Apache Spark vs DuckDB
type: WebPage
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.
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Apache Spark vs DuckDB
type: Article
type: TechArticle
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.
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
main entity of page: Apache Spark vs DuckDB
content reference time: 2026-07-09T06:14:24.389Z
author: A Versus B
publisher: A Versus B
date published: 2026-07-09T06:14:24.304Z
date created: 2026-07-09T06:14:24.304Z
date modified: 2026-07-09T06:14:24.389Z
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.
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  object: Apache Spark
  object: DuckDB
about: Apache Spark
about: DuckDB
mentions: Processing Architecture
mentions: Ideal Data Size
mentions: Query Latency (100GB dataset)
mentions: Setup Complexity
mentions: Memory Efficiency on 10GB Query
mentions: Supported Languages
mentions: Enterprise Ecosystem
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
citation: Apache Spark — Wikipedia
citation: DuckDB — Wikipedia
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significant link: https://www.aversusb.net/entity/duckdb
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A Versus B
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Apache Spark
type: SoftwareApplication
description: In-memory distributed computing framework for fast batch, streaming, and interactive data processing.
alternate name: AS
language: en-US
image: ImageObject
  caption: Apache Spark
subject of: Apache Spark vs DuckDB

DuckDB
type: SoftwareApplication
description: In-process SQL analytical database engine optimized for fast queries on structured data without infrastructure.
alternate name: Duckdb
language: en-US
image: ImageObject
  caption: DuckDB
subject of: Apache Spark vs DuckDB

Apache Spark vs DuckDB — Comparison Data
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Apache Spark vs DuckDB — Attribute Category Vocabulary
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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

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

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
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value reference: Performance

Maximum Practical Data Size
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value reference: Scalability

Memory Usage for 10GB Query
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value reference: Efficiency

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

Number of Supported Languages
type: PropertyValue
value reference: Compatibility

Community GitHub Stars (2026)
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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)
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value reference: Adoption

Setup Time (basic cluster)
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value reference: Deployment

Active Development (2024-2026)
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Approximate Learning Time for Developers
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value reference: Developer Experience

First Release Year
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value reference: History

Maximum Cluster Size
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Query Latency (1GB aggregation)
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value reference: Performance

Compression Ratio (typical)
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Memory Required (minimal)
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Ingest Throughput
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SQL Standard Compliance
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Aggregation Query Time (1 billion rows)
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Memory Usage (1TB analytical dataset)
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ACID Compliance Level
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Concurrent Write Support
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value reference: Concurrency

Years in Production
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Native Format Support
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value reference: Features

Database File Size Limit
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Production Deployments (Estimated)
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Query Latency (100M rows, simple aggregation)
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Idle Memory Usage
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Data Compression Ratio
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SQL Standard Coverage
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ACID Transactions
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Core Language
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Language Bindings Supported
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value reference: Ecosystem

Latest Stable Version
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Total Cost of Ownership (Annual, 100TB dataset)
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Query Latency (1TB dataset, complex join)
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Built-in Machine Learning Capabilities
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GitHub Stars (Community Traction)
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Query Latency on 1GB Dataset
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Minimum Cluster Nodes Required
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Memory Usage (10GB dataset analysis)
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Number of Built-in Data Transformation Methods
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Stack Overflow Questions (as of 2026)
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SQL Window Function Support
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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