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 vs Apache Spark — Side-by-side comparison on A Versus B
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.
Embedded analytical SQL database optimized for in-process OLAP queries
Distributed computing framework for large-scale data processing and machine learning
DuckDB vs Apache Spark — Comparison Data
Structured comparison data for DuckDB vs Apache Spark with 174 attributes.
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)
Setup Time (fresh install to first query)
Query Latency (1B rows, COUNT aggregation)
SQL Query Performance (TPC-DS Benchmark)
Typical Processing Speed (iterative queries)
Approximate Learning Time for Developers
DuckDB vs Apache Spark — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for DuckDB vs Apache Spark.
When should I use DuckDB instead of Spark?
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.
Can DuckDB handle streaming data like Spark?
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.
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.
What's the cost difference between DuckDB and Spark?
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.
Can I migrate from Spark to DuckDB or vice versa?
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.