Apache Spark vs Hadoop
type: Article
description: Apache Spark is a modern distributed computing framework that processes data 10-100x faster than Hadoop MapReduce through in-memory computing, while Hadoop is a mature ecosystem designed primarily for batch processing with disk-based storage. Spark has largely superseded Hadoop for most new big data projects.
language: en-US
date published: 2026-07-09T06:53:01.223Z
date created: 2026-07-09T06:53:01.223Z
date modified: 2026-07-09T06:53:01.261Z
content reference time: 2026-07-09T06:53:01.261Z
author: A Versus B
publisher: A Versus B
about: Apache Spark
about: Hadoop
abstract: Apache Spark is a modern distributed computing framework that processes data 10-100x faster than Hadoop MapReduce through in-memory computing, while Hadoop is a mature ecosystem designed primarily for batch processing with disk-based storage. Spark has largely superseded Hadoop for most new big data projects.
text: Processing Speed. Processing Model. Learning Curve. Ecosystem Maturity. Fault Tolerance. Setup Complexity. Real-time Capability
conclusion text: Choose Apache Spark if you need fast, flexible data processing with support for streaming, machine learning, and interactive queries—it's the industry standard for modern big data projects and most new deployments. Choose Hadoop if you have legacy systems already running it, need proven long-term stability in highly distributed storage environments, or work in organizations with deep Java expertise and existing Hadoop investments.
additional property: Typical Query Latency (1GB dataset)
  type: PropertyValue
  value: Apache Spark: 2000-5000ms | Hadoop: N/A
additional property: Memory Required Per Query
  type: PropertyValue
  value: Apache Spark: 500-2000MB | Hadoop: N/A
additional property: Setup Time for Basic Analytics
  type: PropertyValue
  value: Apache Spark: 30-120 minutes | Hadoop: N/A
additional property: Primary Language Support
  type: PropertyValue
  value: Apache Spark: Python, Scala, SQL, R, Java | Hadoop: N/A
additional property: Query Latency (1GB CSV)
  type: PropertyValue
  value: Apache Spark: 8,000-15,000ms | Hadoop: N/A
additional property: Maximum Scalable Dataset Size
  type: PropertyValue
  value: Apache Spark: 1,000+ PB | Hadoop: N/A
additional property: Minimum Memory Requirement
  type: PropertyValue
  value: Apache Spark: 2-4 GB | Hadoop: N/A
additional property: Setup Time (from scratch)
  type: PropertyValue
  value: Apache Spark: 60-120 (cluster setup) | Hadoop: N/A
additional property: GitHub Stars (2026)
  type: PropertyValue
  value: Apache Spark: 35,900 stars | Hadoop: N/A
additional property: Multi-machine Distributed Computing
  type: PropertyValue
  value: Apache Spark: Native support | Hadoop: N/A
additional property: Fault Tolerance
  type: PropertyValue
  value: Apache Spark: Yes (RDD lineage-based) | Hadoop: N/A
additional property: Initial Licensing Cost
  type: PropertyValue
  value: Apache Spark: $0 | Hadoop: N/A
additional property: Setup Time to Production
  type: PropertyValue
  value: Apache Spark: 6-12 weeks | Hadoop: N/A
additional property: Cluster Management Required
  type: PropertyValue
  value: Apache Spark: 40-80 hours (dedicated DevOps engineer) | Hadoop: N/A
additional property: SQL Query Performance (TPC-DS Benchmark)
  type: PropertyValue
  value: Apache Spark: 45-120 seconds | Hadoop: N/A
additional property: Users Per Collaborative Project
  type: PropertyValue
  value: Apache Spark: 1-5 (via Jupyter sharing) | Hadoop: N/A
additional property: Built-in Security Features
  type: PropertyValue
  value: Apache Spark: 0 (manual implementation required) | Hadoop: N/A
additional property: Supported Data Formats
  type: PropertyValue
  value: Apache Spark: Parquet, ORC, JSON, CSV, Avro, Delta (via library) | Hadoop: N/A
additional property: Community Size
  type: PropertyValue
  value: Apache Spark: 25,000+ questions | Hadoop: N/A
additional property: Typical Cluster Cost (Monthly)
  type: PropertyValue
  value: Apache Spark: $1,500-$5,000+ | Hadoop: N/A
additional property: Data Processing Speed (1TB dataset)
  type: PropertyValue
  value: Apache Spark: 5-15 minutes | Hadoop: N/A
additional property: Supported Programming Languages
  type: PropertyValue
  value: Apache Spark: Python, Scala, Java, R, SQL | Hadoop: N/A
additional property: Setup Time for Production Deployment
  type: PropertyValue
  value: Apache Spark: 40-80 hours | Hadoop: N/A
additional property: Supported Warehouse Platforms
  type: PropertyValue
  value: Apache Spark: Hadoop, Kubernetes, cloud object storage (3+ classes) | Hadoop: N/A
additional property: Built-in Data Testing Features
  type: PropertyValue
  value: Apache Spark: 0 (requires external frameworks) | Hadoop: N/A
additional property: Minimum Dataset Size for Optimal Use
  type: PropertyValue
  value: Apache Spark: 100+ GB | Hadoop: N/A
additional property: GitHub Community (Stars)
  type: PropertyValue
  value: Apache Spark: 38.5K stars | Hadoop: N/A
additional property: Query Performance on 1TB Dataset
  type: PropertyValue
  value: Apache Spark: 30-120 seconds | Hadoop: N/A
additional property: Cluster Setup Time
  type: PropertyValue
  value: Apache Spark: 40-80 hours | Hadoop: N/A
additional property: Cost per Core-Hour
  type: PropertyValue
  value: Apache Spark: $0.035-0.15 | Hadoop: N/A
additional property: Supported Languages/APIs
  type: PropertyValue
  value: Apache Spark: Python, Scala, Java, SQL, R | Hadoop: N/A
additional property: Maximum Dataset Size Supported
  type: PropertyValue
  value: Apache Spark: Unlimited (depends on storage) | Hadoop: N/A
additional property: Cloud Provider Support
  type: PropertyValue
  value: Apache Spark: 4+ (AWS, Azure, GCP, on-prem) | Hadoop: N/A
additional property: Machine Learning Algorithms Available
  type: PropertyValue
  value: Apache Spark: 50+ (MLlib + custom models) | Hadoop: N/A
additional property: Data Format Support
  type: PropertyValue
  value: Apache Spark: 8+ formats (Parquet, ORC, Avro, Delta, Iceberg, HDF5, CSV, JSON) | Hadoop: N/A
additional property: Processing Speed (Same 1TB dataset)
  type: PropertyValue
  value: Apache Spark: 30-60 seconds (in-memory) | Hadoop: N/A
additional property: Processing Speed (Average Query)
  type: PropertyValue
  value: Apache Spark: 10-60 seconds | Hadoop: N/A
additional property: Memory Requirement (Per Node)
  type: PropertyValue
  value: Apache Spark: 16-256 GB | Hadoop: N/A
additional property: First Release
  type: PropertyValue
  value: Apache Spark: 2014 | Hadoop: N/A
additional property: Real-time Streaming Capability
  type: PropertyValue
  value: Apache Spark: 500-5000 ms micro-batches | Hadoop: N/A
additional property: Market Adoption by Fortune 500
  type: PropertyValue
  value: Apache Spark: 82% | Hadoop: N/A
additional property: Typical Cluster Cost (100-node setup)
  type: PropertyValue
  value: Apache Spark: $450,000-650,000 | Hadoop: N/A
additional property: Fault Tolerance Method
  type: PropertyValue
  value: Apache Spark: Lineage-based recovery (RDD parents) | Hadoop: N/A
additional property: End-to-End Latency (p99)
  type: PropertyValue
  value: Apache Spark: 500-2000ms | Hadoop: N/A
additional property: Native Connectors Available
  type: PropertyValue
  value: Apache Spark: 200+ via ecosystem integrations | Hadoop: N/A
additional property: GitHub Stars
  type: PropertyValue
  value: Apache Spark: 40,100 stars | Hadoop: N/A
additional property: Enterprise Adoption Rate
  type: PropertyValue
  value: Apache Spark: 65% (Databricks, AWS, Google, Meta deployments) | Hadoop: N/A
additional property: Memory Overhead (per task)
  type: PropertyValue
  value: Apache Spark: ~400-800MB (GC overhead) | Hadoop: N/A
additional property: Supported Event Time Semantics
  type: PropertyValue
  value: Apache Spark: Partial in Structured Streaming, limited out-of-order support | Hadoop: N/A
interactivity type: active
significant link: https://www.aversusb.net/entity/apache-spark
significant link: https://www.aversusb.net/alternatives/apache-spark
significant link: https://www.aversusb.net/entity/hadoop
significant link: https://www.aversusb.net/alternatives/hadoop
significant link: https://www.aversusb.net/api/answer/apache-spark-vs-hadoop))
significant link: https://www.aversusb.net/api/v1/schema/apache-spark-vs-hadoop))
related link: https://www.aversusb.net/compare/apache-spark-vs-hadoop)
related link: https://www.aversusb.net/compare/duckdb-vs-spark
related link: https://www.aversusb.net/compare/apache-spark-vs-duckdb
related link: https://www.aversusb.net/compare/apache-spark-vs-databricks
related link: https://www.aversusb.net/compare/apache-spark-vs-dbt
related link: https://www.aversusb.net/compare/apache-spark-vs-bigquery
related link: https://www.aversusb.net/compare/hadoop-vs-apache-spark
related link: https://www.aversusb.net/compare/flink-vs-apache-spark
teaches: How to choose between Apache Spark and Hadoop
genre: Software

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caption: Apache Spark vs Hadoop — Side-by-side comparison on A Versus B

A Versus B
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Apache Spark
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alternate name: AS
language: en-US
description: In-memory distributed computing framework for fast batch, streaming, and interactive data processing.
image: ImageObject
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subject of: Apache Spark vs Hadoop
image: ImageObject
  caption: Apache Spark

Hadoop
type: SoftwareApplication
language: en-US
description: Mature distributed computing framework with HDFS storage and MapReduce processing engine.
subject of: Apache Spark vs Hadoop

Apache Spark vs Hadoop — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for Apache Spark vs Hadoop. Contains 90 attribute comparisons, 5 FAQ pairs.
language: en-US
license: https://creativecommons.org/licenses/by/4.0/
creator: A Versus B
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variable measured: Mean Time to Deploy Production Job
variable measured: Stateful Window Operations Complexity
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variable measured: GitHub Stars (Popularity Indicator)
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is based on: https://www.aversusb.net/compare/apache-spark-vs-hadoop))

Apache Spark vs Hadoop — Attribute Category Vocabulary
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language: en-US
description: Taxonomy of attribute categories used to classify comparison data for Apache Spark vs Hadoop.
has defined term: Performance
has defined term: Resource Efficiency
has defined term: Ease of Use
has defined term: Developer Experience
has defined term: Scalability
has defined term: Infrastructure
has defined term: Community
has defined term: Architecture
has defined term: Reliability
has defined term: Cost
has defined term: Deployment
has defined term: Operations
has defined term: Collaboration
has defined term: Security
has defined term: Compatibility
has defined term: Support
has defined term: Development
has defined term: Features
has defined term: Use Case
has defined term: Implementation
has defined term: Pricing
has defined term: ML Capabilities
has defined term: Flexibility
has defined term: History
has defined term: Industry
has defined term: Ecosystem
has defined term: Community Adoption
has defined term: Industry Adoption
has defined term: Resources
has defined term: Capability
has defined term: Maturity
has defined term: Market
has defined term: Development Velocity
has defined term: Functionality
has defined term: Adoption
has defined term: SQL Support
has defined term: Trend
has defined term: Efficiency
has defined term: Setup
has defined term: Hardware
has defined term: Capabilities
has defined term: Maintenance

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

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

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

Developer Experience
type: DefinedTerm
term code: developer-experience
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

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

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

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

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

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

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

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

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

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

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

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

Support
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term code: support
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

Development
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term code: development
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

Features
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term code: features
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

Use Case
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Implementation
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term code: implementation
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

Pricing
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term code: pricing
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

ML Capabilities
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term code: ml-capabilities
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

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

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

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

Ecosystem
type: DefinedTerm
term code: ecosystem
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

Community Adoption
type: DefinedTerm
term code: community-adoption
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

Industry Adoption
type: DefinedTerm
term code: industry-adoption
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

Resources
type: DefinedTerm
term code: resources
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

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

Maturity
type: DefinedTerm
term code: maturity
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

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

Development Velocity
type: DefinedTerm
term code: development-velocity
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

Functionality
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term code: functionality
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

Adoption
type: DefinedTerm
term code: adoption
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

SQL Support
type: DefinedTerm
term code: sql-support
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

Trend
type: DefinedTerm
term code: trend
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

Efficiency
type: DefinedTerm
term code: efficiency
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

Setup
type: DefinedTerm
term code: setup
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

Hardware
type: DefinedTerm
term code: hardware
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

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

Maintenance
type: DefinedTerm
term code: maintenance
in defined term set: Apache Spark vs Hadoop — Attribute Category Vocabulary

faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-09T06:53:01.261Z
author: A Versus B
publisher: A Versus B
is part of: apache-spark-vs-hadoop))
main entity: Can Spark replace Hadoop entirely?
  type: Question
  accepted answer: Answer
    text: Yes, for most modern use cases. Spark can run on YARN (Hadoop's resource manager) and doesn't require HDFS. However, if you need Hadoop's distributed storage (HDFS) specifically for data durability across unreliable hardware, you may still use HDFS alongside Spark. Most new projects start with Spark only, often on Kubernetes or cloud storage (S3, GCS) instead of HDFS.
    language: en-US
main entity: Is Hadoop still used in 2026?
  type: Question
  accepted answer: Answer
    text: Yes, but primarily for legacy systems and maintenance. Industry surveys show 78% of organizations starting new big data projects now choose Spark or cloud-native alternatives like Databricks, while existing Hadoop deployments remain in maintenance mode. Some enterprises maintain Hadoop for petabyte-scale data warehouses where the cost per TB is critical.
    language: en-US
main entity: Why is Spark so much faster than Hadoop MapReduce?
  type: Question
  accepted answer: Answer
    text: Spark keeps data in RAM between processing steps, while Hadoop MapReduce writes to disk after each map and reduce phase. For iterative algorithms (like machine learning), Spark avoids the 10-100x slowdown from repeated disk I/O. For one-time batch jobs, the speed difference is smaller (2-5x faster).
    language: en-US
main entity: What's the hardware cost difference?
  type: Question
  accepted answer: Answer
    text: Spark costs more per node due to higher RAM requirements (32 GB vs 12 GB typical), but achieves results faster, reducing cluster utilization time. For a 100-node cluster processing the same workload: Hadoop might take 40 hours with cheaper nodes, while Spark takes 4 hours with more expensive nodes. Total cost often favors Spark when factoring in operational expenses and developer time.
    language: en-US
main entity: Can I run Spark on Hadoop YARN?
  type: Question
  accepted answer: Answer
    text: Yes. Spark can use YARN as its resource manager while reading from HDFS, giving you Spark's speed with Hadoop's storage layer. This is a common migration path for organizations transitioning from pure Hadoop. However, Spark on Kubernetes is becoming more popular for new deployments due to better multi-tenancy and elasticity.
    language: en-US

How to Choose Between Apache Spark and Hadoop
type: HowTo
description: Apache Spark is a modern distributed computing framework that processes data 10-100x faster than Hadoop MapReduce through in-memory computing, while Hadoop is a mature ecosystem designed primarily for batch processing with disk-based storage. Spark has largely superseded Hadoop for most new big data projects.
language: en-US
total time: PT3M
step: Consider: Processing Speed
step: Consider: Processing Model
step: Consider: Learning Curve
step: Consider: Ecosystem Maturity
step: Consider: Fault Tolerance
step: Consider: Setup Complexity
step: Consider: Real-time Capability
tool: A Versus B Comparison Guide
tip: HowToTip
  text: Choose Apache Spark if you need fast, flexible data processing with support for streaming, machine learning, and interactive queries—it's the industry standard for modern big data projects and most new deployments. Choose Hadoop if you have legacy systems already running it, need proven long-term stability in highly distributed storage environments, or work in organizations with deep Java expertise and existing Hadoop investments.

Consider: Processing Speed
type: HowToStep
position: 1
text: Processing Speed: Apache Spark has 10-100x faster (in-memory), while Hadoop has Baseline (disk-based).

Consider: Processing Model
type: HowToStep
position: 2
text: Processing Model: Apache Spark has In-memory + real-time streaming, while Hadoop has Disk-based batch processing only.

Consider: Learning Curve
type: HowToStep
position: 3
text: Learning Curve: Apache Spark has Moderate (Scala/Python/SQL), while Hadoop has Steep (Java-heavy MapReduce).

Consider: Ecosystem Maturity
type: HowToStep
position: 4
text: Ecosystem Maturity: Apache Spark has Mature (released 2014), while Hadoop has Very mature (released 2006).

Consider: Fault Tolerance
type: HowToStep
position: 5
text: Fault Tolerance: Apache Spark has RDD lineage-based, while Hadoop has Replication-based (HDFS).

Consider: Setup Complexity
type: HowToStep
position: 6
text: Setup Complexity: Apache Spark has Can run standalone or on YARN, while Hadoop has Requires full cluster setup.

Consider: Real-time Capability
type: HowToStep
position: 7
text: Real-time Capability: Apache Spark has Native streaming support, while Hadoop has Batch only (requires external tools).

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: Apache Spark vs Hadoop
review body: Apache Spark is a modern distributed computing framework that processes data 10-100x faster than Hadoop MapReduce through in-memory computing, while Hadoop is a mature ecosystem designed primarily for batch processing with disk-based storage. Spark has largely superseded Hadoop for most new big data projects.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: Apache Spark vs Hadoop

Throughput (events/sec per node)
type: PropertyValue
value: Apache Spark: ~500K-1M events/sec | Hadoop: N/A

Batch+Stream Unified Code
type: PropertyValue
value: Apache Spark: Unified via Structured Streaming/Dataset API | Hadoop: N/A

Processing Speed (Iterative Query)
type: PropertyValue
value: Apache Spark: 0.5-2 seconds | Hadoop: 30-60 seconds
value reference: Apache Spark

Memory Requirement
type: PropertyValue
value: Apache Spark: 8-64 GB per node | Hadoop: 2-4 GB per node
value reference: Hadoop

Supported Languages
type: PropertyValue
value: Apache Spark: 5 (Scala, Python, Java, R, SQL) | Hadoop: 1 (Java)
value reference: Apache Spark

Real-time Processing
type: PropertyValue
value: Apache Spark: 100-500 ms (micro-batch) | Hadoop: Not natively supported

Ecosystem Age
type: PropertyValue
value: Apache Spark: 12 years (since 2013) | Hadoop: 20 years (since 2005)
value reference: Hadoop

Enterprise Adoption
type: PropertyValue
value: Apache Spark: 74% currently use | Hadoop: 45% legacy deployments
value reference: Apache Spark

Machine Learning Capability
type: PropertyValue
value: Apache Spark: Full MLlib with algorithms, pipelines | Hadoop: Requires third-party libraries

Data Storage Redundancy
type: PropertyValue
value: Apache Spark: Depends on underlying storage | Hadoop: 3x replication (HDFS default)

Event Latency (Processing End-to-End)
type: PropertyValue
value: Apache Spark: 100-2,000ms (Spark Streaming micro-batch interval) | Hadoop: N/A

Throughput Capacity
type: PropertyValue
value: Apache Spark: 500,000 - 2,000,000 (batch-optimized) | Hadoop: N/A

Memory Per Node
type: PropertyValue
value: Apache Spark: 8-12GB (caching overhead) | Hadoop: N/A

Developer Community Size
type: PropertyValue
value: Apache Spark: 7,200,000 (StackOverflow, job postings 2024) | Hadoop: N/A

Available Libraries & Integrations
type: PropertyValue
value: Apache Spark: 14,000+ (Spark packages, MLlib, SQL, GraphX, etc.) | Hadoop: N/A

Mean Time to Deploy Production Job
type: PropertyValue
value: Apache Spark: 2-4 weeks (larger talent pool, more examples) | Hadoop: N/A

Stateful Window Operations Complexity
type: PropertyValue
value: Apache Spark: 80-150 lines (custom state handling needed) | Hadoop: N/A

Minimum Achievable Latency (P99)
type: PropertyValue
value: Apache Spark: 500-2000ms | Hadoop: N/A

GitHub Stars (Popularity Indicator)
type: PropertyValue
value: Apache Spark: 32,000 | Hadoop: N/A

Market Adoption Rate
type: PropertyValue
value: Apache Spark: 60-65% | Hadoop: N/A

Memory Overhead per Task
type: PropertyValue
value: Apache Spark: 512-1024MB | Hadoop: N/A

ANSI SQL Compliance
type: PropertyValue
value: Apache Spark: 98% | Hadoop: N/A

State Management Capabilities
type: PropertyValue
value: Apache Spark: 2 types (RDD state, DataFrame state) | Hadoop: N/A

Production Deployments (2026)
type: PropertyValue
value: Apache Spark: 45,000-55,000 | Hadoop: N/A

Year-over-Year Growth Rate
type: PropertyValue
value: Apache Spark: 8% | Hadoop: N/A

Minimum Hardware Requirements
type: PropertyValue
value: Apache Spark: 8GB (per node, 3+ nodes recommended) | Hadoop: N/A

Query Latency on 100GB Dataset
type: PropertyValue
value: Apache Spark: 5-30 seconds | Hadoop: N/A

Maximum Practical Data Size
type: PropertyValue
value: Apache Spark: 100,000+ (petabyte scale) | Hadoop: N/A

Memory Usage for 10GB Query
type: PropertyValue
value: Apache Spark: 2-4GB | Hadoop: N/A

Time to First Query (fresh install)
type: PropertyValue
value: Apache Spark: 45-120 (including cluster setup) | Hadoop: N/A

Number of Supported Languages
type: PropertyValue
value: Apache Spark: 6 (Scala, Python, SQL, R, Java, Go) | Hadoop: N/A

Community GitHub Stars (2026)
type: PropertyValue
value: Apache Spark: 39,500+ | Hadoop: N/A

Query Processing Throughput (GBps)
type: PropertyValue
value: Apache Spark: 1-10 (cluster dependent) | Hadoop: N/A

Typical Processing Speed (iterative queries)
type: PropertyValue
value: Apache Spark: 2-5 seconds | Hadoop: 20-60 seconds
value reference: Apache Spark

Memory Requirements per Node
type: PropertyValue
value: Apache Spark: 16-64 GB (in-memory caching) | Hadoop: 8-16 GB (disk-based)
value reference: Hadoop

Real-time Streaming Support
type: PropertyValue
value: Apache Spark: Native (Spark Streaming, Structured Streaming) | Hadoop: Not native (requires external tools like Storm/Flink)

Production Deployments (estimated 2024)
type: PropertyValue
value: Apache Spark: 450,000+ globally | Hadoop: 120,000+ (declining)
value reference: Apache Spark

Setup Time (basic cluster)
type: PropertyValue
value: Apache Spark: 1-2 hours | Hadoop: 4-8 hours
value reference: Apache Spark

Active Development (2024-2026)
type: PropertyValue
value: Apache Spark: Highly active (Apache Foundation priority) | Hadoop: Maintenance mode (legacy support only)

Approximate Learning Time for Developers
type: PropertyValue
value: Apache Spark: 2-4 weeks | Hadoop: 6-10 weeks
value reference: Apache Spark

First Release Year
type: PropertyValue
value: Apache Spark: 2014 | Hadoop: 2006
value reference: Hadoop

SpeakableSpecification
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Apache Spark — Wikipedia
type: CreativeWork

Hadoop — Wikipedia
type: CreativeWork

Compare Apache Spark vs Hadoop
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Throughput Capacity
type: PropertyValue
value reference: Performance

Memory Per Node
type: PropertyValue
value reference: Resource Efficiency

Developer Community Size
type: PropertyValue
value reference: Ecosystem

Available Libraries & Integrations
type: PropertyValue
value reference: Ecosystem

Mean Time to Deploy Production Job
type: PropertyValue
value reference: Development Velocity

Stateful Window Operations Complexity
type: PropertyValue
value reference: Functionality

Minimum Achievable Latency (P99)
type: PropertyValue
value reference: Performance

GitHub Stars (Popularity Indicator)
type: PropertyValue
value reference: Community

Market Adoption Rate
type: PropertyValue
value reference: Adoption

Memory Overhead per Task
type: PropertyValue
value reference: Resource Efficiency

ANSI SQL Compliance
type: PropertyValue
value reference: SQL Support

State Management Capabilities
type: PropertyValue
value reference: Functionality

Production Deployments (2026)
type: PropertyValue
value reference: Adoption

Year-over-Year Growth Rate
type: PropertyValue
value reference: Trend

Minimum Hardware Requirements
type: PropertyValue
value reference: Infrastructure

Query Latency on 100GB Dataset
type: PropertyValue
value reference: Performance

Maximum Practical Data Size
type: PropertyValue
value reference: Scalability

Memory Usage for 10GB Query
type: PropertyValue
value reference: Efficiency

Time to First Query (fresh install)
type: PropertyValue
value reference: Setup

Number of Supported Languages
type: PropertyValue
value reference: Compatibility

Community GitHub Stars (2026)
type: PropertyValue
value reference: Community

Query Processing Throughput (GBps)
type: PropertyValue
value reference: Performance

Typical Processing Speed (iterative queries)
type: PropertyValue
value reference: Performance

Memory Requirements per Node
type: PropertyValue
value reference: Hardware

Real-time Streaming Support
type: PropertyValue
value reference: Capabilities

Production Deployments (estimated 2024)
type: PropertyValue
value reference: Adoption

Setup Time (basic cluster)
type: PropertyValue
value reference: Deployment

Active Development (2024-2026)
type: PropertyValue
value reference: Maintenance

Approximate Learning Time for Developers
type: PropertyValue
value reference: Developer Experience

First Release Year
type: PropertyValue
value reference: History