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.
Apache Spark vs DuckDB — Side-by-side comparison on A Versus B
In-memory distributed computing framework for fast batch, streaming, and interactive data processing.
In-process SQL analytical database engine optimized for fast queries on structured data without infrastructure.
Processing Architecture. Ideal Data Size. Query Latency (100GB dataset). Setup Complexity. Memory Efficiency on 10GB Query. Supported Languages. Enterprise Ecosystem
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.
Apache Spark: 2000-5000ms | DuckDB: 50-200ms
Apache Spark: 500-2000MB | DuckDB: 10-50MB
Apache Spark: 30-120 minutes | DuckDB: 1-5 minutes
Apache Spark: Python, Scala, SQL, R, Java | DuckDB: Python, SQL, C++, R, Julia, Node.js
Apache Spark: 2-4 GB | DuckDB: 0.1-0.5 GB
Apache Spark: 60-120 (cluster setup) | DuckDB: 2-5 (local install)
Apache Spark: 35,900 stars | DuckDB: 18,500+
Apache Spark: Native support | DuckDB: Not supported
Apache Spark: Yes (RDD lineage-based) | DuckDB: No (single machine)
Apache Spark: 40-80 hours (dedicated DevOps engineer) | DuckDB: N/A
SQL Query Performance (TPC-DS Benchmark)
Apache Spark: 45-120 seconds | DuckDB: N/A
Apache Spark: 1-5 (via Jupyter sharing) | DuckDB: N/A
Apache Spark: 0 (manual implementation required) | DuckDB: N/A
Apache Spark: Parquet, ORC, JSON, CSV, Avro, Delta (via library) | DuckDB: 12+ formats
Apache Spark: 25,000+ questions | DuckDB: N/A
Apache Spark: $1,500-$5,000+ | DuckDB: N/A
Apache Spark: 5-15 minutes | DuckDB: N/A
Apache Spark: Python, Scala, Java, R, SQL | DuckDB: Python, R, Java, C++, Node.js, Go
Apache Spark: Hadoop, Kubernetes, cloud object storage (3+ classes) | DuckDB: N/A
Apache Spark: 0 (requires external frameworks) | DuckDB: N/A
Apache Spark: 30-120 seconds | DuckDB: N/A
Apache Spark: Python, Scala, Java, SQL, R | DuckDB: N/A
Apache Spark: Unlimited (depends on storage) | DuckDB: N/A
Apache Spark: 4+ (AWS, Azure, GCP, on-prem) | DuckDB: N/A
Apache Spark: 50+ (MLlib + custom models) | DuckDB: N/A
Apache Spark: 8+ formats (Parquet, ORC, Avro, Delta, Iceberg, HDF5, CSV, JSON) | DuckDB: N/A
Apache Spark: 30-60 seconds (in-memory) | DuckDB: N/A
Apache Spark: 10-60 seconds | DuckDB: N/A
Apache Spark: 500-5000 ms micro-batches | DuckDB: N/A
Apache Spark: Lineage-based recovery (RDD parents) | DuckDB: N/A
Apache Spark: 200+ via ecosystem integrations | DuckDB: N/A
Apache Spark: 40,100 stars | DuckDB: N/A
Apache Spark: 65% (Databricks, AWS, Google, Meta deployments) | DuckDB: N/A
Apache Spark: ~400-800MB (GC overhead) | DuckDB: N/A
Apache Spark: Partial in Structured Streaming, limited out-of-order support | DuckDB: N/A
Apache Spark: ~500K-1M events/sec | DuckDB: N/A
Apache Spark: Unified via Structured Streaming/Dataset API | DuckDB: N/A
Apache Spark: 0.5-2 seconds | DuckDB: N/A
Apache Spark: 8-64 GB per node | DuckDB: N/A
Apache Spark: 5 (Scala, Python, Java, R, SQL) | DuckDB: 7 (Python, Node.js, Go, Rust, R, Java, C++)
Apache Spark: 100-500 ms (micro-batch) | DuckDB: N/A
Apache Spark: 12 years (since 2013) | DuckDB: N/A
Apache Spark: 74% currently use | DuckDB: N/A
Apache Spark: Full MLlib with algorithms, pipelines | DuckDB: N/A
Apache Spark: Depends on underlying storage | DuckDB: N/A
Apache Spark: 100-2,000ms (Spark Streaming micro-batch interval) | DuckDB: N/A
Apache Spark: 500,000 - 2,000,000 (batch-optimized) | DuckDB: N/A
Apache Spark: 8-12GB (caching overhead) | DuckDB: 2-64 (varies)
Apache Spark: 7,200,000 (StackOverflow, job postings 2024) | DuckDB: N/A
Apache Spark: 14,000+ (Spark packages, MLlib, SQL, GraphX, etc.) | DuckDB: N/A
Apache Spark: 2-4 weeks (larger talent pool, more examples) | DuckDB: N/A
Apache Spark: 80-150 lines (custom state handling needed) | DuckDB: N/A
Apache Spark: 2 types (RDD state, DataFrame state) | DuckDB: N/A
Apache Spark: 45,000-55,000 | DuckDB: N/A
Apache Spark: 8GB (per node, 3+ nodes recommended) | DuckDB: 512MB standalone
Apache Spark: 5-30 seconds | DuckDB: 0.1-0.5 seconds
Apache Spark: 100,000+ (petabyte scale) | DuckDB: 1,000
Apache Spark: 45-120 (including cluster setup) | DuckDB: 2-5 (download and run)
Apache Spark: 6 (Scala, Python, SQL, R, Java, Go) | DuckDB: 5 (SQL, Python, R, C++, Go)
Apache Spark: 1-10 (cluster dependent) | DuckDB: 10-100 (vectorized)
Typical Processing Speed (iterative queries)
Apache Spark: 16-64 GB (in-memory caching) | DuckDB: N/A
Apache Spark: Native (Spark Streaming, Structured Streaming) | DuckDB: N/A
Apache Spark: 450,000+ globally | DuckDB: N/A
Apache Spark: Highly active (Apache Foundation priority) | DuckDB: N/A
Approximate Learning Time for Developers
Apache Spark: N/A | DuckDB: 1 (single machine)
Apache Spark: N/A | DuckDB: 10-50 million rows/sec
Apache Spark: N/A | DuckDB: 95% ANSI SQL
Apache Spark: N/A | DuckDB: 0.5-2 seconds
Apache Spark: N/A | DuckDB: Partial (batch insert-optimized)
Apache Spark: N/A | DuckDB: Single-threaded writes only
Apache Spark: N/A | DuckDB: 5 years (since 2019)
Apache Spark: N/A | DuckDB: Parquet, CSV, JSON, Iceberg, Hugging Face
Apache Spark: N/A | DuckDB: Growing (100K+)
Query Latency (100M rows, simple aggregation)
Apache Spark: N/A | DuckDB: Fully supported
Apache Spark: N/A | DuckDB: C++ (Rust bindings available)
Apache Spark: N/A | DuckDB: 5 (Python, R, Java, Node.js, Go)
Apache Spark: N/A | DuckDB: v0.10.0 (2024)
Total Cost of Ownership (Annual, 100TB dataset)
Query Latency (10GB dataset, simple aggregate)
Query Latency (1TB dataset, complex join)
Apache Spark: N/A | DuckDB: 2 TB (local)
Apache Spark: N/A | DuckDB: 1-5 simultaneous
Apache Spark: N/A | DuckDB: No (requires external integration)
Apache Spark: N/A | DuckDB: Batch-focused only
Apache Spark: N/A | DuckDB: Limited by single machine
Annual Infrastructure Cost (1TB dataset)
Query Performance on 10GB Parquet File (GROUP BY aggregation)
Apache Spark: N/A | DuckDB: 2.1 GB (with compression)
Apache Spark: N/A | DuckDB: 45ms (lightweight binary)
Number of Built-in Data Transformation Methods
Apache Spark: N/A | DuckDB: 65 SQL functions + standard
Apache Spark: N/A | DuckDB: 8.2K questions
Maximum Dataset Size (without disk streaming)
Apache Spark: N/A | DuckDB: 1000+ GB (out-of-core)
Apache Spark: N/A | DuckDB: Yes (ROW_NUMBER, LAG, LEAD, RANK, etc.)
Apache Spark: N/A | DuckDB: Unlimited (disk-dependent)
Apache Spark: N/A | DuckDB: None (local only)
Apache Spark: N/A | DuckDB: Not supported
Apache Spark: N/A | DuckDB: No (embedded library)
Apache Spark: N/A | DuckDB: No (single-node only)
Apache Spark: N/A | DuckDB: No (batch only)
Apache Spark: N/A | DuckDB: 200 MB (compressed)
Apache Spark: N/A | DuckDB: Full ANSI SQL + extensions
Apache Spark: N/A | DuckDB: 7 (requires SQL)
How to choose between Apache Spark and DuckDB
Apache Spark vs DuckDB — Structured Comparison Data
Machine-readable comparison dataset for Apache Spark vs DuckDB. Contains 158 attribute comparisons, 5 FAQ pairs.
Apache Spark vs DuckDB — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Apache Spark vs DuckDB.
When should I use DuckDB instead of Spark?
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.
Can DuckDB replace Spark for production analytics?
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.
What's the cost difference between Spark and DuckDB?
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.
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.
Can I migrate from Spark to DuckDB or vice versa?
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.
How to Choose Between Apache Spark and DuckDB
Processing Architecture: Apache Spark has Distributed cluster-based (master-worker), while DuckDB has In-process single-machine or shared-memory.
Ideal Data Size: Apache Spark has 100GB to petabytes, while DuckDB has 1MB to 1TB.
Query Latency (100GB dataset): Apache Spark has 5-30 seconds (cluster overhead), while DuckDB has 100-500ms (in-process).
Setup Complexity: Apache Spark has High (requires cluster, Hadoop/Kubernetes), while DuckDB has Minimal (single binary, no dependencies).
Consider: Memory Efficiency on 10GB Query
Memory Efficiency on 10GB Query: Apache Spark has 2-4GB required (JVM overhead), while DuckDB has 500MB-1GB required.
Supported Languages: Apache Spark has Scala, Python, SQL, R, Java (6 languages), while DuckDB has SQL, Python, R, C++, Go (5 languages).
Enterprise Ecosystem: Apache Spark has Mature (Databricks, AWS EMR, Cloudera), while DuckDB has Growing (DuckLabs, open-source focus).