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DuckDB vs Apache Spark 2026

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.

DuckDB

DuckDB

Embedded in-process OLAP database optimized for analytical queries on local datasets

Data analysts, data scientists, and teams doing interactive queries on datasets under 1TB who need fast results without infrastructure overhead

Score71%
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VS
Apache Spark

Apache Spark

Distributed computing framework for large-scale data processing and machine learning

Enterprise data teams processing massive datasets, building ML pipelines, streaming data applications, and organizations with existing distributed infrastructure

Score63%
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Quick Answer

AI Summary

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.

Our Verdict

AI-assisted

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.

Community feedback

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DuckDB
8.3/10
Apache Spark
6.7/10
DuckDB

Choose DuckDB if

Best pick

Data analysts, data scientists, and teams doing interactive queries on datasets under 1TB who need fast results without infrastructure overhead

Apache Spark

Choose Apache Spark if

Enterprise data teams processing massive datasets, building ML pipelines, streaming data applications, and organizations with existing distributed infrastructure

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Key Differences at a Glance

  • Architecture:Single-machine in-process database vs Distributed cluster computing framework
  • Query Latency (typical OLAP query):✓ DuckDB wins(50-500ms vs 5-30 seconds)
  • Maximum Data Scale:✓ Apache Spark wins(Multiple petabytes vs 100GB-1TB (practical limit))
See all 7 differences

Key Facts & Figures

130 numeric metrics compared

MetricDuckDBApache SparkRatio
Maximum Cluster Size(nodes)1 (single machine)——
Query Latency (1GB aggregation)(milliseconds)10-50ms——
Compression Ratio (typical)(ratio)4:1 to 8:1——
Memory Required (minimal)(MB)10-50MB——
Ingest Throughput(million rows/second)10-50 million rows/sec——
SQL Standard Compliance(% compatibility)95% standard SQL——
GitHub Stars (2026)(stars)18,500+35,900 stars
Aggregation Query Time (1 billion rows)(seconds)0.5-2 seconds——
Memory Usage (1TB analytical dataset)(GB)10-50 GB——
Years in Production(years)5 years (since 2019)——
Typical Maximum Dataset Size(GB)~100 GB——
Query Latency (100M rows, simple aggregation)(milliseconds)50-200ms——
Idle Memory Usage(MB)50-100 MB——
Supported Data Formats(formats)12+ formatsParquet, ORC, JSON, CSV, Avro, Delta (via library)—
Typical Query Latency (1GB dataset)(milliseconds)50-200ms2000-5000ms
Maximum Practical Data Size(TB)1,000100,000+ (petabyte scale)
Memory Required Per Query(MB)10-50MB500-2000MB
Setup Time for Basic Analytics(minutes)1-5 minutes30-120 minutes
Query Latency (1GB CSV)(milliseconds)150-500ms8,000-15,000ms
Maximum Scalable Dataset Size(GB)10-501,000+ PB
Setup Time (from scratch)(minutes)2-5 (local install)60-120 (cluster setup)
Aggregation Query Speed (10M rows)(seconds)2.3s——
Memory Usage (1GB dataset)(MB)450MB——
SQL Standard Coverage(% of SQL:2016)95%——
Language Bindings Supported(count)5 (Python, R, Java, Node.js, Go)——
Total Cost of Ownership (Annual, 100TB dataset)(USD)$0——
Setup Time to First Query(minutes)< 1 minute——
Query Latency (10GB dataset, simple aggregate)(seconds)0.3 seconds——
Query Latency (1TB dataset, complex join)(seconds)3-5 seconds——
Maximum Supported Dataset Size(TB)2 TB (local)——
Concurrent User Queries(users)1-5 simultaneous——
GitHub Stars (Community Traction)(thousands)18,500+——
Setup Time (Minutes)(minutes)5-10——
Query Latency on 1GB Dataset(milliseconds)10-50——
Minimum Cluster Nodes Required(nodes)1——
Supported Programming Languages(languages)Python, R, Java, C++, Node.js, GoPython, Scala, Java, R, SQL
Annual Infrastructure Cost (1TB dataset)(USD)0-5,000——
Query Performance on 10GB Parquet File (GROUP BY aggregation)(seconds)1.2 seconds——
Memory Usage (10GB dataset analysis)(GB)2.1 GB (with compression)——
Startup/Import Time(milliseconds)45ms (lightweight binary)——
Number of Built-in Data Transformation Methods(count)65 SQL functions + standard——
Stack Overflow Questions (as of 2026)(thousands)8.2K questions——
Maximum Dataset Size (without disk streaming)(GB)1000+ GB (out-of-core)——
Time to Analyze 100MB CSV (end-to-end)(seconds)3.8 seconds——
Base Monthly Cost(USD)Free——
Global Edge Locations(count)None (local only)——
OLAP Query Speed (1GB dataset)(milliseconds)50-100ms——
Supported Languages(count)7 (Python, Node.js, Go, Rust, R, Java, C++)5 (Scala, Python, Java, R, SQL)
Setup Time (fresh install to first query)(minutes)2 minutes60-120 minutes
Minimum Memory Requirement(GB)0.1GB2GB (per executor)
Single Query Latency (10GB dataset)(seconds)0.3 seconds8 seconds
Maximum Practical Data Scale(TB)1TB1000TB+ (petabytes)
Supported SQL Features(percentage of ANSI SQL)95%85%
Community Contributors (2025)(active developers)280+ contributors1500+ contributors
GitHub Stars(stars)22000 stars40000 stars
Time to Learn (for SQL users)(hours)4 hours40 hours
Ingestion Rate (events/second)(events/sec)50,000——
Query Latency (1B rows)(seconds)0.5-2——
Maximum Recommended Dataset Size(rows)1——
Deployment Time(seconds)0.08——
Minimum Cluster Size(nodes)1——
Memory Per Node(GB per 1M events/sec)2-64 (varies)8-12GB (caching overhead)
Query Speed (1GB CSV aggregation)(seconds)1.2 seconds——
Maximum Practical Dataset Size(petabytes)100+ GB——
Memory Usage (1GB CSV load)(MB)200 MB (compressed)——
Built-in Statistical Functions(count)200+——
Learning Curve (1-10 scale)(difficulty)7 (requires SQL)——
Stack Overflow Questions Answered(count)3,200——
First Release Year20192014
Query Latency on 100GB Dataset(seconds)0.1-0.5 seconds5-30 seconds
Memory Usage for 10GB Query(GB)0.5-1GB2-4GB
Time to First Query (fresh install)(minutes)2-5 (download and run)45-120 (including cluster setup)
Number of Supported Languages(languages)5 (SQL, Python, R, C++, Go)6 (Scala, Python, SQL, R, Java, Go)
Community GitHub Stars (2026)(stars)21,800+39,500+
Query Processing Throughput (GBps)(GB/s)10-100 (vectorized)1-10 (cluster dependent)
Maximum Dataset Size(TB)~1 TB (single machine)——
Query Latency (1B rows, COUNT aggregation)(milliseconds)80-150ms——
Data Compression Ratio(x)4-8x compression——
Community Size (GitHub Stars)(stars)20,000+ stars——
Initial Licensing Cost(USD)$0$0
Setup Time to Production(minutes)6-12 weeks6-12 weeks
SQL Query Performance (TPC-DS Benchmark)(seconds)45-120 seconds45-120 seconds
Users Per Collaborative Project(concurrent users)1-5 (via Jupyter sharing)1-5 (via Jupyter sharing)
Typical Cluster Cost (Monthly)(USD)$1,500-$5,000+$1,500-$5,000+
Data Processing Speed (1TB dataset)(minutes)5-15 minutes5-15 minutes
Setup Time for Production Deployment(hours)40-80 hours40-80 hours
Supported Warehouse Platforms(platforms)Hadoop, Kubernetes, cloud object storage (3+ classes)Hadoop, Kubernetes, cloud object storage (3+ classes)
Built-in Data Testing Features(count)0 (requires external frameworks)0 (requires external frameworks)
Minimum Dataset Size for Optimal Use(GB)100+ GB100+ GB
GitHub Community (Stars)(thousands)38.5K stars38.5K stars
Query Performance on 1TB Dataset(seconds)30-120 seconds30-120 seconds
Cluster Setup Time(hours)40-80 hours40-80 hours
Cost per Core-Hour(USD)$0.035-0.15$0.035-0.15
Supported Languages/APIs(count)Python, Scala, Java, SQL, RPython, Scala, Java, SQL, R
Cloud Provider Support(providers)4+ (AWS, Azure, GCP, on-prem)4+ (AWS, Azure, GCP, on-prem)
Machine Learning Algorithms Available(count)50+ (MLlib + custom models)50+ (MLlib + custom models)
Data Format Support8+ formats (Parquet, ORC, Avro, Delta, Iceberg, HDF5, CSV, JSON)8+ formats (Parquet, ORC, Avro, Delta, Iceberg, HDF5, CSV, JSON)
Processing Speed (Same 1TB dataset)(seconds)30-60 seconds (in-memory)30-60 seconds (in-memory)
Processing Speed (Average Query)(seconds)10-60 seconds10-60 seconds
Memory Requirement (Per Node)(GB)16-256 GB16-256 GB
Real-time Streaming Capability(latency (ms))500-5000 ms micro-batches500-5000 ms micro-batches
Market Adoption by Fortune 500(percent)82%82%
Typical Cluster Cost (100-node setup)(USD annual)$450,000-650,000$450,000-650,000
End-to-End Latency (p99)(milliseconds)500-2000ms500-2000ms
Native Connectors Available(count)200+ via ecosystem integrations200+ via ecosystem integrations
Memory Overhead (per task)(MB)~400-800MB (GC overhead)~400-800MB (GC overhead)
Throughput (events/sec per node)(events/sec)~500K-1M events/sec~500K-1M events/sec
Processing Speed (Iterative Query)(seconds)0.5-2 seconds0.5-2 seconds
Memory Requirement(GB)8-64 GB per node8-64 GB per node
Real-time Processing(latency (milliseconds))100-500 ms (micro-batch)100-500 ms (micro-batch)
Ecosystem Age(years)12 years (since 2013)12 years (since 2013)
Enterprise Adoption(companies)74% currently use74% currently use
Event Latency (Processing End-to-End)(milliseconds)100-2,000ms (Spark Streaming micro-batch interval)100-2,000ms (Spark Streaming micro-batch interval)
Throughput Capacity(events/second/node)500,000 - 2,000,000 (batch-optimized)500,000 - 2,000,000 (batch-optimized)
Available Libraries & Integrations(count)14,000+ (Spark packages, MLlib, SQL, GraphX, etc.)14,000+ (Spark packages, MLlib, SQL, GraphX, etc.)
Mean Time to Deploy Production Job(weeks)2-4 weeks (larger talent pool, more examples)2-4 weeks (larger talent pool, more examples)
Stateful Window Operations Complexity(lines of code for session windows)80-150 lines (custom state handling needed)80-150 lines (custom state handling needed)
Minimum Achievable Latency (P99)(milliseconds)500-2000ms500-2000ms
GitHub Stars (Popularity Indicator)(stars)32,00032,000
Market Adoption Rate(percentage of streaming workloads)60-65%60-65%
Memory Overhead per Task(megabytes (baseline))512-1024MB512-1024MB
ANSI SQL Compliance(percentage)98%98%
State Management Capabilities(feature count)2 types (RDD state, DataFrame state)2 types (RDD state, DataFrame state)
Production Deployments (2026)(thousands of deployments)45,000-55,00045,000-55,000
Year-over-Year Growth Rate(percentage)8%8%
Typical Processing Speed (iterative queries)(seconds)2-5 seconds2-5 seconds
Memory Requirements per Node(GB)16-64 GB (in-memory caching)16-64 GB (in-memory caching)
Production Deployments (estimated 2024)(thousands)450,000+ globally450,000+ globally
Setup Time (basic cluster)(hours)1-2 hours1-2 hours
Approximate Learning Time for Developers(weeks)2-4 weeks2-4 weeks

Sourced from publicly available data ·

Key Differences

7 attributes compared head-to-head

DuckDB
4DuckDB
DuckDB leads1 tie
Apache Spark
2Apache Spark
  • Architecture

    DuckDB

    Single-machine in-process database

    Apache Spark

    Distributed cluster computing framework

  • Query Latency (typical OLAP query)

    DuckDB

    50-500ms(winner)

    Apache Spark

    5-30 seconds

  • Maximum Data Scale

    DuckDB

    100GB-1TB (practical limit)

    Apache Spark

    Multiple petabytes(winner)

  • Setup Complexity

    DuckDB

    Zero configuration, pip install duckdb(winner)

    Apache Spark

    Requires cluster setup, YARN/Kubernetes

  • Memory Usage (typical)

    DuckDB

    100MB-2GB for operations(winner)

    Apache Spark

    2GB+ per executor minimum

  • Supported Data Formats

    DuckDB

    Parquet, CSV, JSON, Arrow, SQLite

    Apache Spark

    Parquet, ORC, CSV, Avro, Delta Lake, Iceberg(winner)

  • Learning Curve

    DuckDB

    Minimal - standard SQL, Python/R integration(winner)

    Apache Spark

    Steep - requires understanding of RDDs, DAGs, cluster concepts

Full Comparison

DuckDB
Apache Spark
Maximum Cluster Size(nodes)
1 (single machine)
—
Database File Size Limit(TB)
Unlimited
—
Typical Maximum Dataset Size(GB)
~100 GB
—
Maximum Practical Data Size(TB)
1,000
100,000+ (petabyte scale)
Maximum Scalable Dataset Size(GB)
10-50
1,000+ PB
Show 6 more attributes
Maximum Supported Dataset Size(TB)
2 TB (local)
—
Concurrent User Queries(users)
1-5 simultaneous
—
Maximum Dataset Size (without disk streaming)(GB)
1000+ GB (out-of-core)
—
Maximum Practical Data Scale(TB)
1TB
1000TB+ (petabytes)
Maximum Practical Dataset Size(petabytes)
100+ GB
—
Maximum Dataset Size(TB)
~1 TB (single machine)
—
Query Latency (1GB aggregation)(milliseconds)
10-50ms
—
Ingest Throughput(million rows/second)
10-50 million rows/sec
—
Aggregation Query Time (1 billion rows)(seconds)
0.5-2 seconds
—
Query Latency (100M rows, simple aggregation)(milliseconds)
50-200ms
—
Idle Memory Usage(MB)
50-100 MB
—
Show 34 more attributes
Typical Query Latency (1GB dataset)(milliseconds)
50-200ms
2000-5000ms
Query Latency (1GB CSV)(milliseconds)
150-500ms
8,000-15,000ms
Aggregation Query Speed (10M rows)(seconds)
2.3s
—
Query Latency (10GB dataset, simple aggregate)(seconds)
0.3 seconds
—
Query Latency (1TB dataset, complex join)(seconds)
3-5 seconds
—
Query Latency on 1GB Dataset(milliseconds)
10-50
—
Concurrent Queries Supported(queries)
Limited by single machine
—
Query Performance on 10GB Parquet File (GROUP BY aggregation)(seconds)
1.2 seconds
—
Startup/Import Time(milliseconds)
45ms (lightweight binary)
—
OLAP Query Speed (1GB dataset)(milliseconds)
50-100ms
—
Replication Latency(milliseconds)
Not supported
—
Single Query Latency (10GB dataset)(seconds)
0.3 seconds
8 seconds
Ingestion Rate (events/second)(events/sec)
50,000
—
Query Latency (1B rows)(seconds)
0.5-2
—
Maximum Recommended Dataset Size(rows)
1
—
Deployment Time(seconds)
0.08
—
Query Speed (1GB CSV aggregation)(seconds)
1.2 seconds
—
Query Latency on 100GB Dataset(seconds)
0.1-0.5 seconds
5-30 seconds
Query Processing Throughput (GBps)(GB/s)
10-100 (vectorized)
1-10 (cluster dependent)
Query Latency (1B rows, COUNT aggregation)(milliseconds)
80-150ms
—
SQL Query Performance (TPC-DS Benchmark)(seconds)
45-120 seconds
—
Data Processing Speed (1TB dataset)(minutes)
5-15 minutes
—
Query Performance on 1TB Dataset(seconds)
30-120 seconds
—
Maximum Dataset Size Supported(GB)
Unlimited (depends on storage)
—
Processing Speed (Same 1TB dataset)(seconds)
30-60 seconds (in-memory)
—
Processing Speed (Average Query)(seconds)
10-60 seconds
—
End-to-End Latency (p99)(milliseconds)
500-2000ms
—
Memory Overhead (per task)(MB)
~400-800MB (GC overhead)
—
Throughput (events/sec per node)(events/sec)
~500K-1M events/sec
—
Processing Speed (Iterative Query)(seconds)
0.5-2 seconds
—
Event Latency (Processing End-to-End)(milliseconds)
100-2,000ms (Spark Streaming micro-batch interval)
—
Throughput Capacity(events/second/node)
500,000 - 2,000,000 (batch-optimized)
—
Minimum Achievable Latency (P99)(milliseconds)
500-2000ms
—
Typical Processing Speed (iterative queries)(seconds)
2-5 seconds
—
Compression Ratio (typical)(ratio)
4:1 to 8:1
—
Memory Usage (1GB CSV load)(MB)
200 MB (compressed)
—
Memory Usage for 10GB Query(GB)
0.5-1GB
2-4GB
Memory Required (minimal)(MB)
10-50MB
—
SQL Standard Compliance(% compatibility)
95% standard SQL
—
Supported Data Formats(formats)
12+ formats
Parquet, ORC, JSON, CSV, Avro, Delta (via library)
Primary Language Support
Python, SQL, C++, R, Julia, Node.js
Python, Scala, SQL, R, Java
Number of Supported Languages(languages)
5 (SQL, Python, R, C++, Go)
6 (Scala, Python, SQL, R, Java, Go)
Supported Warehouse Platforms(platforms)
Hadoop, Kubernetes, cloud object storage (3+ classes)
—
Show 1 more attribute
Supported Languages/APIs(count)
Python, Scala, Java, SQL, R
—
GitHub Stars (2026)(stars)
18,500+
35,900 stars
GitHub Stars (Community Traction)(thousands)
18,500+
—
Community Contributors (2025)(active developers)
280+ contributors
1500+ contributors
GitHub Stars(stars)
22000 stars
40000 stars
Stack Overflow Questions Answered(count)
3,200
—
Show 6 more attributes
Community GitHub Stars (2026)(stars)
21,800+
39,500+
Community Size (GitHub Stars)(stars)
20,000+ stars
—
Community Size(members)
25,000+ questions
—
GitHub Community (Stars)(thousands)
38.5K stars
—
Developer Community Size(millions)
7,200,000 (StackOverflow, job postings 2024)
—
GitHub Stars (Popularity Indicator)(stars)
32,000
—
Memory Usage (1TB analytical dataset)(GB)
10-50 GB
—
Memory Required Per Query(MB)
10-50MB
500-2000MB
Memory Usage (1GB dataset)(MB)
450MB
—
Memory Usage (10GB dataset analysis)(GB)
2.1 GB (with compression)
—
Memory Per Node(GB per 1M events/sec)
2-64 (varies)
8-12GB (caching overhead)
Show 1 more attribute
Memory Overhead per Task(megabytes (baseline))
512-1024MB
—
ACID Compliance Level
Partial (batch insert-optimized)
—
Fault Tolerance(capability)
No (single machine)
Yes (RDD lineage-based)
Fault Tolerance Method(mechanism)
Lineage-based recovery (RDD parents)
—
Data Storage Redundancy(replication factor)
Depends on underlying storage
—
Concurrent Write Support
Single-threaded writes only
—
Years in Production(years)
5 years (since 2019)
—
First Release Year
2019
2014
Ecosystem Age(years)
12 years (since 2013)
—
Native Format Support
Parquet, CSV, JSON, Iceberg, Hugging Face
—
Built-in Machine Learning Capabilities
No (requires external integration)
—
Real-time Streaming Ingestion
Batch-focused only
—
Supported SQL Features(percentage of ANSI SQL)
95%
85%
Real-time Upsert Support(boolean)
No (batch only)
—
Show 8 more attributes
Built-in Statistical Functions(count)
200+
—
Built-in Data Testing Features(count)
0 (requires external frameworks)
—
Cloud Provider Support(providers)
4+ (AWS, Azure, GCP, on-prem)
—
Data Format Support
8+ formats (Parquet, ORC, Avro, Delta, Iceberg, HDF5, CSV, JSON)
—
Real-time Streaming Capability(latency (ms))
500-5000 ms micro-batches
—
Supported Event Time Semantics
Partial in Structured Streaming, limited out-of-order support
—
Batch+Stream Unified Code
Unified via Structured Streaming/Dataset API
—
Machine Learning Capability(native support)
Full MLlib with algorithms, pipelines
—
Production Deployments (Estimated)(count)
Growing (100K+)
—
Market Adoption Rate(percentage of streaming workloads)
60-65%
—
Production Deployments (2026)(thousands of deployments)
45,000-55,000
—
Production Deployments (estimated 2024)(thousands)
450,000+ globally
—
Setup Time for Basic Analytics(minutes)
1-5 minutes
30-120 minutes
Setup Time (from scratch)(minutes)
2-5 (local install)
60-120 (cluster setup)
Setup Time (Minutes)(minutes)
5-10
—
Setup Time for Production Deployment(hours)
40-80 hours
—
Multi-machine Distributed Computing(capability)
Not supported
Native support
Multi-node Support(boolean)
No (single-node only)
—
SQL Standard Coverage(% of SQL:2016)
95%
—
ACID Transactions
Fully supported
—
Core Language
C++ (Rust bindings available)
—
Cluster Setup Time(hours)
40-80 hours
—
Language Bindings Supported(count)
5 (Python, R, Java, Node.js, Go)
—
Native Connectors Available(count)
200+ via ecosystem integrations
—
Available Libraries & Integrations(count)
14,000+ (Spark packages, MLlib, SQL, GraphX, etc.)
—
Latest Stable Version
v0.10.0 (2024)
—
Total Cost of Ownership (Annual, 100TB dataset)(USD)
$0
—
Annual Infrastructure Cost (1TB dataset)(USD)
0-5,000
—
Initial Licensing Cost(USD)
$0
—
Typical Cluster Cost (Monthly)(USD)
$1,500-$5,000+
—
Typical Cluster Cost (100-node setup)(USD annual)
$450,000-650,000
—
Setup Time to First Query(minutes)
< 1 minute
—
Learning Curve (1-10 scale)(difficulty)
7 (requires SQL)
—
Minimum Cluster Nodes Required(nodes)
1
—
Global Edge Locations(count)
None (local only)
—
Minimum Cluster Size(nodes)
1
—
Minimum Hardware Requirements(GB RAM)
512MB standalone
8GB (per node, 3+ nodes recommended)
Memory Requirement (Per Node)(GB)
16-256 GB
—
Supported Programming Languages(languages)
Python, R, Java, C++, Node.js, Go
Python, Scala, Java, R, SQL
Stateful Window Operations Complexity(lines of code for session windows)
80-150 lines (custom state handling needed)
—
State Management Capabilities(feature count)
2 types (RDD state, DataFrame state)
—
Number of Built-in Data Transformation Methods(count)
65 SQL functions + standard
—
Stack Overflow Questions (as of 2026)(thousands)
8.2K questions
—
SQL Window Function Support(yes/no)
Yes (ROW_NUMBER, LAG, LEAD, RANK, etc.)
—
Time to Analyze 100MB CSV (end-to-end)(seconds)
3.8 seconds
—
Base Monthly Cost(USD)
Free
—
Free Tier Storage(GB)
Unlimited (disk-dependent)
—
Cost per Core-Hour(USD)
$0.035-0.15
—
Free Tier Row Reads/Month(millions)
Unlimited
—
Supported Languages(count)
7 (Python, Node.js, Go, Rust, R, Java, C++)
5 (Scala, Python, Java, R, SQL)
Installation Required
No (embedded library)
—
Setup Time (fresh install to first query)(minutes)
2 minutes
60-120 minutes
Minimum Memory Requirement(GB)
0.1GB
2GB (per executor)
Memory Requirement(GB)
8-64 GB per node
—
Time to Learn (for SQL users)(hours)
4 hours
40 hours
SQL Support Level
Full ANSI SQL + extensions
—
Time to First Query (fresh install)(minutes)
2-5 (download and run)
45-120 (including cluster setup)
Setup Time(minutes)
< 1 minute
—
Operational Complexity(1-10 scale)
2 (minimal)
—
Cluster Management Required(hours/month)
40-80 hours (dedicated DevOps engineer)
—
Data Compression Ratio(x)
4-8x compression
—
License Restrictions(commercial use)
MIT - No restrictions
—
Setup Time to Production(minutes)
6-12 weeks
—
Setup Time (basic cluster)(hours)
1-2 hours
—
Users Per Collaborative Project(concurrent users)
1-5 (via Jupyter sharing)
—
Built-in Security Features
0 (manual implementation required)
—
Minimum Dataset Size for Optimal Use(GB)
100+ GB
—
Machine Learning Algorithms Available(count)
50+ (MLlib + custom models)
—
First Release(year)
2014
—
Market Adoption by Fortune 500(percent)
82%
—
Enterprise Adoption Rate(%)
65% (Databricks, AWS, Google, Meta deployments)
—
Real-time Processing(latency (milliseconds))
100-500 ms (micro-batch)
—
Enterprise Adoption(companies)
74% currently use
—
Mean Time to Deploy Production Job(weeks)
2-4 weeks (larger talent pool, more examples)
—
ANSI SQL Compliance(percentage)
98%
—
Year-over-Year Growth Rate(percentage)
8%
—
Memory Requirements per Node(GB)
16-64 GB (in-memory caching)
—
Real-time Streaming Support
Native (Spark Streaming, Structured Streaming)
—
Active Development (2024-2026)
Highly active (Apache Foundation priority)
—
Approximate Learning Time for Developers(weeks)
2-4 weeks
—

Pros & Cons

10 pros·5 cons across both

DuckDB
Apache Spark
DuckDB

DuckDB

+5-2

Pros

  • Sub-second query latency on analytical queries
  • Zero setup required - runs in-process with pip install
  • Excellent OLAP performance with columnar execution
  • Direct integration with Pandas, Arrow, and Parquet without data copying
  • Minimal memory footprint (100MB-2GB typical)

Cons

  • Limited to single-machine scaling (multi-node clustering in beta only)
  • Smaller ecosystem compared to Spark with fewer integrations
Apache Spark

Apache Spark

+5-3

Pros

  • Horizontal scaling across hundreds/thousands of nodes for petabyte-scale data
  • Unified API for batch, streaming, SQL, and ML (MLlib) workloads
  • Production-grade fault tolerance and checkpoint recovery
  • Extensive ecosystem (Delta Lake, Spark SQL, GraphX, Structured Streaming)
  • Multi-language support (Python, Scala, SQL, R, Java)

Cons

  • Significant setup and maintenance overhead requiring cluster infrastructure
  • Higher latency (seconds to minutes) due to distributed coordination overhead
  • Steep learning curve requiring understanding of distributed computing concepts

Frequently Asked Questions

5 questions

  1. 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.

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