DuckDB Analytics Expert

Expert guidance for building high-performance analytical queries and data pipelines using DuckDB, the embedded OLAP database engine with columnar storage and vectorized execution.

Gold

v1.0.00 activationsData & AnalyticsTechnologyintermediate

SupaScore

83.85

Research Quality (15%)

8.3

Prompt Engineering (25%)

8.5

Practical Utility (15%)

8.6

Completeness (10%)

8.4

User Satisfaction (20%)

8.2

Decision Usefulness (15%)

8.3

Best for

▸Building analytical queries against Parquet files for exploratory data analysis
▸Optimizing columnar storage schemas for time-series and dimensional analytics
▸Designing embedded analytics pipelines for Python applications with Arrow integration
▸Creating high-performance ETL transforms using vectorized SQL operations
▸Implementing analytical workloads that outgrow pandas but don't need distributed systems

What you'll get

●Optimized DuckDB SQL with COLUMNS expressions, QUALIFY clauses, and predicate pushdown strategies
●Schema design recommendations with appropriate data types and partitioning for analytical workloads
●Python integration patterns showing zero-copy Arrow data exchange and performance benchmarks

Not designed for ↓

×High-concurrency OLTP applications with many simultaneous writers
×Multi-user database serving with complex user authentication and permissions
×Distributed processing of datasets larger than single-machine memory
×Real-time streaming analytics requiring sub-second latency guarantees

Expects

Clear description of data volume, file formats, query patterns, and performance requirements for analytical workloads.

Returns

Optimized DuckDB SQL queries, schema designs, and integration patterns with specific performance tuning recommendations.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

duckdbolapcolumnar-databaseparquetanalytical-sqlembedded-databasedata-pipelinearrowvectorized-executionpython-analyticsdata-engineeringsql-analytics

Research Foundation: 7 sources (3 official docs, 1 academic, 2 books, 1 web)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/16/2026

Initial release

Prerequisites

Use these skills first for best results.

SQL Window Functions ExpertGold

Works well with

Data Pipeline ArchitectGold Python Data AnalystGold SQL Query OptimizerPlatinum

Need more depth?

Specialist skills that go deeper in areas this skill touches.

Apache Spark Data Processing ExpertGold ClickHouse Analytics EngineerGold Data Lakehouse DesignerGold

Common Workflows

Analytical Data Pipeline Design

Design data ingestion architecture, implement high-performance analytical queries, and integrate with Python analytics workflows

Data Pipeline Architect→duckdb-analytics-expert→Python Data Analyst

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