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
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.
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.
Apache Spark vs DuckDB — Comparison Data
Structured comparison data for Apache Spark vs DuckDB with 158 attributes.
SQL Query Performance (TPC-DS Benchmark)
Typical Processing Speed (iterative queries)
Approximate Learning Time for Developers
Query Latency (100M rows, simple aggregation)
Total Cost of Ownership (Annual, 100TB dataset)
Query Latency (10GB dataset, simple aggregate)
Query Latency (1TB dataset, complex join)
Annual Infrastructure Cost (1TB dataset)
Query Performance on 10GB Parquet File (GROUP BY aggregation)
Number of Built-in Data Transformation Methods
Maximum Dataset Size (without disk streaming)
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.