Apache Spark vs DuckDB
type: Article
description: Apache Spark is a distributed computing framework designed for large-scale data processing across clusters, while DuckDB is an in-process SQL database optimized for analytical queries on single machines or small clusters. Spark handles petabyte-scale data; DuckDB excels at sub-terabyte interactive analytics with minimal overhead.
language: en-US
date published: 2026-07-09T06:14:24.304Z
date created: 2026-07-09T06:14:24.304Z
date modified: 2026-07-09T06:14:24.389Z
content reference time: 2026-07-09T06:14:24.389Z
author: A Versus B
publisher: A Versus B
about: Apache Spark
about: DuckDB
abstract: Apache Spark is a distributed computing framework designed for large-scale data processing across clusters, while DuckDB is an in-process SQL database optimized for analytical queries on single machines or small clusters. Spark handles petabyte-scale data; DuckDB excels at sub-terabyte interactive analytics with minimal overhead.
text: Processing Architecture. Ideal Data Size. Query Latency (100GB dataset). Setup Complexity. Memory Efficiency on 10GB Query. Supported Languages. Enterprise Ecosystem
conclusion text: Choose Apache Spark if you're processing data across 100GB+ datasets, need distributed computing across multiple machines, or require enterprise support and a mature ecosystem. Choose DuckDB if you need fast interactive analytics on datasets under 1TB, want zero infrastructure overhead, prefer minimal setup time, or are building analytical applications that embed a database engine.
additional property: Typical Query Latency (1GB dataset)
  type: PropertyValue
  value: Apache Spark: 2000-5000ms | DuckDB: 50-200ms
  value reference: DuckDB
additional property: Memory Required Per Query
  type: PropertyValue
  value: Apache Spark: 500-2000MB | DuckDB: 10-50MB
  value reference: DuckDB
additional property: Setup Time for Basic Analytics
  type: PropertyValue
  value: Apache Spark: 30-120 minutes | DuckDB: 1-5 minutes
  value reference: DuckDB
additional property: Primary Language Support
  type: PropertyValue
  value: Apache Spark: Python, Scala, SQL, R, Java | DuckDB: Python, SQL, C++, R, Julia, Node.js
additional property: Query Latency (1GB CSV)
  type: PropertyValue
  value: Apache Spark: 8,000-15,000ms | DuckDB: 150-500ms
  value reference: DuckDB
additional property: Maximum Scalable Dataset Size
  type: PropertyValue
  value: Apache Spark: 1,000+ PB | DuckDB: 10-50
  value reference: Apache Spark
additional property: Minimum Memory Requirement
  type: PropertyValue
  value: Apache Spark: 2-4 GB | DuckDB: 0.1-0.5 GB
  value reference: DuckDB
additional property: Setup Time (from scratch)
  type: PropertyValue
  value: Apache Spark: 60-120 (cluster setup) | DuckDB: 2-5 (local install)
  value reference: DuckDB
additional property: GitHub Stars (2026)
  type: PropertyValue
  value: Apache Spark: 35,900 stars | DuckDB: 18,500+
  value reference: Apache Spark
additional property: Multi-machine Distributed Computing
  type: PropertyValue
  value: Apache Spark: Native support | DuckDB: Not supported
additional property: Fault Tolerance
  type: PropertyValue
  value: Apache Spark: Yes (RDD lineage-based) | DuckDB: No (single machine)
additional property: Initial Licensing Cost
  type: PropertyValue
  value: Apache Spark: $0 | DuckDB: N/A
additional property: Setup Time to Production
  type: PropertyValue
  value: Apache Spark: 6-12 weeks | DuckDB: N/A
additional property: Cluster Management Required
  type: PropertyValue
  value: Apache Spark: 40-80 hours (dedicated DevOps engineer) | DuckDB: N/A
additional property: SQL Query Performance (TPC-DS Benchmark)
  type: PropertyValue
  value: Apache Spark: 45-120 seconds | DuckDB: N/A
additional property: Users Per Collaborative Project
  type: PropertyValue
  value: Apache Spark: 1-5 (via Jupyter sharing) | DuckDB: N/A
additional property: Built-in Security Features
  type: PropertyValue
  value: Apache Spark: 0 (manual implementation required) | DuckDB: N/A
additional property: Supported Data Formats
  type: PropertyValue
  value: Apache Spark: Parquet, ORC, JSON, CSV, Avro, Delta (via library) | DuckDB: 12+ formats
additional property: Community Size
  type: PropertyValue
  value: Apache Spark: 25,000+ questions | DuckDB: N/A
additional property: Typical Cluster Cost (Monthly)
  type: PropertyValue
  value: Apache Spark: $1,500-$5,000+ | DuckDB: N/A
additional property: Data Processing Speed (1TB dataset)
  type: PropertyValue
  value: Apache Spark: 5-15 minutes | DuckDB: N/A
additional property: Supported Programming Languages
  type: PropertyValue
  value: Apache Spark: Python, Scala, Java, R, SQL | DuckDB: Python, R, Java, C++, Node.js, Go
  value reference: DuckDB
additional property: Setup Time for Production Deployment
  type: PropertyValue
  value: Apache Spark: 40-80 hours | DuckDB: N/A
additional property: Supported Warehouse Platforms
  type: PropertyValue
  value: Apache Spark: Hadoop, Kubernetes, cloud object storage (3+ classes) | DuckDB: N/A
additional property: Built-in Data Testing Features
  type: PropertyValue
  value: Apache Spark: 0 (requires external frameworks) | DuckDB: N/A
additional property: Minimum Dataset Size for Optimal Use
  type: PropertyValue
  value: Apache Spark: 100+ GB | DuckDB: N/A
additional property: GitHub Community (Stars)
  type: PropertyValue
  value: Apache Spark: 38.5K stars | DuckDB: N/A
additional property: Query Performance on 1TB Dataset
  type: PropertyValue
  value: Apache Spark: 30-120 seconds | DuckDB: N/A
additional property: Cluster Setup Time
  type: PropertyValue
  value: Apache Spark: 40-80 hours | DuckDB: N/A
additional property: Cost per Core-Hour
  type: PropertyValue
  value: Apache Spark: $0.035-0.15 | DuckDB: N/A
additional property: Supported Languages/APIs
  type: PropertyValue
  value: Apache Spark: Python, Scala, Java, SQL, R | DuckDB: N/A
additional property: Maximum Dataset Size Supported
  type: PropertyValue
  value: Apache Spark: Unlimited (depends on storage) | DuckDB: N/A
additional property: Cloud Provider Support
  type: PropertyValue
  value: Apache Spark: 4+ (AWS, Azure, GCP, on-prem) | DuckDB: N/A
additional property: Machine Learning Algorithms Available
  type: PropertyValue
  value: Apache Spark: 50+ (MLlib + custom models) | DuckDB: N/A
additional property: Data Format Support
  type: PropertyValue
  value: Apache Spark: 8+ formats (Parquet, ORC, Avro, Delta, Iceberg, HDF5, CSV, JSON) | DuckDB: N/A
additional property: Processing Speed (Same 1TB dataset)
  type: PropertyValue
  value: Apache Spark: 30-60 seconds (in-memory) | DuckDB: N/A
additional property: Processing Speed (Average Query)
  type: PropertyValue
  value: Apache Spark: 10-60 seconds | DuckDB: N/A
additional property: Memory Requirement (Per Node)
  type: PropertyValue
  value: Apache Spark: 16-256 GB | DuckDB: N/A
additional property: First Release
  type: PropertyValue
  value: Apache Spark: 2014 | DuckDB: N/A
additional property: Real-time Streaming Capability
  type: PropertyValue
  value: Apache Spark: 500-5000 ms micro-batches | DuckDB: N/A
additional property: Market Adoption by Fortune 500
  type: PropertyValue
  value: Apache Spark: 82% | DuckDB: N/A
additional property: Typical Cluster Cost (100-node setup)
  type: PropertyValue
  value: Apache Spark: $450,000-650,000 | DuckDB: N/A
additional property: Fault Tolerance Method
  type: PropertyValue
  value: Apache Spark: Lineage-based recovery (RDD parents) | DuckDB: N/A
additional property: End-to-End Latency (p99)
  type: PropertyValue
  value: Apache Spark: 500-2000ms | DuckDB: N/A
additional property: Native Connectors Available
  type: PropertyValue
  value: Apache Spark: 200+ via ecosystem integrations | DuckDB: N/A
additional property: GitHub Stars
  type: PropertyValue
  value: Apache Spark: 40,100 stars | DuckDB: N/A
additional property: Enterprise Adoption Rate
  type: PropertyValue
  value: Apache Spark: 65% (Databricks, AWS, Google, Meta deployments) | DuckDB: N/A
additional property: Memory Overhead (per task)
  type: PropertyValue
  value: Apache Spark: ~400-800MB (GC overhead) | DuckDB: N/A
additional property: Supported Event Time Semantics
  type: PropertyValue
  value: Apache Spark: Partial in Structured Streaming, limited out-of-order support | DuckDB: 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/duckdb
significant link: https://www.aversusb.net/alternatives/duckdb
significant link: https://www.aversusb.net/api/answer/apache-spark-vs-duckdb)
significant link: https://www.aversusb.net/api/v1/schema/apache-spark-vs-duckdb)
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/clickhouse-vs-duckdb
related link: https://www.aversusb.net/compare/duckdb-vs-sqlite
related link: https://www.aversusb.net/compare/duckdb-vs-polars
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/duckdb-vs-bigquery
teaches: How to choose between Apache Spark and DuckDB
genre: Software

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

A Versus B
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Apache Spark
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description: In-memory distributed computing framework for fast batch, streaming, and interactive data processing.
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subject of: Apache Spark vs DuckDB
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DuckDB
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language: en-US
description: In-process SQL analytical database engine optimized for fast queries on structured data without infrastructure.
image: ImageObject
  caption: DuckDB
subject of: Apache Spark vs DuckDB
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  caption: DuckDB

Apache Spark vs DuckDB — Structured Comparison Data
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description: Machine-readable comparison dataset for Apache Spark vs DuckDB. Contains 158 attribute comparisons, 5 FAQ pairs.
language: en-US
license: https://creativecommons.org/licenses/by/4.0/
creator: A Versus B
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is based on: https://www.aversusb.net/compare/apache-spark-vs-duckdb)

Apache Spark vs DuckDB — Attribute Category Vocabulary
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language: en-US
description: Taxonomy of attribute categories used to classify comparison data for Apache Spark vs DuckDB.
has defined term: Performance
has defined term: Resource Efficiency
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has defined term: Community
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has defined term: Deployment
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has defined term: Development
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has defined term: Pricing
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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
has defined term: Resource Usage
has defined term: Concurrency
has defined term: Storage Efficiency
has defined term: Feature Completeness
has defined term: Data Integrity
has defined term: API Completeness
has defined term: Community Support
has defined term: SQL Features
has defined term: Workflow Speed
has defined term: Capacity
has defined term: Quotas
has defined term: Convenience
has defined term: Query Language
has defined term: Operability

Performance
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term code: performance
in defined term set: Apache Spark vs DuckDB — Attribute Category Vocabulary

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

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

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

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

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

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

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

Reliability
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term code: reliability
in defined term set: Apache Spark vs DuckDB — Attribute Category Vocabulary

Cost
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term code: cost
in defined term set: Apache Spark vs DuckDB — Attribute Category Vocabulary

Deployment
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term code: deployment
in defined term set: Apache Spark vs DuckDB — Attribute Category Vocabulary

Operations
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term code: operations
in defined term set: Apache Spark vs DuckDB — Attribute Category Vocabulary

Collaboration
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term code: collaboration
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Security
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Compatibility
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Use Case
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Implementation
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Pricing
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ML Capabilities
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Flexibility
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term code: flexibility
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History
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Industry
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term code: industry
in defined term set: Apache Spark vs DuckDB — Attribute Category Vocabulary

Ecosystem
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term code: ecosystem
in defined term set: Apache Spark vs DuckDB — Attribute Category Vocabulary

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Industry Adoption
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Resources
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Capability
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Functionality
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term code: functionality
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Adoption
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SQL Support
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Trend
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Efficiency
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term code: efficiency
in defined term set: Apache Spark vs DuckDB — Attribute Category Vocabulary

Setup
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term code: setup
in defined term set: Apache Spark vs DuckDB — Attribute Category Vocabulary

Hardware
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in defined term set: Apache Spark vs DuckDB — Attribute Category Vocabulary

Capabilities
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term code: capabilities
in defined term set: Apache Spark vs DuckDB — Attribute Category Vocabulary

Maintenance
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term code: maintenance
in defined term set: Apache Spark vs DuckDB — Attribute Category Vocabulary

Resource Usage
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term code: resource-usage
in defined term set: Apache Spark vs DuckDB — Attribute Category Vocabulary

Concurrency
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term code: concurrency
in defined term set: Apache Spark vs DuckDB — Attribute Category Vocabulary

Storage Efficiency
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term code: storage-efficiency
in defined term set: Apache Spark vs DuckDB — Attribute Category Vocabulary

Feature Completeness
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in defined term set: Apache Spark vs DuckDB — Attribute Category Vocabulary

Data Integrity
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API Completeness
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Community Support
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in defined term set: Apache Spark vs DuckDB — Attribute Category Vocabulary

SQL Features
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in defined term set: Apache Spark vs DuckDB — Attribute Category Vocabulary

Workflow Speed
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Capacity
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Quotas
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Convenience
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Query Language
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Operability
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faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-09T06:14:24.389Z
author: A Versus B
publisher: A Versus B
is part of: apache-spark-vs-duckdb)
main entity: When should I use DuckDB instead of Spark?
  type: Question
  accepted answer: Answer
    text: Use DuckDB when your dataset is under 1TB, you need interactive query responses (sub-second latency), you want zero infrastructure overhead, or you're building an embedded analytics feature in an application. DuckDB's 100-500ms latency is 10-100x faster than Spark's 5-30 second queries on equivalent datasets. For single-analyst exploration or small team BI, DuckDB eliminates cluster management entirely.
    language: en-US
main entity: Can DuckDB replace Spark for production analytics?
  type: Question
  accepted answer: Answer
    text: DuckDB can replace Spark for production analytics pipelines on datasets under 1TB, but not for petabyte-scale processing or cross-organization distributed computing. DuckDB is production-ready for embedded analytics, API-backed dashboards, and data science workflows. If your data exceeds 1TB or requires horizontal scaling across 100+ nodes, Spark remains necessary.
    language: en-US
main entity: What's the cost difference between Spark and DuckDB?
  type: Question
  accepted answer: Answer
    text: DuckDB is free and open-source with minimal infrastructure costs (runs on laptops or small VMs). Spark is free but requires significant infrastructure: a 10-node cluster costs $500-2,000/month on AWS (EC2 + EMR). Enterprise Spark support (Databricks) adds $30,000-100,000+ annually. For sub-1TB analytics, DuckDB's zero infrastructure cost saves 95% compared to Spark clusters.
    language: en-US
main entity: How do the query languages compare?
  type: Question
  accepted answer: Answer
    text: Both support standard SQL, but Spark also supports Scala, Python, R, and Java DataFrames with distributed operations. DuckDB supports SQL plus bindings for Python, R, C++, and Go. For pure SQL analytics, both are equivalent. For complex transformations, Spark's multi-language ecosystem (especially Scala and PySpark) offers more flexibility than DuckDB's Python focus.
    language: en-US
main entity: Can I migrate from Spark to DuckDB or vice versa?
  type: Question
  accepted answer: Answer
    text: Migrating from Spark to DuckDB is straightforward for queries under 1TB: Parquet and CSV files load directly into DuckDB without conversion. Migrating from DuckDB to Spark requires packaging your data in distributed format (Parquet shards across HDFS/S3). Most migrations go Spark → DuckDB for cost/latency savings. Reverse migrations (DuckDB → Spark) occur when data grows beyond 1TB.
    language: en-US

How to Choose Between Apache Spark and DuckDB
type: HowTo
description: Apache Spark is a distributed computing framework designed for large-scale data processing across clusters, while DuckDB is an in-process SQL database optimized for analytical queries on single machines or small clusters. Spark handles petabyte-scale data; DuckDB excels at sub-terabyte interactive analytics with minimal overhead.
language: en-US
total time: PT3M
step: Consider: Processing Architecture
step: Consider: Ideal Data Size
step: Consider: Query Latency (100GB dataset)
step: Consider: Setup Complexity
step: Consider: Memory Efficiency on 10GB Query
step: Consider: Supported Languages
step: Consider: Enterprise Ecosystem
tool: A Versus B Comparison Guide
tip: HowToTip
  text: Choose Apache Spark if you're processing data across 100GB+ datasets, need distributed computing across multiple machines, or require enterprise support and a mature ecosystem. Choose DuckDB if you need fast interactive analytics on datasets under 1TB, want zero infrastructure overhead, prefer minimal setup time, or are building analytical applications that embed a database engine.

Consider: Processing Architecture
type: HowToStep
position: 1
text: Processing Architecture: Apache Spark has Distributed cluster-based (master-worker), while DuckDB has In-process single-machine or shared-memory.

Consider: Ideal Data Size
type: HowToStep
position: 2
text: Ideal Data Size: Apache Spark has 100GB to petabytes, while DuckDB has 1MB to 1TB.

Consider: Query Latency (100GB dataset)
type: HowToStep
position: 3
text: Query Latency (100GB dataset): Apache Spark has 5-30 seconds (cluster overhead), while DuckDB has 100-500ms (in-process).

Consider: Setup Complexity
type: HowToStep
position: 4
text: Setup Complexity: Apache Spark has High (requires cluster, Hadoop/Kubernetes), while DuckDB has Minimal (single binary, no dependencies).

Consider: Memory Efficiency on 10GB Query
type: HowToStep
position: 5
text: Memory Efficiency on 10GB Query: Apache Spark has 2-4GB required (JVM overhead), while DuckDB has 500MB-1GB required.

Consider: Supported Languages
type: HowToStep
position: 6
text: Supported Languages: Apache Spark has Scala, Python, SQL, R, Java (6 languages), while DuckDB has SQL, Python, R, C++, Go (5 languages).

Consider: Enterprise Ecosystem
type: HowToStep
position: 7
text: Enterprise Ecosystem: Apache Spark has Mature (Databricks, AWS EMR, Cloudera), while DuckDB has Growing (DuckLabs, open-source focus).

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: Apache Spark vs DuckDB
review body: Apache Spark is a distributed computing framework designed for large-scale data processing across clusters, while DuckDB is an in-process SQL database optimized for analytical queries on single machines or small clusters. Spark handles petabyte-scale data; DuckDB excels at sub-terabyte interactive analytics with minimal overhead.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: Apache Spark vs DuckDB

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

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

Processing Speed (Iterative Query)
type: PropertyValue
value: Apache Spark: 0.5-2 seconds | DuckDB: N/A

Memory Requirement
type: PropertyValue
value: Apache Spark: 8-64 GB per node | DuckDB: N/A

Supported Languages
type: PropertyValue
value: Apache Spark: 5 (Scala, Python, Java, R, SQL) | DuckDB: 7 (Python, Node.js, Go, Rust, R, Java, C++)
value reference: DuckDB

Real-time Processing
type: PropertyValue
value: Apache Spark: 100-500 ms (micro-batch) | DuckDB: N/A

Ecosystem Age
type: PropertyValue
value: Apache Spark: 12 years (since 2013) | DuckDB: N/A

Enterprise Adoption
type: PropertyValue
value: Apache Spark: 74% currently use | DuckDB: N/A

Machine Learning Capability
type: PropertyValue
value: Apache Spark: Full MLlib with algorithms, pipelines | DuckDB: N/A

Data Storage Redundancy
type: PropertyValue
value: Apache Spark: Depends on underlying storage | DuckDB: N/A

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

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

Memory Per Node
type: PropertyValue
value: Apache Spark: 8-12GB (caching overhead) | DuckDB: 2-64 (varies)
value reference: Apache Spark

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

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

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

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

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

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

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

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

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

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

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

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

Minimum Hardware Requirements
type: PropertyValue
value: Apache Spark: 8GB (per node, 3+ nodes recommended) | DuckDB: 512MB standalone
value reference: DuckDB

Query Latency on 100GB Dataset
type: PropertyValue
value: Apache Spark: 5-30 seconds | DuckDB: 0.1-0.5 seconds
value reference: DuckDB

Maximum Practical Data Size
type: PropertyValue
value: Apache Spark: 100,000+ (petabyte scale) | DuckDB: 1,000
value reference: Apache Spark

Memory Usage for 10GB Query
type: PropertyValue
value: Apache Spark: 2-4GB | DuckDB: 0.5-1GB
value reference: DuckDB

Time to First Query (fresh install)
type: PropertyValue
value: Apache Spark: 45-120 (including cluster setup) | DuckDB: 2-5 (download and run)
value reference: DuckDB

Number of Supported Languages
type: PropertyValue
value: Apache Spark: 6 (Scala, Python, SQL, R, Java, Go) | DuckDB: 5 (SQL, Python, R, C++, Go)
value reference: Apache Spark

Community GitHub Stars (2026)
type: PropertyValue
value: Apache Spark: 39,500+ | DuckDB: 21,800+
value reference: Apache Spark

Query Processing Throughput (GBps)
type: PropertyValue
value: Apache Spark: 1-10 (cluster dependent) | DuckDB: 10-100 (vectorized)
value reference: DuckDB

Typical Processing Speed (iterative queries)
type: PropertyValue
value: Apache Spark: 2-5 seconds | DuckDB: N/A

Memory Requirements per Node
type: PropertyValue
value: Apache Spark: 16-64 GB (in-memory caching) | DuckDB: N/A

Real-time Streaming Support
type: PropertyValue
value: Apache Spark: Native (Spark Streaming, Structured Streaming) | DuckDB: N/A

Production Deployments (estimated 2024)
type: PropertyValue
value: Apache Spark: 450,000+ globally | DuckDB: N/A

Setup Time (basic cluster)
type: PropertyValue
value: Apache Spark: 1-2 hours | DuckDB: N/A

Active Development (2024-2026)
type: PropertyValue
value: Apache Spark: Highly active (Apache Foundation priority) | DuckDB: N/A

Approximate Learning Time for Developers
type: PropertyValue
value: Apache Spark: 2-4 weeks | DuckDB: N/A

First Release Year
type: PropertyValue
value: Apache Spark: 2014 | DuckDB: 2019
value reference: Apache Spark

Maximum Cluster Size
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 1 (single machine)

Query Latency (1GB aggregation)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 10-50ms

Compression Ratio (typical)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 4:1 to 8:1

Memory Required (minimal)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 10-50MB

Ingest Throughput
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 10-50 million rows/sec

SQL Standard Compliance
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 95% ANSI SQL

Aggregation Query Time (1 billion rows)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 0.5-2 seconds

Memory Usage (1TB analytical dataset)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 10-50 GB

ACID Compliance Level
type: PropertyValue
value: Apache Spark: N/A | DuckDB: Partial (batch insert-optimized)

Concurrent Write Support
type: PropertyValue
value: Apache Spark: N/A | DuckDB: Single-threaded writes only

Years in Production
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 5 years (since 2019)

Native Format Support
type: PropertyValue
value: Apache Spark: N/A | DuckDB: Parquet, CSV, JSON, Iceberg, Hugging Face

Database File Size Limit
type: PropertyValue
value: Apache Spark: N/A | DuckDB: Unlimited

Production Deployments (Estimated)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: Growing (100K+)

Typical Maximum Dataset Size
type: PropertyValue
value: Apache Spark: N/A | DuckDB: ~100 GB

Query Latency (100M rows, simple aggregation)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 50-200ms

Idle Memory Usage
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 50-100 MB

Data Compression Ratio
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 5-8x

Aggregation Query Speed (10M rows)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 2.3s

Memory Usage (1GB dataset)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 450MB

SQL Standard Coverage
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 95%

ACID Transactions
type: PropertyValue
value: Apache Spark: N/A | DuckDB: Fully supported

Core Language
type: PropertyValue
value: Apache Spark: N/A | DuckDB: C++ (Rust bindings available)

Language Bindings Supported
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 5 (Python, R, Java, Node.js, Go)

Latest Stable Version
type: PropertyValue
value: Apache Spark: N/A | DuckDB: v0.10.0 (2024)

Total Cost of Ownership (Annual, 100TB dataset)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: $0

Setup Time to First Query
type: PropertyValue
value: Apache Spark: N/A | DuckDB: < 1 minute

Query Latency (10GB dataset, simple aggregate)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 0.3 seconds

Query Latency (1TB dataset, complex join)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 3-5 seconds

Maximum Supported Dataset Size
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 2 TB (local)

Concurrent User Queries
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 1-5 simultaneous

Built-in Machine Learning Capabilities
type: PropertyValue
value: Apache Spark: N/A | DuckDB: No (requires external integration)

GitHub Stars (Community Traction)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 18,500+

Setup Time (Minutes)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 5-10

Query Latency on 1GB Dataset
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 10-50

Minimum Cluster Nodes Required
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 1

Real-time Streaming Ingestion
type: PropertyValue
value: Apache Spark: N/A | DuckDB: Batch-focused only

Concurrent Queries Supported
type: PropertyValue
value: Apache Spark: N/A | DuckDB: Limited by single machine

Annual Infrastructure Cost (1TB dataset)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 0-5,000

Query Performance on 10GB Parquet File (GROUP BY aggregation)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 1.2 seconds

Memory Usage (10GB dataset analysis)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 2.1 GB (with compression)

Startup/Import Time
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 45ms (lightweight binary)

Number of Built-in Data Transformation Methods
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 65 SQL functions + standard

Stack Overflow Questions (as of 2026)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 8.2K questions

Maximum Dataset Size (without disk streaming)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 1000+ GB (out-of-core)

SQL Window Function Support
type: PropertyValue
value: Apache Spark: N/A | DuckDB: Yes (ROW_NUMBER, LAG, LEAD, RANK, etc.)

Time to Analyze 100MB CSV (end-to-end)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 3.8 seconds

Base Monthly Cost
type: PropertyValue
value: Apache Spark: N/A | DuckDB: Free

Free Tier Storage
type: PropertyValue
value: Apache Spark: N/A | DuckDB: Unlimited (disk-dependent)

Free Tier Row Reads/Month
type: PropertyValue
value: Apache Spark: N/A | DuckDB: Unlimited

Global Edge Locations
type: PropertyValue
value: Apache Spark: N/A | DuckDB: None (local only)

OLAP Query Speed (1GB dataset)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 50-100ms

Replication Latency
type: PropertyValue
value: Apache Spark: N/A | DuckDB: Not supported

Installation Required
type: PropertyValue
value: Apache Spark: N/A | DuckDB: No (embedded library)

Ingestion Rate (events/second)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 50,000

Query Latency (1B rows)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 0.5-2

Maximum Recommended Dataset Size
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 1

Deployment Time
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 0.08

Minimum Cluster Size
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 1

Multi-node Support
type: PropertyValue
value: Apache Spark: N/A | DuckDB: No (single-node only)

Real-time Upsert Support
type: PropertyValue
value: Apache Spark: N/A | DuckDB: No (batch only)

Query Speed (1GB CSV aggregation)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 1.2 seconds

Maximum Practical Dataset Size
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 100+ GB

Memory Usage (1GB CSV load)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 200 MB (compressed)

Built-in Statistical Functions
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 200+

SQL Support Level
type: PropertyValue
value: Apache Spark: N/A | DuckDB: Full ANSI SQL + extensions

Learning Curve (1-10 scale)
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 7 (requires SQL)

Stack Overflow Questions Answered
type: PropertyValue
value: Apache Spark: N/A | DuckDB: 3,200

SpeakableSpecification
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Apache Spark — Wikipedia
type: CreativeWork

DuckDB — Wikipedia
type: CreativeWork

Compare Apache Spark vs DuckDB
type: Action
additional type: CompareAction
target: https://www.aversusb.net/compare/apache-spark-vs-duckdb)
object: Apache Spark
object: DuckDB

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

Maximum Cluster Size
type: PropertyValue
value reference: Scalability

Query Latency (1GB aggregation)
type: PropertyValue
value reference: Performance

Compression Ratio (typical)
type: PropertyValue
value reference: Efficiency

Memory Required (minimal)
type: PropertyValue
value reference: Resource Usage

Ingest Throughput
type: PropertyValue
value reference: Performance

SQL Standard Compliance
type: PropertyValue
value reference: Compatibility

Aggregation Query Time (1 billion rows)
type: PropertyValue
value reference: Performance

Memory Usage (1TB analytical dataset)
type: PropertyValue
value reference: Resource Efficiency

ACID Compliance Level
type: PropertyValue
value reference: Reliability

Concurrent Write Support
type: PropertyValue
value reference: Concurrency

Years in Production
type: PropertyValue
value reference: Maturity

Native Format Support
type: PropertyValue
value reference: Features

Database File Size Limit
type: PropertyValue
value reference: Scalability

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

Typical Maximum Dataset Size
type: PropertyValue
value reference: Scalability

Query Latency (100M rows, simple aggregation)
type: PropertyValue
value reference: Performance

Idle Memory Usage
type: PropertyValue
value reference: Resource Efficiency

Data Compression Ratio
type: PropertyValue
value reference: Storage Efficiency

Aggregation Query Speed (10M rows)
type: PropertyValue
value reference: Performance

Memory Usage (1GB dataset)
type: PropertyValue
value reference: Resource Efficiency

SQL Standard Coverage
type: PropertyValue
value reference: Feature Completeness

ACID Transactions
type: PropertyValue
value reference: Data Integrity

Core Language
type: PropertyValue
value reference: Implementation

Language Bindings Supported
type: PropertyValue
value reference: Ecosystem

Latest Stable Version
type: PropertyValue
value reference: Development

Total Cost of Ownership (Annual, 100TB dataset)
type: PropertyValue
value reference: Cost

Setup Time to First Query
type: PropertyValue
value reference: Ease of Use

Query Latency (10GB dataset, simple aggregate)
type: PropertyValue
value reference: Performance

Query Latency (1TB dataset, complex join)
type: PropertyValue
value reference: Performance

Maximum Supported Dataset Size
type: PropertyValue
value reference: Scalability

Concurrent User Queries
type: PropertyValue
value reference: Scalability

Built-in Machine Learning Capabilities
type: PropertyValue
value reference: Features

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

Setup Time (Minutes)
type: PropertyValue
value reference: Ease of Use

Query Latency on 1GB Dataset
type: PropertyValue
value reference: Performance

Minimum Cluster Nodes Required
type: PropertyValue
value reference: Infrastructure

Real-time Streaming Ingestion
type: PropertyValue
value reference: Features

Concurrent Queries Supported
type: PropertyValue
value reference: Performance

Annual Infrastructure Cost (1TB dataset)
type: PropertyValue
value reference: Cost

Query Performance on 10GB Parquet File (GROUP BY aggregation)
type: PropertyValue
value reference: Performance

Memory Usage (10GB dataset analysis)
type: PropertyValue
value reference: Resource Efficiency

Startup/Import Time
type: PropertyValue
value reference: Performance

Number of Built-in Data Transformation Methods
type: PropertyValue
value reference: API Completeness

Stack Overflow Questions (as of 2026)
type: PropertyValue
value reference: Community Support

Maximum Dataset Size (without disk streaming)
type: PropertyValue
value reference: Scalability

SQL Window Function Support
type: PropertyValue
value reference: SQL Features

Time to Analyze 100MB CSV (end-to-end)
type: PropertyValue
value reference: Workflow Speed

Base Monthly Cost
type: PropertyValue
value reference: Pricing

Free Tier Storage
type: PropertyValue
value reference: Capacity

Free Tier Row Reads/Month
type: PropertyValue
value reference: Quotas

Global Edge Locations
type: PropertyValue
value reference: Infrastructure

OLAP Query Speed (1GB dataset)
type: PropertyValue
value reference: Performance

Replication Latency
type: PropertyValue
value reference: Performance

Installation Required
type: PropertyValue
value reference: Convenience

Ingestion Rate (events/second)
type: PropertyValue
value reference: Performance

Query Latency (1B rows)
type: PropertyValue
value reference: Performance

Maximum Recommended Dataset Size
type: PropertyValue
value reference: Performance

Deployment Time
type: PropertyValue
value reference: Performance

Minimum Cluster Size
type: PropertyValue
value reference: Infrastructure

Multi-node Support
type: PropertyValue
value reference: Architecture

Real-time Upsert Support
type: PropertyValue
value reference: Features

Query Speed (1GB CSV aggregation)
type: PropertyValue
value reference: Performance

Maximum Practical Dataset Size
type: PropertyValue
value reference: Scalability

Memory Usage (1GB CSV load)
type: PropertyValue
value reference: Efficiency

Built-in Statistical Functions
type: PropertyValue
value reference: Features

SQL Support Level
type: PropertyValue
value reference: Query Language

Learning Curve (1-10 scale)
type: PropertyValue
value reference: Operability

Stack Overflow Questions Answered
type: PropertyValue
value reference: Community