DuckDB vs Apache Spark
type: Article
description: DuckDB is a lightweight, single-machine analytical database optimized for OLAP queries on local data with sub-second response times, while Apache Spark is a distributed computing framework designed for large-scale data processing across clusters with support for batch, streaming, and ML workloads. DuckDB excels at interactive analytics on datasets under 100GB, while Spark handles petabyte-scale distributed processing.
language: en-US
date published: 2026-07-09T13:35:35.714Z
date created: 2026-07-09T13:35:35.714Z
date modified: 2026-07-09T13:35:35.768Z
content reference time: 2026-07-09T13:35:35.768Z
author: A Versus B
publisher: A Versus B
about: DuckDB
about: Apache Spark
abstract: DuckDB is a lightweight, single-machine analytical database optimized for OLAP queries on local data with sub-second response times, while Apache Spark is a distributed computing framework designed for large-scale data processing across clusters with support for batch, streaming, and ML workloads. DuckDB excels at interactive analytics on datasets under 100GB, while Spark handles petabyte-scale distributed processing.
text: Architecture. Query Latency (typical OLAP query). Maximum Data Scale. Setup Complexity. Memory Usage (typical). Supported Data Formats. Learning Curve
conclusion text: Choose DuckDB if you're performing interactive analytics on local or cloud-connected datasets under 1TB, need fast query responses, and want zero operational overhead. Choose Apache Spark if you need to process petabyte-scale data across distributed clusters, require streaming capabilities, or need production-grade fault tolerance and multi-tenancy support.
additional property: Maximum Cluster Size
  type: PropertyValue
  value: DuckDB: 1 (single machine) | Apache Spark: N/A
additional property: Query Latency (1GB aggregation)
  type: PropertyValue
  value: DuckDB: 10-50ms | Apache Spark: N/A
additional property: Compression Ratio (typical)
  type: PropertyValue
  value: DuckDB: 4:1 to 8:1 | Apache Spark: N/A
additional property: Memory Required (minimal)
  type: PropertyValue
  value: DuckDB: 10-50MB | Apache Spark: N/A
additional property: Ingest Throughput
  type: PropertyValue
  value: DuckDB: 10-50 million rows/sec | Apache Spark: N/A
additional property: SQL Standard Compliance
  type: PropertyValue
  value: DuckDB: 95% standard SQL | Apache Spark: N/A
additional property: GitHub Stars (2026)
  type: PropertyValue
  value: DuckDB: 18,500+ | Apache Spark: 35,900 stars
  value reference: Apache Spark
additional property: Aggregation Query Time (1 billion rows)
  type: PropertyValue
  value: DuckDB: 0.5-2 seconds | Apache Spark: N/A
additional property: Memory Usage (1TB analytical dataset)
  type: PropertyValue
  value: DuckDB: 10-50 GB | Apache Spark: N/A
additional property: ACID Compliance Level
  type: PropertyValue
  value: DuckDB: Partial (batch insert-optimized) | Apache Spark: N/A
additional property: Concurrent Write Support
  type: PropertyValue
  value: DuckDB: Single-threaded writes only | Apache Spark: N/A
additional property: Years in Production
  type: PropertyValue
  value: DuckDB: 5 years (since 2019) | Apache Spark: N/A
additional property: Native Format Support
  type: PropertyValue
  value: DuckDB: Parquet, CSV, JSON, Iceberg, Hugging Face | Apache Spark: N/A
additional property: Database File Size Limit
  type: PropertyValue
  value: DuckDB: Unlimited | Apache Spark: N/A
additional property: Production Deployments (Estimated)
  type: PropertyValue
  value: DuckDB: Growing (100K+) | Apache Spark: N/A
additional property: Typical Maximum Dataset Size
  type: PropertyValue
  value: DuckDB: ~100 GB | Apache Spark: N/A
additional property: Query Latency (100M rows, simple aggregation)
  type: PropertyValue
  value: DuckDB: 50-200ms | Apache Spark: N/A
additional property: Idle Memory Usage
  type: PropertyValue
  value: DuckDB: 50-100 MB | Apache Spark: N/A
additional property: Supported Data Formats
  type: PropertyValue
  value: DuckDB: 12+ formats | Apache Spark: Parquet, ORC, JSON, CSV, Avro, Delta (via library)
additional property: Typical Query Latency (1GB dataset)
  type: PropertyValue
  value: DuckDB: 50-200ms | Apache Spark: 2000-5000ms
  value reference: DuckDB
additional property: Maximum Practical Data Size
  type: PropertyValue
  value: DuckDB: 1,000 | Apache Spark: 100,000+ (petabyte scale)
  value reference: Apache Spark
additional property: Memory Required Per Query
  type: PropertyValue
  value: DuckDB: 10-50MB | Apache Spark: 500-2000MB
  value reference: DuckDB
additional property: Setup Time for Basic Analytics
  type: PropertyValue
  value: DuckDB: 1-5 minutes | Apache Spark: 30-120 minutes
  value reference: DuckDB
additional property: Primary Language Support
  type: PropertyValue
  value: DuckDB: Python, SQL, C++, R, Julia, Node.js | Apache Spark: Python, Scala, SQL, R, Java
additional property: Query Latency (1GB CSV)
  type: PropertyValue
  value: DuckDB: 150-500ms | Apache Spark: 8,000-15,000ms
  value reference: DuckDB
additional property: Maximum Scalable Dataset Size
  type: PropertyValue
  value: DuckDB: 10-50 | Apache Spark: 1,000+ PB
  value reference: Apache Spark
additional property: Setup Time (from scratch)
  type: PropertyValue
  value: DuckDB: 2-5 (local install) | Apache Spark: 60-120 (cluster setup)
  value reference: DuckDB
additional property: Multi-machine Distributed Computing
  type: PropertyValue
  value: DuckDB: Not supported | Apache Spark: Native support
additional property: Fault Tolerance
  type: PropertyValue
  value: DuckDB: No (single machine) | Apache Spark: Yes (RDD lineage-based)
additional property: Aggregation Query Speed (10M rows)
  type: PropertyValue
  value: DuckDB: 2.3s | Apache Spark: N/A
additional property: Memory Usage (1GB dataset)
  type: PropertyValue
  value: DuckDB: 450MB | Apache Spark: N/A
additional property: SQL Standard Coverage
  type: PropertyValue
  value: DuckDB: 95% | Apache Spark: N/A
additional property: ACID Transactions
  type: PropertyValue
  value: DuckDB: Fully supported | Apache Spark: N/A
additional property: Core Language
  type: PropertyValue
  value: DuckDB: C++ (Rust bindings available) | Apache Spark: N/A
additional property: Language Bindings Supported
  type: PropertyValue
  value: DuckDB: 5 (Python, R, Java, Node.js, Go) | Apache Spark: N/A
additional property: Latest Stable Version
  type: PropertyValue
  value: DuckDB: v0.10.0 (2024) | Apache Spark: N/A
additional property: Total Cost of Ownership (Annual, 100TB dataset)
  type: PropertyValue
  value: DuckDB: $0 | Apache Spark: N/A
additional property: Setup Time to First Query
  type: PropertyValue
  value: DuckDB: < 1 minute | Apache Spark: N/A
additional property: Query Latency (10GB dataset, simple aggregate)
  type: PropertyValue
  value: DuckDB: 0.3 seconds | Apache Spark: N/A
additional property: Query Latency (1TB dataset, complex join)
  type: PropertyValue
  value: DuckDB: 3-5 seconds | Apache Spark: N/A
additional property: Maximum Supported Dataset Size
  type: PropertyValue
  value: DuckDB: 2 TB (local) | Apache Spark: N/A
additional property: Concurrent User Queries
  type: PropertyValue
  value: DuckDB: 1-5 simultaneous | Apache Spark: N/A
additional property: Built-in Machine Learning Capabilities
  type: PropertyValue
  value: DuckDB: No (requires external integration) | Apache Spark: N/A
additional property: GitHub Stars (Community Traction)
  type: PropertyValue
  value: DuckDB: 18,500+ | Apache Spark: N/A
additional property: Setup Time (Minutes)
  type: PropertyValue
  value: DuckDB: 5-10 | Apache Spark: N/A
additional property: Query Latency on 1GB Dataset
  type: PropertyValue
  value: DuckDB: 10-50 | Apache Spark: N/A
additional property: Minimum Cluster Nodes Required
  type: PropertyValue
  value: DuckDB: 1 | Apache Spark: N/A
additional property: Real-time Streaming Ingestion
  type: PropertyValue
  value: DuckDB: Batch-focused only | Apache Spark: N/A
additional property: Concurrent Queries Supported
  type: PropertyValue
  value: DuckDB: Limited by single machine | Apache Spark: N/A
interactivity type: active
significant link: https://www.aversusb.net/entity/duckdb
significant link: https://www.aversusb.net/alternatives/duckdb
significant link: https://www.aversusb.net/entity/apache-spark
significant link: https://www.aversusb.net/alternatives/apache-spark
significant link: https://www.aversusb.net/api/answer/duckdb-vs-spark)
significant link: https://www.aversusb.net/api/v1/schema/duckdb-vs-spark)
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-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
teaches: How to choose between DuckDB and Apache Spark
genre: Software

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

A Versus B
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Apache Spark
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DuckDB vs Apache Spark — Structured Comparison Data
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is based on: https://www.aversusb.net/compare/duckdb-vs-spark)

DuckDB vs Apache Spark — Attribute Category Vocabulary
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has defined term: Maintenance
has defined term: Developer Experience

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term code: scalability
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term code: performance
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term code: efficiency
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Compatibility
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term code: compatibility
in defined term set: DuckDB vs Apache Spark — Attribute Category Vocabulary

Community
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term code: community
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Resource Efficiency
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term code: resource-efficiency
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Reliability
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term code: reliability
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Concurrency
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term code: concurrency
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term code: features
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Query Language
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Setup
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term code: deployment
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Use Case
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ML Capabilities
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History
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Functionality
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SQL Support
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Hardware
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Capabilities
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Developer Experience
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faq
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language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-09T13:35:35.768Z
author: A Versus B
publisher: A Versus B
is part of: duckdb-vs-spark)
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 response times (sub-second), you're working locally or with a single machine, and you want zero infrastructure overhead. DuckDB is ideal for data exploration, ad-hoc analytics, and notebooks. Spark is overkill for these use cases and adds unnecessary complexity.
    language: en-US
main entity: Can DuckDB handle streaming data like Spark?
  type: Question
  accepted answer: Answer
    text: No, DuckDB is designed for analytical SQL on static/batch data. It does not natively support streaming ingestion. Apache Spark has Structured Streaming for real-time data processing. If you need streaming, Spark is the right choice, or consider complementing DuckDB with a separate streaming system like Kafka.
    language: en-US
main entity: Is DuckDB production-ready?
  type: Question
  accepted answer: Answer
    text: Yes, DuckDB 1.0 reached production stability in 2024. However, its production use case is limited to single-machine deployments. Multi-node clustering (Motherduck) is in beta. For mission-critical distributed systems, Spark has more mature production deployments across thousands of organizations.
    language: en-US
main entity: What's the cost difference between DuckDB and Spark?
  type: Question
  accepted answer: Answer
    text: DuckDB is open-source and free. Apache Spark is also open-source and free, but requires infrastructure costs (servers, clusters, cloud resources). Motherduck (DuckDB's cloud offering) costs $0.005 per GB scanned. Databricks (Spark managed service) costs significantly more. For pure software, both are free; costs come from infrastructure.
    language: en-US
main entity: Can I migrate from Spark to DuckDB or vice versa?
  type: Question
  accepted answer: Answer
    text: Partial migration is possible. DuckDB uses standard SQL, so Spark SQL queries often work directly. However, Spark's distributed execution model, RDDs, and complex transformations don't translate to DuckDB. You can use DuckDB for small datasets, then scale to Spark if needed. The reverse (Spark to DuckDB) is not practical for large-scale systems.
    language: en-US

How to Choose Between DuckDB and Apache Spark
type: HowTo
description: DuckDB is a lightweight, single-machine analytical database optimized for OLAP queries on local data with sub-second response times, while Apache Spark is a distributed computing framework designed for large-scale data processing across clusters with support for batch, streaming, and ML workloads. DuckDB excels at interactive analytics on datasets under 100GB, while Spark handles petabyte-scale distributed processing.
language: en-US
total time: PT3M
step: Consider: Architecture
step: Consider: Query Latency (typical OLAP query)
step: Consider: Maximum Data Scale
step: Consider: Setup Complexity
step: Consider: Memory Usage (typical)
step: Consider: Supported Data Formats
step: Consider: Learning Curve
tool: A Versus B Comparison Guide
tip: HowToTip
  text: Choose DuckDB if you're performing interactive analytics on local or cloud-connected datasets under 1TB, need fast query responses, and want zero operational overhead. Choose Apache Spark if you need to process petabyte-scale data across distributed clusters, require streaming capabilities, or need production-grade fault tolerance and multi-tenancy support.

Consider: Architecture
type: HowToStep
position: 1
text: Architecture: DuckDB has Single-machine in-process database, while Apache Spark has Distributed cluster computing framework.

Consider: Query Latency (typical OLAP query)
type: HowToStep
position: 2
text: Query Latency (typical OLAP query): DuckDB has 50-500ms, while Apache Spark has 5-30 seconds.

Consider: Maximum Data Scale
type: HowToStep
position: 3
text: Maximum Data Scale: DuckDB has 100GB-1TB (practical limit), while Apache Spark has Multiple petabytes.

Consider: Setup Complexity
type: HowToStep
position: 4
text: Setup Complexity: DuckDB has Zero configuration, pip install duckdb, while Apache Spark has Requires cluster setup, YARN/Kubernetes.

Consider: Memory Usage (typical)
type: HowToStep
position: 5
text: Memory Usage (typical): DuckDB has 100MB-2GB for operations, while Apache Spark has 2GB+ per executor minimum.

Consider: Supported Data Formats
type: HowToStep
position: 6
text: Supported Data Formats: DuckDB has Parquet, CSV, JSON, Arrow, SQLite, while Apache Spark has Parquet, ORC, CSV, Avro, Delta Lake, Iceberg.

Consider: Learning Curve
type: HowToStep
position: 7
text: Learning Curve: DuckDB has Minimal - standard SQL, Python/R integration, while Apache Spark has Steep - requires understanding of RDDs, DAGs, cluster concepts.

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: DuckDB vs Apache Spark
review body: DuckDB is a lightweight, single-machine analytical database optimized for OLAP queries on local data with sub-second response times, while Apache Spark is a distributed computing framework designed for large-scale data processing across clusters with support for batch, streaming, and ML workloads. DuckDB excels at interactive analytics on datasets under 100GB, while Spark handles petabyte-scale distributed processing.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: DuckDB vs Apache Spark

Supported Programming Languages
type: PropertyValue
value: DuckDB: Python, R, Java, C++, Node.js, Go | Apache Spark: Python, Scala, Java, R, SQL
value reference: DuckDB

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Setup Time (fresh install to first query)
type: PropertyValue
value: DuckDB: 2 minutes | Apache Spark: 60-120 minutes
value reference: DuckDB

Minimum Memory Requirement
type: PropertyValue
value: DuckDB: 0.1GB | Apache Spark: 2GB (per executor)
value reference: DuckDB

Single Query Latency (10GB dataset)
type: PropertyValue
value: DuckDB: 0.3 seconds | Apache Spark: 8 seconds
value reference: DuckDB

Maximum Practical Data Scale
type: PropertyValue
value: DuckDB: 1TB | Apache Spark: 1000TB+ (petabytes)
value reference: Apache Spark

Supported SQL Features
type: PropertyValue
value: DuckDB: 95% | Apache Spark: 85%
value reference: DuckDB

Community Contributors (2025)
type: PropertyValue
value: DuckDB: 280+ contributors | Apache Spark: 1500+ contributors
value reference: Apache Spark

GitHub Stars
type: PropertyValue
value: DuckDB: 22000 stars | Apache Spark: 40000 stars
value reference: Apache Spark

Time to Learn (for SQL users)
type: PropertyValue
value: DuckDB: 4 hours | Apache Spark: 40 hours
value reference: DuckDB

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Query Latency (1B rows, COUNT aggregation)
type: PropertyValue
value: DuckDB: 80-150ms | Apache Spark: N/A

Data Compression Ratio
type: PropertyValue
value: DuckDB: 4-8x compression | Apache Spark: N/A

Operational Complexity
type: PropertyValue
value: DuckDB: 2 (minimal) | Apache Spark: N/A

Community Size (GitHub Stars)
type: PropertyValue
value: DuckDB: 20,000+ stars | Apache Spark: N/A

License Restrictions
type: PropertyValue
value: DuckDB: MIT - No restrictions | Apache Spark: N/A

Initial Licensing Cost
type: PropertyValue
value: DuckDB: N/A | Apache Spark: $0

Setup Time to Production
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 6-12 weeks

Cluster Management Required
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 40-80 hours (dedicated DevOps engineer)

SQL Query Performance (TPC-DS Benchmark)
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 45-120 seconds

Users Per Collaborative Project
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 1-5 (via Jupyter sharing)

Built-in Security Features
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 0 (manual implementation required)

Community Size
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 25,000+ questions

Typical Cluster Cost (Monthly)
type: PropertyValue
value: DuckDB: N/A | Apache Spark: $1,500-$5,000+

Data Processing Speed (1TB dataset)
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 5-15 minutes

Setup Time for Production Deployment
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 40-80 hours

Supported Warehouse Platforms
type: PropertyValue
value: DuckDB: N/A | Apache Spark: Hadoop, Kubernetes, cloud object storage (3+ classes)

Built-in Data Testing Features
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 0 (requires external frameworks)

Minimum Dataset Size for Optimal Use
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 100+ GB

GitHub Community (Stars)
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 38.5K stars

Query Performance on 1TB Dataset
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 30-120 seconds

Cluster Setup Time
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 40-80 hours

Cost per Core-Hour
type: PropertyValue
value: DuckDB: N/A | Apache Spark: $0.035-0.15

Supported Languages/APIs
type: PropertyValue
value: DuckDB: N/A | Apache Spark: Python, Scala, Java, SQL, R

Maximum Dataset Size Supported
type: PropertyValue
value: DuckDB: N/A | Apache Spark: Unlimited (depends on storage)

Cloud Provider Support
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 4+ (AWS, Azure, GCP, on-prem)

Machine Learning Algorithms Available
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 50+ (MLlib + custom models)

Data Format Support
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 8+ formats (Parquet, ORC, Avro, Delta, Iceberg, HDF5, CSV, JSON)

Processing Speed (Same 1TB dataset)
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 30-60 seconds (in-memory)

Processing Speed (Average Query)
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 10-60 seconds

Memory Requirement (Per Node)
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 16-256 GB

First Release
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 2014

Real-time Streaming Capability
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 500-5000 ms micro-batches

Market Adoption by Fortune 500
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 82%

Typical Cluster Cost (100-node setup)
type: PropertyValue
value: DuckDB: N/A | Apache Spark: $450,000-650,000

Fault Tolerance Method
type: PropertyValue
value: DuckDB: N/A | Apache Spark: Lineage-based recovery (RDD parents)

End-to-End Latency (p99)
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 500-2000ms

Native Connectors Available
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 200+ via ecosystem integrations

Enterprise Adoption Rate
type: PropertyValue
value: DuckDB: N/A | Apache Spark: 65% (Databricks, AWS, Google, Meta deployments)

Memory Overhead (per task)
type: PropertyValue
value: DuckDB: N/A | Apache Spark: ~400-800MB (GC overhead)

Supported Event Time Semantics
type: PropertyValue
value: DuckDB: N/A | Apache Spark: Partial in Structured Streaming, limited out-of-order support

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

SpeakableSpecification
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DuckDB — Wikipedia
type: CreativeWork

Apache Spark — Wikipedia
type: CreativeWork

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

Free Tier Storage
type: PropertyValue
value reference: Pricing

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

Supported Languages
type: PropertyValue
value reference: Localization

Installation Required
type: PropertyValue
value reference: Convenience

Setup Time (fresh install to first query)
type: PropertyValue
value reference: Operational

Minimum Memory Requirement
type: PropertyValue
value reference: Resources

Single Query Latency (10GB dataset)
type: PropertyValue
value reference: Performance

Maximum Practical Data Scale
type: PropertyValue
value reference: Scalability

Supported SQL Features
type: PropertyValue
value reference: Features

Community Contributors (2025)
type: PropertyValue
value reference: Community

GitHub Stars
type: PropertyValue
value reference: Community

Time to Learn (for SQL users)
type: PropertyValue
value reference: Learning

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: Scalability

Memory Per Node
type: PropertyValue
value reference: Resource Efficiency

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: Usability

Stack Overflow Questions Answered
type: PropertyValue
value reference: Community

First Release Year
type: PropertyValue
value reference: Maturity

Minimum Hardware Requirements
type: PropertyValue
value reference: Infrastructure

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

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

Setup Time
type: PropertyValue
value reference: Implementation

Maximum Dataset Size
type: PropertyValue
value reference: Scalability

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

Data Compression Ratio
type: PropertyValue
value reference: Storage Efficiency

Operational Complexity
type: PropertyValue
value reference: Operations

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

License Restrictions
type: PropertyValue
value reference: Licensing

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

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

Users Per Collaborative Project
type: PropertyValue
value reference: Collaboration

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

Community Size
type: PropertyValue
value reference: Community

Typical Cluster Cost (Monthly)
type: PropertyValue
value reference: Cost

Data Processing Speed (1TB dataset)
type: PropertyValue
value reference: Performance

Setup Time for Production Deployment
type: PropertyValue
value reference: Ease of Use

Supported Warehouse Platforms
type: PropertyValue
value reference: Compatibility

Built-in Data Testing Features
type: PropertyValue
value reference: Features

Minimum Dataset Size for Optimal Use
type: PropertyValue
value reference: Use Case

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

Query Performance on 1TB Dataset
type: PropertyValue
value reference: Performance

Cluster Setup Time
type: PropertyValue
value reference: Implementation

Cost per Core-Hour
type: PropertyValue
value reference: Pricing

Supported Languages/APIs
type: PropertyValue
value reference: Compatibility

Maximum Dataset Size Supported
type: PropertyValue
value reference: Performance

Cloud Provider Support
type: PropertyValue
value reference: Features

Machine Learning Algorithms Available
type: PropertyValue
value reference: ML Capabilities

Data Format Support
type: PropertyValue
value reference: Features

Processing Speed (Same 1TB dataset)
type: PropertyValue
value reference: Performance

Processing Speed (Average Query)
type: PropertyValue
value reference: Performance

Memory Requirement (Per Node)
type: PropertyValue
value reference: Infrastructure

First Release
type: PropertyValue
value reference: History

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

Market Adoption by Fortune 500
type: PropertyValue
value reference: Industry

Typical Cluster Cost (100-node setup)
type: PropertyValue
value reference: Cost

Fault Tolerance Method
type: PropertyValue
value reference: Reliability

End-to-End Latency (p99)
type: PropertyValue
value reference: Performance

Native Connectors Available
type: PropertyValue
value reference: Ecosystem

Enterprise Adoption Rate
type: PropertyValue
value reference: Industry Adoption

Memory Overhead (per task)
type: PropertyValue
value reference: Performance

Supported Event Time Semantics
type: PropertyValue
value reference: Features

Throughput (events/sec per node)
type: PropertyValue
value reference: Performance

Batch+Stream Unified Code
type: PropertyValue
value reference: Features

Processing Speed (Iterative Query)
type: PropertyValue
value reference: Performance

Memory Requirement
type: PropertyValue
value reference: Resources

Real-time Processing
type: PropertyValue
value reference: Capability

Ecosystem Age
type: PropertyValue
value reference: Maturity

Enterprise Adoption
type: PropertyValue
value reference: Market

Machine Learning Capability
type: PropertyValue
value reference: Features

Data Storage Redundancy
type: PropertyValue
value reference: Reliability

Event Latency (Processing End-to-End)
type: PropertyValue
value reference: Performance

Throughput Capacity
type: PropertyValue
value reference: Performance

Developer Community Size
type: PropertyValue
value reference: Community

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

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