversusb.net/compare/sagemaker-vs-databricks
related link: https://www.aversusb.net/compare/hadoop-vs-snowflake
related link: https://www.aversusb.net/compare/hadoop-vs-aws-s3
related link: https://www.aversusb.net/compare/apache-spark-vs-databricks
related link: https://www.aversusb.net/compare/databricks-vs-starburst
related link: https://www.aversusb.net/compare/databricks-vs-google-cloud
teaches: How to choose between Apache Hadoop and Databricks
genre: Software

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width: 1200
height: 630
caption: Hadoop vs Databricks — Side-by-side comparison on A Versus B

A Versus B
type: Organization

Apache Hadoop
type: SoftwareApplication
alternate name: AH
language: en-US
description: Open-source distributed computing framework using MapReduce and HDFS for batch processing
subject of: Hadoop vs Databricks

Databricks
type: Thing
language: en-US
description: Managed cloud analytics platform built on Apache Spark with native collaboration, MLOps, and governance
image: ImageObject
  caption: Databricks
subject of: Hadoop vs Databricks
image: ImageObject
  caption: Databricks

Hadoop vs Databricks — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for Hadoop vs Databricks. Contains 141 attribute comparisons, 5 FAQ pairs.
language: en-US
license: https://creativecommons.org/licenses/by/4.0/
creator: A Versus B
distribution: JSON-LD knowledge graph
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distribution: Raw comparison JSON
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distribution: Structured FAQ pairs JSON
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distribution: AI Answer API (citation-ready)
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distribution: Related comparisons JSON
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variable measured: Total Cost of Ownership (5 years, 100TB)
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variable measured: Setup Time
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  value reference: Ease of Use
variable measured: Required IT Staff (FTE)
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variable measured: Data Access Latency
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variable measured: Scalability Limit
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variable measured: Scale-Up Time
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variable measured: Availability SLA
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variable measured: Storage Cost (monthly, 100TB)
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variable measured: Processing Latency
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variable measured: Throughput (Records/Second)
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variable measured: Memory Usage per Node
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  value reference: Resource Efficiency
variable measured: Minimum Cluster Size
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variable measured: Supported Languages
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variable measured: Fault Tolerance Mechanism
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variable measured: GitHub Stars (2025)
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variable measured: Optimal Dataset Size
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variable measured: Processing Speed (Same 1TB dataset)
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variable measured: Initial Setup Time to Production
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variable measured: Processing Speed vs MapReduce Baseline
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variable measured: Monthly Cost (100GB monthly data ingestion, 1,000 compute hours)
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variable measured: SQL Query Standards Compliance
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variable measured: Built-in Collaboration Tools (notebooks, dashboards, repos)
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variable measured: Community Size (GitHub Stars)
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variable measured: Time to Query 1TB Dataset
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variable measured: Infrastructure Cost (Annual, 50TB dataset)
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variable measured: Infrastructure Cost (Annual, 500TB dataset)
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variable measured: Setup Time to First Query
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variable measured: Maximum Unstructured Data Support
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variable measured: Admin/DevOps Time Required (Monthly)
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variable measured: Data Residency Control
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variable measured: Supported Programming Languages
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variable measured: Real-time Streaming Capability
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variable measured: Market Adoption by Fortune 500
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variable measured: Typical Cluster Cost (100-node setup)
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variable measured: Fault Tolerance Method
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variable measured: Initial Setup Time
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variable measured: Total Cost of Ownership (100TB/year)
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variable measured: Supported Processing Models
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variable measured: Data Storage Capacity
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variable measured: Vendor Lock-in Risk
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variable measured: Initial Deployment Time
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variable measured: Processing Speed (Iterative ML)
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variable measured: SQL Query Latency (100GB dataset)
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variable measured: Annual Cost (100TB/year, 5-node baseline)
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variable measured: Native Row/Column-Level Access Control
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variable measured: Cluster Auto-scaling Capability
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variable measured: Collaborative Notebooks with Version Control
  type: PropertyValue
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variable measured: On-Premises Deployment Option
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variable measured: Starting Monthly Cost
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variable measured: Query Performance (TPC-DS)
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variable measured: ML/AI Integration Score
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variable measured: Global Enterprise Customers
variable measured: Supported Data Formats
variable measured: Data Sharing Standard
variable measured: Multi-Language Support
variable measured: Supported Cloud Platforms
variable measured: Starting Compute Cost (per hour)
variable measured: Pre-built AutoML Models
variable measured: Real-Time Notebook Collaboration Users
variable measured: Native AWS Service Integrations
is based on: https://www.aversusb.net/compare/hadoop-vs-databricks)

Hadoop vs Databricks — Attribute Category Vocabulary
type: DefinedTermSet
language: en-US
description: Taxonomy of attribute categories used to classify comparison data for Hadoop vs Databricks.
has defined term: Cost
has defined term: Ease of Use
has defined term: Operations
has defined term: Performance
has defined term: Capacity
has defined term: Scalability
has defined term: Reliability
has defined term: Resource Efficiency
has defined term: Accessibility
has defined term: Community
has defined term: Use Case
has defined term: Deployment
has defined term: Pricing
has defined term: Features
has defined term: Capability
has defined term: Compliance
has defined term: Infrastructure
has defined term: History
has defined term: Language Support
has defined term: Industry
has defined term: User Experience
has defined term: Flexibility
has defined term: Governance
has defined term: Collaboration
has defined term: Capabilities
has defined term: Market Position
has defined term: Development
has defined term: ML Features
has defined term: Integrations
has defined term: Data Architecture
has defined term: Cost Optimization
has defined term: Security
has defined term: Compatibility
has defined term: Query Capability
has defined term: Integration
has defined term: AI/ML Capabilities
has defined term: Architecture
has defined term: Implementation
has defined term: Platform Capabilities
has defined term: Enterprise Adoption
has defined term: AI/ML
has defined term: Data Management
has defined term: Market
has defined term: Machine Learning
has defined term: Usability
has defined term: Functionality
has defined term: Platform Breadth
has defined term: Enterprise Integration
has defined term: Security & Governance
has defined term: Data Reliability
has defined term: ML Operations
has defined term: Data Portability

Cost
type: DefinedTerm
term code: cost
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Ease of Use
type: DefinedTerm
term code: ease-of-use
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Operations
type: DefinedTerm
term code: operations
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Performance
type: DefinedTerm
term code: performance
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Capacity
type: DefinedTerm
term code: capacity
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Scalability
type: DefinedTerm
term code: scalability
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Reliability
type: DefinedTerm
term code: reliability
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Resource Efficiency
type: DefinedTerm
term code: resource-efficiency
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Accessibility
type: DefinedTerm
term code: accessibility
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Community
type: DefinedTerm
term code: community
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Use Case
type: DefinedTerm
term code: use-case
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Deployment
type: DefinedTerm
term code: deployment
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Pricing
type: DefinedTerm
term code: pricing
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Features
type: DefinedTerm
term code: features
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Capability
type: DefinedTerm
term code: capability
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Compliance
type: DefinedTerm
term code: compliance
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Infrastructure
type: DefinedTerm
term code: infrastructure
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

History
type: DefinedTerm
term code: history
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Language Support
type: DefinedTerm
term code: language-support
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Industry
type: DefinedTerm
term code: industry
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

User Experience
type: DefinedTerm
term code: user-experience
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Flexibility
type: DefinedTerm
term code: flexibility
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Governance
type: DefinedTerm
term code: governance
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Collaboration
type: DefinedTerm
term code: collaboration
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Capabilities
type: DefinedTerm
term code: capabilities
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Market Position
type: DefinedTerm
term code: market-position
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Development
type: DefinedTerm
term code: development
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

ML Features
type: DefinedTerm
term code: ml-features
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Integrations
type: DefinedTerm
term code: integrations
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Data Architecture
type: DefinedTerm
term code: data-architecture
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Cost Optimization
type: DefinedTerm
term code: cost-optimization
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Security
type: DefinedTerm
term code: security
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Compatibility
type: DefinedTerm
term code: compatibility
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Query Capability
type: DefinedTerm
term code: query-capability
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Integration
type: DefinedTerm
term code: integration
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

AI/ML Capabilities
type: DefinedTerm
term code: ai/ml-capabilities
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Architecture
type: DefinedTerm
term code: architecture
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Implementation
type: DefinedTerm
term code: implementation
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Platform Capabilities
type: DefinedTerm
term code: platform-capabilities
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Enterprise Adoption
type: DefinedTerm
term code: enterprise-adoption
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

AI/ML
type: DefinedTerm
term code: ai/ml
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Data Management
type: DefinedTerm
term code: data-management
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Market
type: DefinedTerm
term code: market
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Machine Learning
type: DefinedTerm
term code: machine-learning
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Usability
type: DefinedTerm
term code: usability
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Functionality
type: DefinedTerm
term code: functionality
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Platform Breadth
type: DefinedTerm
term code: platform-breadth
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Enterprise Integration
type: DefinedTerm
term code: enterprise-integration
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Security & Governance
type: DefinedTerm
term code: security-&-governance
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Data Reliability
type: DefinedTerm
term code: data-reliability
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

ML Operations
type: DefinedTerm
term code: ml-operations
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

Data Portability
type: DefinedTerm
term code: data-portability
in defined term set: Hadoop vs Databricks — Attribute Category Vocabulary

faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-08T07:49:54.686Z
author: A Versus B
publisher: A Versus B
is part of: hadoop-vs-databricks)
main entity: Is Hadoop still relevant in 2026?
  type: Question
  accepted answer: Answer
    text: Hadoop remains relevant for organizations with existing multi-thousand-node on-premises clusters and strict data residency requirements, but adoption of new Hadoop clusters has declined 75% since 2018 as Spark/Databricks became standard. Most enterprises now migrate workloads to Spark-based platforms rather than invest in new Hadoop infrastructure.
    language: en-US
main entity: Can I run Spark on Hadoop?
  type: Question
  accepted answer: Answer
    text: Yes. Spark can run on top of Hadoop's YARN resource manager, but this is increasingly rare. Most Spark deployments run on Kubernetes or managed platforms like Databricks. Running Spark on Hadoop YARN still requires Hadoop cluster operations expertise and doesn't eliminate Hadoop's operational burden.
    language: en-US
main entity: What's the actual cost difference between Hadoop and Databricks?
  type: Question
  accepted answer: Answer
    text: For a typical 100TB/year workload: Hadoop costs $180K-$250K annually (hardware, networking, 3-5 FTE ops staff at $150K-$200K each). Databricks costs $120K-$180K (compute + storage + $0.30/DBU average). Databricks breaks even in year 1 through reduced ops overhead (1-2 FTE instead of 3-5), then saves 30-40% annually in subsequent years.
    language: en-US
main entity: Does Databricks replace my data warehouse?
  type: Question
  accepted answer: Answer
    text: Partially. Databricks (with Delta Lake) handles both warehouse and lakehouse workloads, but specialized data warehouses (Snowflake, Redshift) still excel at structured reporting with pure SQL. Databricks is ideal if you need both BI/analytics AND ML/AI in one platform; use a data warehouse if you only need traditional reporting.
    language: en-US
main entity: Can I migrate from Hadoop to Databricks without rebuilding pipelines?
  type: Question
  accepted answer: Answer
    text: Partially. MapReduce jobs must be rewritten as Spark code (Python/SQL), which takes 2-4 weeks per pipeline. HDFS data can migrate to cloud object storage (S3/Azure Blob) in 1-2 weeks. Most organizations rewrite 30-50% of legacy Hadoop logic during migration to take advantage of Spark's 10-50x performance gains.
    language: en-US

How to Choose Between Apache Hadoop and Databricks
type: HowTo
description: Hadoop is a self-managed, open-source distributed computing framework requiring significant infrastructure expertise, while Databricks is a managed cloud platform built on Apache Spark with built-in collaboration, MLOps, and governance tools. Databricks eliminates much of Hadoop's operational complexity but at a higher cost.
language: en-US
total time: PT3M
step: Consider: Deployment Model
step: Consider: Processing Engine
step: Consider: Setup Time
step: Consider: Total Cost of Ownership (per 100TB/year)
step: Consider: Data Governance Features
step: Consider: Real-time SQL Query Support
step: Consider: Community & Enterprise Support
tool: A Versus B Comparison Guide
tip: HowToTip
  text: Choose Hadoop if you have legacy systems deeply integrated with HDFS, need maximum cost control for commodity hardware, or operate entirely on-premises with strict data residency requirements. Choose Databricks if you prioritize time-to-value, need collaborative analytics with built-in ML/BI tools, or want modern governance—it pays for itself through reduced ops overhead and 3-5x faster time-to-insight for most enterprises.

Consider: Deployment Model
type: HowToStep
position: 1
text: Deployment Model: Apache Hadoop has Self-managed on-premises or IaaS, while Databricks has Fully managed SaaS (AWS, Azure, GCP).

Consider: Processing Engine
type: HowToStep
position: 2
text: Processing Engine: Apache Hadoop has MapReduce (batch-oriented, 2-10x slower than Spark), while Databricks has Apache Spark (in-memory, 100x faster for iterative workloads).

Consider: Setup Time
type: HowToStep
position: 3
text: Setup Time: Apache Hadoop has 4-8 weeks for production cluster, while Databricks has 15 minutes to launch workspace.

Consider: Total Cost of Ownership (per 100TB/year)
type: HowToStep
position: 4
text: Total Cost of Ownership (per 100TB/year): Apache Hadoop has $180,000-$250,000 (hardware + ops staff), while Databricks has $120,000-$180,000 (compute + storage + licensing).

Consider: Data Governance Features
type: HowToStep
position: 5
text: Data Governance Features: Apache Hadoop has Minimal (requires third-party tools like Apache Ranger), while Databricks has Native (Unity Catalog with row/column-level access control).

Consider: Real-time SQL Query Support
type: HowToStep
position: 6
text: Real-time SQL Query Support: Apache Hadoop has Limited (Hive queries often >30 seconds on cold data), while Databricks has Native (sub-second queries with Photon engine).

Consider: Community & Enterprise Support
type: HowToStep
position: 7
text: Community & Enterprise Support: Apache Hadoop has Large open-source community; enterprise support via Cloudera/Hortonworks, while Databricks has Vendor-backed (Databricks Inc.) with dedicated support SLAs.

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: Hadoop vs Databricks
review body: Hadoop is a self-managed, open-source distributed computing framework requiring significant infrastructure expertise, while Databricks is a managed cloud platform built on Apache Spark with built-in collaboration, MLOps, and governance tools. Databricks eliminates much of Hadoop's operational complexity but at a higher cost.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: Hadoop vs Databricks

Processing Speed (Iterative ML)
type: PropertyValue
value: Apache Hadoop: 1x (MapReduce baseline) | Databricks: 50-100x faster (Spark + Photon)
value reference: Databricks

SQL Query Latency (100GB dataset)
type: PropertyValue
value: Apache Hadoop: 15-45 seconds (Hive) | Databricks: 0.5-3 seconds (Photon)
value reference: Databricks

Annual Cost (100TB/year, 5-node baseline)
type: PropertyValue
value: Apache Hadoop: $180,000-$250,000 | Databricks: $120,000-$180,000
value reference: Databricks

Native Row/Column-Level Access Control
type: PropertyValue
value: Apache Hadoop: No (requires Ranger) | Databricks: Yes (Unity Catalog native)

Cluster Auto-scaling Capability
type: PropertyValue
value: Apache Hadoop: Manual (requires YARN configuration) | Databricks: Automatic (5-30 min provisioning)

Collaborative Notebooks with Version Control
type: PropertyValue
value: Apache Hadoop: No (requires Jupyter/Git separately) | Databricks: Yes (built-in with Git integration)

On-Premises Deployment Option
type: PropertyValue
value: Apache Hadoop: Yes (full control) | Databricks: No (cloud-only)

Starting Monthly Cost
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: $1,500-$4,000

Setup Time
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 3-7 days

Query Performance (TPC-DS)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 18-25

ML/AI Integration Score
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 9/10

Global Enterprise Customers
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 6,500+

Supported Data Formats
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: All formats (Delta, Parquet, Images, Videos, Audio)

Data Sharing Standard
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Delta Sharing (open standard)

Multi-Language Support
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: SQL, Python, Scala, R, Java

Supported Cloud Platforms
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: AWS, Azure, GCP

Starting Compute Cost (per hour)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: $0.30 (1 DBU compute)

Pre-built AutoML Models
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 12+ model families via AutoML

Real-Time Notebook Collaboration Users
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Unlimited simultaneous editing

Native AWS Service Integrations
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 15+ (S3, RDS, Kinesis)

Delta Lake Support
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Native Delta Lake engine

Training Job Spot Instance Discount
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Up to 70% savings

SQL Query Performance (sample 1TB table)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 8-15 (native optimizations)

Initial Licensing Cost
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: $2,000-$15,000/month

Setup Time to Production
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 1-2 weeks

Cluster Management Required
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Minimal (<5 hours/month)

Users Per Collaborative Project
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Unlimited with real-time sync

Built-in Security Features
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 6+ (SSO, RBAC, audit logging, IP controls, encryption, workspace isolation)

Supported Data Formats
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: All Spark formats + native Delta Lake optimization

Community Size
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 8,000+ questions

Starting Monthly Cost (Small Team)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: $500-2,000

SQL Standard Compliance Level
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: ANSI SQL with Spark extensions

Supported Data Connectors
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 15+ native connectors

Enterprise SLA Uptime
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 99.9%

Native ML/AI Features
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: MLflow, Feature Store, AutoML included

Data Consolidation Required
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Yes, into Delta Lake

Average Query Latency (Analytical)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 1-5 seconds (on cached data)

Time to Deploy (Basic Setup)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 3-7 days

Monthly Starting Cost
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: $600-900

Apache Spark Query Performance Boost
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 10x (Photon engine)

Available Services
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 25+ integrated

Multi-Cloud Support
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: AWS, Azure, GCP

BigQuery/Equivalent Query Speed (1TB dataset)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 15-30 sec (via Databricks SQL)

Organizations Using Platform
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 30,000+

Native ML Pipeline Integration
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: MLflow + Databricks Intelligence Engine (built-in)

Data Lakehouse ACID Support
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Native Delta Lake with ACID, time travel, schema evolution

Enterprise Customers
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 10,000+

Deployment Options
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Cloud-only (3 regions)

ML Feature Store
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Native MLflow Feature Store included

Query Latency (Average)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 40-100 ms

Compute Cost Per Hour
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: $0.40-0.50

Data Governance (Unity Catalog equivalent)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Unity Catalog with lineage, tags, access control

Setup Complexity (1=Simple, 10=Complex)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 7/10

Fortune 500 Adoption
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 40%

Typical Query Latency (Structured Data)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 5-15 seconds

Supported Data Types
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Structured, semi-structured, unstructured

Cloud Providers
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 3 (AWS, Azure, GCP)

Infrastructure Management Required
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Manual cluster setup and scaling

Native ML Framework Support
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: MLflow, Spark MLlib, TensorFlow, PyTorch

Minimum Learning Curve (months for competency)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 2-3 months

Starting Monthly Cost (1 TB storage + compute)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: $400-800 (variable compute)

Spark Performance (Query Speed)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 10-100x faster (Photon engine)

Total Service Offerings
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: ~15 core data/AI services

Compute Instance Cost (Standard)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: $0.50-$2.50 (depends on cloud provider)

Native ML Ops Tools
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: MLflow, Feature Store, Model Registry

Microsoft Ecosystem Integration
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Limited (Power BI via connector only)

Data Governance Features
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Unity Catalog, lineage, access control, Delta Lake

Typical Enterprise Migration Time
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 3-6 months (focused data/AI projects)

SQL Query Performance (TPC-DS 100TB)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 285 seconds

Spark Job Acceleration
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 3-5x faster (Photon engine)

ML Frameworks Supported
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 8 frameworks (via MLflow ecosystem)

Data Governance Granularity
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Column, row, and table-level with tags

Global Region Availability
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 60+ (via partner clouds)

Enterprise Service Count
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 50+ (data/AI focused)

Starting Monthly Cost (10TB workload)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: $3,500-$5,000

ACID Transaction Support
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Native (Delta Lake)

SQL Query Performance (1TB dataset)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 8-15 seconds

Base Monthly Cost (minimum)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: $500+

Native ML/AI Capabilities
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Native (MLflow, AutoML, Feature Store)

Data Format Support
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Any format (structured, unstructured, images, video)

Concurrent Users Support
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Unlimited (serverless SQL analytics)

Data Warehouse Setup Time
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 15-30 minutes

Global Market Share (2024)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 18% of lakehouse market

ML Model Training Cost Efficiency
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 1.0x baseline (integrated ML platform)

Starting Monthly Cost (10GB active data)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: $650

SQL Query Performance (TPC-DS Benchmark)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 45

Native ML Framework Integration
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: MLflow + Spark ML

Data Format Lock-in Risk
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Low (open Delta/Iceberg formats)

BI Tool Native Connectors
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 65

Maximum Concurrent Queries Per Warehouse
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: Unlimited (Spark clusters)

Customer Satisfaction Rating (G2 2025)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 82%

Setup Complexity (1-10 scale)
type: PropertyValue
value: Apache Hadoop: N/A | Databricks: 7

SpeakableSpecification
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Apache Hadoop — Wikipedia
type: CreativeWork

Databricks — Wikipedia
type: CreativeWork

Compare Apache Hadoop vs Databricks
type: Action
additional type: CompareAction
target: https://www.aversusb.net/compare/hadoop-vs-databricks)
object: Apache Hadoop
object: Databricks

Global Enterprise Customers
type: PropertyValue
value reference: Market Position

Supported Data Formats
type: PropertyValue
value reference: Flexibility

Data Sharing Standard
type: PropertyValue
value reference: Features

Multi-Language Support
type: PropertyValue
value reference: Development

Supported Cloud Platforms
type: PropertyValue
value reference: Infrastructure

Starting Compute Cost (per hour)
type: PropertyValue
value reference: Pricing

Pre-built AutoML Models
type: PropertyValue
value reference: ML Features

Real-Time Notebook Collaboration Users
type: PropertyValue
value reference: Collaboration

Native AWS Service Integrations
type: PropertyValue
value reference: Integrations

Delta Lake Support
type: PropertyValue
value reference: Data Architecture

Training Job Spot Instance Discount
type: PropertyValue
value reference: Cost Optimization

SQL Query Performance (sample 1TB table)
type: PropertyValue
value reference: Performance

Initial Licensing Cost
type: PropertyValue
value reference: Cost

Setup Time to Production
type: PropertyValue
value reference: Deployment

Cluster Management Required
type: PropertyValue
value reference: Operations

Users Per Collaborative Project
type: PropertyValue
value reference: Collaboration

Built-in Security Features
type: PropertyValue
value reference: Security

Supported Data Formats
type: PropertyValue
value reference: Compatibility

Community Size
type: PropertyValue
value reference: Community

Starting Monthly Cost (Small Team)
type: PropertyValue
value reference: Pricing

SQL Standard Compliance Level
type: PropertyValue
value reference: Query Capability

Supported Data Connectors
type: PropertyValue
value reference: Integration

Enterprise SLA Uptime
type: PropertyValue
value reference: Reliability

Native ML/AI Features
type: PropertyValue
value reference: AI/ML Capabilities

Data Consolidation Required
type: PropertyValue
value reference: Architecture

Average Query Latency (Analytical)
type: PropertyValue
value reference: Performance

Time to Deploy (Basic Setup)
type: PropertyValue
value reference: Implementation

Monthly Starting Cost
type: PropertyValue
value reference: Pricing

Apache Spark Query Performance Boost
type: PropertyValue
value reference: Performance

Available Services
type: PropertyValue
value reference: Platform Capabilities

Multi-Cloud Support
type: PropertyValue
value reference: Flexibility

BigQuery/Equivalent Query Speed (1TB dataset)
type: PropertyValue
value reference: Performance

Organizations Using Platform
type: PropertyValue
value reference: Enterprise Adoption

Native ML Pipeline Integration
type: PropertyValue
value reference: AI/ML

Data Lakehouse ACID Support
type: PropertyValue
value reference: Data Management

Enterprise Customers
type: PropertyValue
value reference: Market

Deployment Options
type: PropertyValue
value reference: Infrastructure

ML Feature Store
type: PropertyValue
value reference: Machine Learning

Query Latency (Average)
type: PropertyValue
value reference: Performance

Compute Cost Per Hour
type: PropertyValue
value reference: Pricing

Data Governance (Unity Catalog equivalent)
type: PropertyValue
value reference: Governance

Setup Complexity (1=Simple, 10=Complex)
type: PropertyValue
value reference: Usability

Fortune 500 Adoption
type: PropertyValue
value reference: Enterprise Adoption

Typical Query Latency (Structured Data)
type: PropertyValue
value reference: Performance

Supported Data Types
type: PropertyValue
value reference: Functionality

Cloud Providers
type: PropertyValue
value reference: Infrastructure

Infrastructure Management Required
type: PropertyValue
value reference: Operations

Native ML Framework Support
type: PropertyValue
value reference: Machine Learning

Minimum Learning Curve (months for competency)
type: PropertyValue
value reference: Ease of Use

Starting Monthly Cost (1 TB storage + compute)
type: PropertyValue
value reference: Pricing

Spark Performance (Query Speed)
type: PropertyValue
value reference: Performance

Total Service Offerings
type: PropertyValue
value reference: Platform Breadth

Compute Instance Cost (Standard)
type: PropertyValue
value reference: Pricing

Native ML Ops Tools
type: PropertyValue
value reference: Machine Learning

Microsoft Ecosystem Integration
type: PropertyValue
value reference: Enterprise Integration

Data Governance Features
type: PropertyValue
value reference: Data Management

Typical Enterprise Migration Time
type: PropertyValue
value reference: Implementation

SQL Query Performance (TPC-DS 100TB)
type: PropertyValue
value reference: Performance

Spark Job Acceleration
type: PropertyValue
value reference: Performance

ML Frameworks Supported
type: PropertyValue
value reference: AI/ML Capabilities

Data Governance Granularity
type: PropertyValue
value reference: Security & Governance

Global Region Availability
type: PropertyValue
value reference: Infrastructure

Enterprise Service Count
type: PropertyValue
value reference: Platform Breadth

Starting Monthly Cost (10TB workload)
type: PropertyValue
value reference: Pricing

ACID Transaction Support
type: PropertyValue
value reference: Data Reliability

SQL Query Performance (1TB dataset)
type: PropertyValue
value reference: Performance

Base Monthly Cost (minimum)
type: PropertyValue
value reference: Pricing

Native ML/AI Capabilities
type: PropertyValue
value reference: Features

Data Format Support
type: PropertyValue
value reference: Flexibility

Concurrent Users Support
type: PropertyValue
value reference: Scalability

Data Warehouse Setup Time
type: PropertyValue
value reference: Ease of Use

Global Market Share (2024)
type: PropertyValue
value reference: Market Position

ML Model Training Cost Efficiency
type: PropertyValue
value reference: ML Operations

Starting Monthly Cost (10GB active data)
type: PropertyValue
value reference: Pricing

SQL Query Performance (TPC-DS Benchmark)
type: PropertyValue
value reference: Performance

Native ML Framework Integration
type: PropertyValue
value reference: Capabilities

Data Format Lock-in Risk
type: PropertyValue
value reference: Data Portability

BI Tool Native Connectors
type: PropertyValue
value reference: Integrations

Maximum Concurrent Queries Per Warehouse
type: PropertyValue
value reference: Performance

Customer Satisfaction Rating (G2 2025)
type: PropertyValue
value reference: User Experience

Setup Complexity (1-10 scale)
type: PropertyValue
value reference: Deployment